/[escript]/trunk/escript/py_src/pdetools.py
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revision 1956 by gross, Mon Nov 3 05:08:42 2008 UTC revision 2251 by gross, Fri Feb 6 06:50:39 2009 UTC
# Line 20  http://www.opensource.org/licenses/osl-3 Line 20  http://www.opensource.org/licenses/osl-3
20  __url__="http://www.uq.edu.au/esscc/escript-finley"  __url__="http://www.uq.edu.au/esscc/escript-finley"
21    
22  """  """
23  Provides some tools related to PDEs.  Provides some tools related to PDEs.
24    
25  Currently includes:  Currently includes:
26      - Projector - to project a discontinuous      - Projector - to project a discontinuous function onto a continuous function
27      - Locator - to trace values in data objects at a certain location      - Locator - to trace values in data objects at a certain location
28      - TimeIntegrationManager - to handel extraplotion in time      - TimeIntegrationManager - to handle extrapolation in time
29      - SaddlePointProblem - solver for Saddle point problems using the inexact uszawa scheme      - SaddlePointProblem - solver for Saddle point problems using the inexact uszawa scheme
30    
31  @var __author__: name of author  @var __author__: name of author
# Line 52  import math Line 52  import math
52    
53  class TimeIntegrationManager:  class TimeIntegrationManager:
54    """    """
55    a simple mechanism to manage time dependend values.    A simple mechanism to manage time dependend values.
56    
57    typical usage is::    Typical usage is::
58    
59       dt=0.1 # time increment       dt=0.1 # time increment
60       tm=TimeIntegrationManager(inital_value,p=1)       tm=TimeIntegrationManager(inital_value,p=1)
# Line 68  class TimeIntegrationManager: Line 68  class TimeIntegrationManager:
68    """    """
69    def __init__(self,*inital_values,**kwargs):    def __init__(self,*inital_values,**kwargs):
70       """       """
71       sets up the value manager where inital_value is the initial value and p is order used for extrapolation       Sets up the value manager where C{inital_values} are the initial values
72         and p is the order used for extrapolation.
73       """       """
74       if kwargs.has_key("p"):       if kwargs.has_key("p"):
75              self.__p=kwargs["p"]              self.__p=kwargs["p"]
# Line 85  class TimeIntegrationManager: Line 86  class TimeIntegrationManager:
86    
87    def getTime(self):    def getTime(self):
88        return self.__t        return self.__t
89    
90    def getValue(self):    def getValue(self):
91        out=self.__v_mem[0]        out=self.__v_mem[0]
92        if len(out)==1:        if len(out)==1:
# Line 94  class TimeIntegrationManager: Line 96  class TimeIntegrationManager:
96    
97    def checkin(self,dt,*values):    def checkin(self,dt,*values):
98        """        """
99        adds new values to the manager. the p+1 last value get lost        Adds new values to the manager. The p+1 last values are lost.
100        """        """
101        o=min(self.__order+1,self.__p)        o=min(self.__order+1,self.__p)
102        self.__order=min(self.__order+1,self.__p)        self.__order=min(self.__order+1,self.__p)
# Line 111  class TimeIntegrationManager: Line 113  class TimeIntegrationManager:
113    
114    def extrapolate(self,dt):    def extrapolate(self,dt):
115        """        """
116        extrapolates to dt forward in time.        Extrapolates to C{dt} forward in time.
117        """        """
118        if self.__order==0:        if self.__order==0:
119           out=self.__v_mem[0]           out=self.__v_mem[0]
# Line 126  class TimeIntegrationManager: Line 128  class TimeIntegrationManager:
128           return out[0]           return out[0]
129        else:        else:
130           return out           return out
131    
132    
133  class Projector:  class Projector:
134    """    """
135    The Projector is a factory which projects a discontiuous function onto a    The Projector is a factory which projects a discontinuous function onto a
136    continuous function on the a given domain.    continuous function on a given domain.
137    """    """
138    def __init__(self, domain, reduce = True, fast=True):    def __init__(self, domain, reduce=True, fast=True):
139      """      """
140      Create a continuous function space projector for a domain.      Creates a continuous function space projector for a domain.
141    
142      @param domain: Domain of the projection.      @param domain: Domain of the projection.
143      @param reduce: Flag to reduce projection order (default is True)      @param reduce: Flag to reduce projection order
144      @param fast: Flag to use a fast method based on matrix lumping (default is true)      @param fast: Flag to use a fast method based on matrix lumping
145      """      """
146      self.__pde = linearPDEs.LinearPDE(domain)      self.__pde = linearPDEs.LinearPDE(domain)
147      if fast:      if fast:
148        self.__pde.setSolverMethod(linearPDEs.LinearPDE.LUMPING)          self.__pde.setSolverMethod(linearPDEs.LinearPDE.LUMPING)
149      self.__pde.setSymmetryOn()      self.__pde.setSymmetryOn()
150      self.__pde.setReducedOrderTo(reduce)      self.__pde.setReducedOrderTo(reduce)
151      self.__pde.setValue(D = 1.)      self.__pde.setValue(D = 1.)
# Line 151  class Projector: Line 153  class Projector:
153    
154    def __call__(self, input_data):    def __call__(self, input_data):
155      """      """
156      Projects input_data onto a continuous function      Projects C{input_data} onto a continuous function.
157    
158      @param input_data: The input_data to be projected.      @param input_data: the data to be projected
159      """      """
160      out=escript.Data(0.,input_data.getShape(),self.__pde.getFunctionSpaceForSolution())      out=escript.Data(0.,input_data.getShape(),self.__pde.getFunctionSpaceForSolution())
161      self.__pde.setValue(Y = escript.Data(), Y_reduced = escript.Data())      self.__pde.setValue(Y = escript.Data(), Y_reduced = escript.Data())
# Line 186  class Projector: Line 188  class Projector:
188    
189  class NoPDE:  class NoPDE:
190       """       """
191       solves the following problem for u:       Solves the following problem for u:
192    
193       M{kronecker[i,j]*D[j]*u[j]=Y[i]}       M{kronecker[i,j]*D[j]*u[j]=Y[i]}
194    
195       with constraint       with constraint
196    
197       M{u[j]=r[j]}  where M{q[j]>0}       M{u[j]=r[j]}  where M{q[j]>0}
198    
199       where D, Y, r and q are given functions of rank 1.       where M{D}, M{Y}, M{r} and M{q} are given functions of rank 1.
200    
201       In the case of scalars this takes the form       In the case of scalars this takes the form
202    
203       M{D*u=Y}       M{D*u=Y}
204    
205       with constraint       with constraint
206    
207       M{u=r}  where M{q>0}       M{u=r} where M{q>0}
208    
209       where D, Y, r and q are given scalar functions.       where M{D}, M{Y}, M{r} and M{q} are given scalar functions.
210    
211       The constraint is overwriting any other condition.       The constraint overwrites any other condition.
212    
213       @note: This class is similar to the L{linearPDEs.LinearPDE} class with A=B=C=X=0 but has the intention       @note: This class is similar to the L{linearPDEs.LinearPDE} class with
214              that all input parameter are given in L{Solution} or L{ReducedSolution}. The whole              A=B=C=X=0 but has the intention that all input parameters are given
215              thing is a bit strange and I blame Robert.Woodcock@csiro.au for this.              in L{Solution} or L{ReducedSolution}.
216       """       """
217         # The whole thing is a bit strange and I blame Rob Woodcock (CSIRO) for
218         # this.
219       def __init__(self,domain,D=None,Y=None,q=None,r=None):       def __init__(self,domain,D=None,Y=None,q=None,r=None):
220           """           """
221           initialize the problem           Initializes the problem.
222    
223           @param domain: domain of the PDE.           @param domain: domain of the PDE
224           @type domain: L{Domain}           @type domain: L{Domain}
225           @param D: coefficient of the solution.           @param D: coefficient of the solution
226           @type D: C{float}, C{int}, L{numarray.NumArray}, L{Data}           @type D: C{float}, C{int}, C{numarray.NumArray}, L{Data}
227           @param Y: right hand side           @param Y: right hand side
228           @type Y: C{float}, C{int}, L{numarray.NumArray}, L{Data}           @type Y: C{float}, C{int}, C{numarray.NumArray}, L{Data}
229           @param q: location of constraints           @param q: location of constraints
230           @type q: C{float}, C{int}, L{numarray.NumArray}, L{Data}           @type q: C{float}, C{int}, C{numarray.NumArray}, L{Data}
231           @param r: value of solution at locations of constraints           @param r: value of solution at locations of constraints
232           @type r: C{float}, C{int}, L{numarray.NumArray}, L{Data}           @type r: C{float}, C{int}, C{numarray.NumArray}, L{Data}
233           """           """
234           self.__domain=domain           self.__domain=domain
235           self.__D=D           self.__D=D
# Line 234  class NoPDE: Line 238  class NoPDE:
238           self.__r=r           self.__r=r
239           self.__u=None           self.__u=None
240           self.__function_space=escript.Solution(self.__domain)           self.__function_space=escript.Solution(self.__domain)
241    
242       def setReducedOn(self):       def setReducedOn(self):
243           """           """
244           sets the L{FunctionSpace} of the solution to L{ReducedSolution}           Sets the L{FunctionSpace} of the solution to L{ReducedSolution}.
245           """           """
246           self.__function_space=escript.ReducedSolution(self.__domain)           self.__function_space=escript.ReducedSolution(self.__domain)
247           self.__u=None           self.__u=None
248    
249       def setReducedOff(self):       def setReducedOff(self):
250           """           """
251           sets the L{FunctionSpace} of the solution to L{Solution}           Sets the L{FunctionSpace} of the solution to L{Solution}.
252           """           """
253           self.__function_space=escript.Solution(self.__domain)           self.__function_space=escript.Solution(self.__domain)
254           self.__u=None           self.__u=None
255            
256       def setValue(self,D=None,Y=None,q=None,r=None):       def setValue(self,D=None,Y=None,q=None,r=None):
257           """           """
258           assigns values to the parameters.           Assigns values to the parameters.
259    
260           @param D: coefficient of the solution.           @param D: coefficient of the solution
261           @type D: C{float}, C{int}, L{numarray.NumArray}, L{Data}           @type D: C{float}, C{int}, C{numarray.NumArray}, L{Data}
262           @param Y: right hand side           @param Y: right hand side
263           @type Y: C{float}, C{int}, L{numarray.NumArray}, L{Data}           @type Y: C{float}, C{int}, C{numarray.NumArray}, L{Data}
264           @param q: location of constraints           @param q: location of constraints
265           @type q: C{float}, C{int}, L{numarray.NumArray}, L{Data}           @type q: C{float}, C{int}, C{numarray.NumArray}, L{Data}
266           @param r: value of solution at locations of constraints           @param r: value of solution at locations of constraints
267           @type r: C{float}, C{int}, L{numarray.NumArray}, L{Data}           @type r: C{float}, C{int}, C{numarray.NumArray}, L{Data}
268           """           """
269           if not D==None:           if not D==None:
270              self.__D=D              self.__D=D
# Line 276  class NoPDE: Line 281  class NoPDE:
281    
282       def getSolution(self):       def getSolution(self):
283           """           """
284           returns the solution           Returns the solution.
285            
286           @return: the solution of the problem           @return: the solution of the problem
287           @rtype: L{Data} object in the L{FunctionSpace} L{Solution} or L{ReducedSolution}.           @rtype: L{Data} object in the L{FunctionSpace} L{Solution} or
288                     L{ReducedSolution}
289           """           """
290           if self.__u==None:           if self.__u==None:
291              if self.__D==None:              if self.__D==None:
# Line 296  class NoPDE: Line 302  class NoPDE:
302                  self.__u*=(1.-q)                  self.__u*=(1.-q)
303                  if not self.__r==None: self.__u+=q*self.__r                  if not self.__r==None: self.__u+=q*self.__r
304           return self.__u           return self.__u
305                
306  class Locator:  class Locator:
307       """       """
308       Locator provides access to the values of data objects at a given       Locator provides access to the values of data objects at a given spatial
309       spatial coordinate x.         coordinate x.
310        
311       In fact, a Locator object finds the sample in the set of samples of a       In fact, a Locator object finds the sample in the set of samples of a
312       given function space or domain where which is closest to the given       given function space or domain which is closest to the given point x.
      point x.  
313       """       """
314    
315       def __init__(self,where,x=numarray.zeros((3,))):       def __init__(self,where,x=numarray.zeros((3,))):
316         """         """
317         Initializes a Locator to access values in Data objects on the Doamin         Initializes a Locator to access values in Data objects on the Doamin
318         or FunctionSpace where for the sample point which         or FunctionSpace for the sample point which is closest to the given
319         closest to the given point x.         point x.
320    
321         @param where: function space         @param where: function space
322         @type where: L{escript.FunctionSpace}         @type where: L{escript.FunctionSpace}
323         @param x: coefficient of the solution.         @param x: coefficient of the solution
324         @type x: L{numarray.NumArray} or C{list} of L{numarray.NumArray}         @type x: C{numarray.NumArray} or C{list} of C{numarray.NumArray}
325         """         """
326         if isinstance(where,escript.FunctionSpace):         if isinstance(where,escript.FunctionSpace):
327            self.__function_space=where            self.__function_space=where
# Line 350  class Locator: Line 355  class Locator:
355    
356       def getX(self):       def getX(self):
357          """          """
358      Returns the exact coordinates of the Locator.          Returns the exact coordinates of the Locator.
359      """          """
360          return self(self.getFunctionSpace().getX())          return self(self.getFunctionSpace().getX())
361    
362       def getFunctionSpace(self):       def getFunctionSpace(self):
363          """          """
364      Returns the function space of the Locator.          Returns the function space of the Locator.
365      """          """
366          return self.__function_space          return self.__function_space
367    
368       def getId(self,item=None):       def getId(self,item=None):
369          """          """
370      Returns the identifier of the location.          Returns the identifier of the location.
371      """          """
372          if item == None:          if item == None:
373             return self.__id             return self.__id
374          else:          else:
# Line 375  class Locator: Line 380  class Locator:
380    
381       def __call__(self,data):       def __call__(self,data):
382          """          """
383      Returns the value of data at the Locator of a Data object otherwise          Returns the value of data at the Locator of a Data object.
384      the object is returned.          """
     """  
385          return self.getValue(data)          return self.getValue(data)
386    
387       def getValue(self,data):       def getValue(self,data):
388          """          """
389      Returns the value of data at the Locator if data is a Data object          Returns the value of C{data} at the Locator if C{data} is a L{Data}
390      otherwise the object is returned.          object otherwise the object is returned.
391      """          """
392          if isinstance(data,escript.Data):          if isinstance(data,escript.Data):
393             if data.getFunctionSpace()==self.getFunctionSpace():             if data.getFunctionSpace()==self.getFunctionSpace():
394               dat=data               dat=data
# Line 412  class Locator: Line 416  class Locator:
416    
417  class SolverSchemeException(Exception):  class SolverSchemeException(Exception):
418     """     """
419     exceptions thrown by solvers     This is a generic exception thrown by solvers.
420     """     """
421     pass     pass
422    
423  class IndefinitePreconditioner(SolverSchemeException):  class IndefinitePreconditioner(SolverSchemeException):
424     """     """
425     the preconditioner is not positive definite.     Exception thrown if the preconditioner is not positive definite.
426     """     """
427     pass     pass
428    
429  class MaxIterReached(SolverSchemeException):  class MaxIterReached(SolverSchemeException):
430     """     """
431     maxium number of iteration steps is reached.     Exception thrown if the maximum number of iteration steps is reached.
432     """     """
433     pass     pass
434  class IterationBreakDown(SolverSchemeException):  
435    class CorrectionFailed(SolverSchemeException):
436     """     """
437     iteration scheme econouters an incurable breakdown.     Exception thrown if no convergence has been achieved in the solution
438       correction scheme.
439     """     """
440     pass     pass
441  class NegativeNorm(SolverSchemeException):  
442    class IterationBreakDown(SolverSchemeException):
443     """     """
444     a norm calculation returns a negative norm.     Exception thrown if the iteration scheme encountered an incurable breakdown.
445     """     """
446     pass     pass
447    
448  class IterationHistory(object):  class NegativeNorm(SolverSchemeException):
449     """     """
450     The IterationHistory class is used to define a stopping criterium. It keeps track of the     Exception thrown if a norm calculation returns a negative norm.
    residual norms. The stoppingcriterium indicates termination if the residual norm has been reduced by  
    a given tolerance.  
451     """     """
452     def __init__(self,tolerance=math.sqrt(util.EPSILON),verbose=False):     pass
       """  
       Initialization  
   
       @param tolerance: tolerance  
       @type tolerance: positive C{float}  
       @param verbose: switches on the printing out some information  
       @type verbose: C{bool}  
       """  
       if not tolerance>0.:  
           raise ValueError,"tolerance needs to be positive."  
       self.tolerance=tolerance  
       self.verbose=verbose  
       self.history=[]  
    def stoppingcriterium(self,norm_r,r,x):  
        """  
        returns True if the C{norm_r} is C{tolerance}*C{norm_r[0]} where C{norm_r[0]}  is the residual norm at the first call.  
   
         
        @param norm_r: current residual norm  
        @type norm_r: non-negative C{float}  
        @param r: current residual (not used)  
        @param x: current solution approximation (not used)  
        @return: C{True} is the stopping criterium is fullfilled. Otherwise C{False} is returned.  
        @rtype: C{bool}  
   
        """  
        self.history.append(norm_r)  
        if self.verbose: print "iter: %s:  inner(rhat,r) = %e"#(len(self.history)-1, self.history[-1])  
        return self.history[-1]<=self.tolerance * self.history[0]  
   
    def stoppingcriterium2(self,norm_r,norm_b,solver="GMRES",TOL=None):  
        """  
        returns True if the C{norm_r} is C{tolerance}*C{norm_b}  
   
         
        @param norm_r: current residual norm  
        @type norm_r: non-negative C{float}  
        @param norm_b: norm of right hand side  
        @type norm_b: non-negative C{float}  
        @return: C{True} is the stopping criterium is fullfilled. Otherwise C{False} is returned.  
        @rtype: C{bool}  
   
        """  
        if TOL==None:  
           TOL=self.tolerance  
        self.history.append(norm_r)  
        if self.verbose: print "iter: %s:  norm(r) = %e"#(len(self.history)-1, self.history[-1])  
        return self.history[-1]<=TOL * norm_b  
453    
454  def PCG(b, Aprod, Msolve, bilinearform, stoppingcriterium, x=None, iter_max=100):  def PCG(r, Aprod, x, Msolve, bilinearform, atol=0, rtol=1.e-8, iter_max=100, initial_guess=True, verbose=False):
455     """     """
456     Solver for     Solver for
457    
458     M{Ax=b}     M{Ax=b}
459    
460     with a symmetric and positive definite operator A (more details required!).     with a symmetric and positive definite operator A (more details required!).
461     It uses the conjugate gradient method with preconditioner M providing an approximation of A.     It uses the conjugate gradient method with preconditioner M providing an
462       approximation of A.
463    
464     The iteration is terminated if the C{stoppingcriterium} function return C{True}.     The iteration is terminated if
465    
466     For details on the preconditioned conjugate gradient method see the book:     M{|r| <= atol+rtol*|r0|}
467    
468     Templates for the Solution of Linear Systems by R. Barrett, M. Berry,     where M{r0} is the initial residual and M{|.|} is the energy norm. In fact
    T.F. Chan, J. Demmel, J. Donato, J. Dongarra, V. Eijkhout, R. Pozo,  
    C. Romine, and H. van der Vorst.  
469    
470     @param b: the right hand side of the liner system. C{b} is altered.     M{|r| = sqrt( bilinearform(Msolve(r),r))}
471     @type b: any object supporting inplace add (x+=y) and scaling (x=scalar*y)  
472     @param Aprod: returns the value Ax     For details on the preconditioned conjugate gradient method see the book:
473     @type Aprod: function C{Aprod(x)} where C{x} is of the same object like argument C{x}. The returned object needs to be of the same type like argument C{b}.  
474     @param Msolve: solves Mx=r     I{Templates for the Solution of Linear Systems by R. Barrett, M. Berry,
475     @type Msolve: function C{Msolve(r)} where C{r} is of the same type like argument C{b}. The returned object needs to be of the same     T.F. Chan, J. Demmel, J. Donato, J. Dongarra, V. Eijkhout, R. Pozo,
476  type like argument C{x}.     C. Romine, and H. van der Vorst}.
477     @param bilinearform: inner product C{<x,r>}  
478     @type bilinearform: function C{bilinearform(x,r)} where C{x} is of the same type like argument C{x} and C{r} is . The returned value is a C{float}.     @param r: initial residual M{r=b-Ax}. C{r} is altered.
479     @param stoppingcriterium: function which returns True if a stopping criterium is meet. C{stoppingcriterium} has the arguments C{norm_r}, C{r} and C{x} giving the current norm of the residual (=C{sqrt(bilinearform(Msolve(r),r)}), the current residual and the current solution approximation. C{stoppingcriterium} is called in each iteration step.     @type r: any object supporting inplace add (x+=y) and scaling (x=scalar*y)
480     @type stoppingcriterium: function that returns C{True} or C{False}     @param x: an initial guess for the solution
    @param x: an initial guess for the solution. If no C{x} is given 0*b is used.  
481     @type x: any object supporting inplace add (x+=y) and scaling (x=scalar*y)     @type x: any object supporting inplace add (x+=y) and scaling (x=scalar*y)
482     @param iter_max: maximum number of iteration steps.     @param Aprod: returns the value Ax
483       @type Aprod: function C{Aprod(x)} where C{x} is of the same object like
484                    argument C{x}. The returned object needs to be of the same type
485                    like argument C{r}.
486       @param Msolve: solves Mx=r
487       @type Msolve: function C{Msolve(r)} where C{r} is of the same type like
488                     argument C{r}. The returned object needs to be of the same
489                     type like argument C{x}.
490       @param bilinearform: inner product C{<x,r>}
491       @type bilinearform: function C{bilinearform(x,r)} where C{x} is of the same
492                           type like argument C{x} and C{r} is. The returned value
493                           is a C{float}.
494       @param atol: absolute tolerance
495       @type atol: non-negative C{float}
496       @param rtol: relative tolerance
497       @type rtol: non-negative C{float}
498       @param iter_max: maximum number of iteration steps
499     @type iter_max: C{int}     @type iter_max: C{int}
500     @return: the solution approximation and the corresponding residual     @return: the solution approximation and the corresponding residual
501     @rtype: C{tuple}     @rtype: C{tuple}
502     @warning: C{b} and C{x} are altered.     @warning: C{r} and C{x} are altered.
503     """     """
504     iter=0     iter=0
    if x==None:  
       x=0*b  
    else:  
       b += (-1)*Aprod(x)  
    r=b  
505     rhat=Msolve(r)     rhat=Msolve(r)
506     d = rhat     d = rhat
507     rhat_dot_r = bilinearform(rhat, r)     rhat_dot_r = bilinearform(rhat, r)
508     if rhat_dot_r<0: raise NegativeNorm,"negative norm."     if rhat_dot_r<0: raise NegativeNorm,"negative norm."
509       norm_r0=math.sqrt(rhat_dot_r)
510       atol2=atol+rtol*norm_r0
511       if atol2<=0:
512          raise ValueError,"Non-positive tolarance."
513       atol2=max(atol2, 100. * util.EPSILON * norm_r0)
514    
515       if verbose: print "PCG: initial residual norm = %e (absolute tolerance = %e)"%(norm_r0, atol2)
516    
517    
518     while not stoppingcriterium(math.sqrt(rhat_dot_r),r,x):     while not math.sqrt(rhat_dot_r) <= atol2:
519         iter+=1         iter+=1
520         if iter  >= iter_max: raise MaxIterReached,"maximum number of %s steps reached."%iter_max         if iter  >= iter_max: raise MaxIterReached,"maximum number of %s steps reached."%iter_max
521    
# Line 557  type like argument C{x}. Line 532  type like argument C{x}.
532    
533         rhat_dot_r = rhat_dot_r_new         rhat_dot_r = rhat_dot_r_new
534         if rhat_dot_r<0: raise NegativeNorm,"negative norm."         if rhat_dot_r<0: raise NegativeNorm,"negative norm."
535           if verbose: print "PCG: iteration step %s: residual norm = %e"%(iter, math.sqrt(rhat_dot_r))
536       if verbose: print "PCG: tolerance reached after %s steps."%iter
537     return x,r     return x,r
538    
539  class Defect(object):  class Defect(object):
540      """      """
541      defines a non-linear defect F(x) of a variable x      Defines a non-linear defect F(x) of a variable x.
542      """      """
543      def __init__(self):      def __init__(self):
544          """          """
545          initialize defect          Initializes defect.
546          """          """
547          self.setDerivativeIncrementLength()          self.setDerivativeIncrementLength()
548    
549      def bilinearform(self, x0, x1):      def bilinearform(self, x0, x1):
550          """          """
551          returns the inner product of x0 and x1          Returns the inner product of x0 and x1
552          @param x0: a value for x  
553          @param x1: a value for x          @param x0: value for x0
554            @param x1: value for x1
555          @return: the inner product of x0 and x1          @return: the inner product of x0 and x1
556          @rtype: C{float}          @rtype: C{float}
557          """          """
558          return 0          return 0
559          
560      def norm(self,x):      def norm(self,x):
561          """          """
562          the norm of argument C{x}          Returns the norm of argument C{x}.
563    
564          @param x: a value for x          @param x: a value
565          @return: norm of argument x          @return: norm of argument x
566          @rtype: C{float}          @rtype: C{float}
567          @note: by default C{sqrt(self.bilinearform(x,x)} is retrurned.          @note: by default C{sqrt(self.bilinearform(x,x)} is returned.
568          """          """
569          s=self.bilinearform(x,x)          s=self.bilinearform(x,x)
570          if s<0: raise NegativeNorm,"negative norm."          if s<0: raise NegativeNorm,"negative norm."
571          return math.sqrt(s)          return math.sqrt(s)
572    
   
573      def eval(self,x):      def eval(self,x):
574          """          """
575          returns the value F of a given x          Returns the value F of a given C{x}.
576    
577          @param x: value for which the defect C{F} is evalulated.          @param x: value for which the defect C{F} is evaluated
578          @return: value of the defect at C{x}          @return: value of the defect at C{x}
579          """          """
580          return 0          return 0
# Line 608  class Defect(object): Line 584  class Defect(object):
584    
585      def setDerivativeIncrementLength(self,inc=math.sqrt(util.EPSILON)):      def setDerivativeIncrementLength(self,inc=math.sqrt(util.EPSILON)):
586          """          """
587          sets the relative length of the increment used to approximate the derivative of the defect          Sets the relative length of the increment used to approximate the
588          the increment is inc*norm(x)/norm(v)*v in the direction of v with x as a staring point.          derivative of the defect. The increment is inc*norm(x)/norm(v)*v in the
589            direction of v with x as a starting point.
590    
591          @param inc: relative increment length          @param inc: relative increment length
592          @type inc: positive C{float}          @type inc: positive C{float}
# Line 619  class Defect(object): Line 596  class Defect(object):
596    
597      def getDerivativeIncrementLength(self):      def getDerivativeIncrementLength(self):
598          """          """
599          returns the relative increment length used to approximate the derivative of the defect          Returns the relative increment length used to approximate the
600            derivative of the defect.
601          @return: value of the defect at C{x}          @return: value of the defect at C{x}
602          @rtype: positive C{float}          @rtype: positive C{float}
603          """          """
# Line 627  class Defect(object): Line 605  class Defect(object):
605    
606      def derivative(self, F0, x0, v, v_is_normalised=True):      def derivative(self, F0, x0, v, v_is_normalised=True):
607          """          """
608          returns the directional derivative at x0 in the direction of v          Returns the directional derivative at C{x0} in the direction of C{v}.
609    
610          @param F0: value of this defect at x0          @param F0: value of this defect at x0
611          @param x0: value at which derivative is calculated.          @param x0: value at which derivative is calculated
612          @param v: direction          @param v: direction
613          @param v_is_normalised: is true to indicate that C{v} is nomalized (self.norm(v)=0)          @param v_is_normalised: True to indicate that C{v} is nomalized
614                                    (self.norm(v)=0)
615          @return: derivative of this defect at x0 in the direction of C{v}          @return: derivative of this defect at x0 in the direction of C{v}
616          @note: by default numerical evaluation (self.eval(x0+eps*v)-F0)/eps is used but this method          @note: by default numerical evaluation (self.eval(x0+eps*v)-F0)/eps is
617          maybe oepsnew verwritten to use exact evalution.                 used but this method maybe overwritten to use exact evaluation.
618          """          """
619          normx=self.norm(x0)          normx=self.norm(x0)
620          if normx>0:          if normx>0:
# Line 651  class Defect(object): Line 630  class Defect(object):
630          F1=self.eval(x0 + epsnew * v)          F1=self.eval(x0 + epsnew * v)
631          return (F1-F0)/epsnew          return (F1-F0)/epsnew
632    
633  ######################################      ######################################
634  def NewtonGMRES(defect, x, iter_max=100, sub_iter_max=20, atol=0,rtol=1.e-4, sub_tol_max=0.5, gamma=0.9, verbose=False):  def NewtonGMRES(defect, x, iter_max=100, sub_iter_max=20, atol=0,rtol=1.e-4, sub_tol_max=0.5, gamma=0.9, verbose=False):
635     """     """
636     solves a non-linear problem M{F(x)=0} for unknown M{x} using the stopping criterion:     Solves a non-linear problem M{F(x)=0} for unknown M{x} using the stopping
637       criterion:
638    
639     M{norm(F(x) <= atol + rtol * norm(F(x0)}     M{norm(F(x) <= atol + rtol * norm(F(x0)}
640      
641     where M{x0} is the initial guess.     where M{x0} is the initial guess.
642    
643     @param defect: object defining the the function M{F}, C{defect.norm} defines the M{norm} used in the stopping criterion.     @param defect: object defining the function M{F}. C{defect.norm} defines the
644                      M{norm} used in the stopping criterion.
645     @type defect: L{Defect}     @type defect: L{Defect}
646     @param x: initial guess for the solution, C{x} is altered.     @param x: initial guess for the solution, C{x} is altered.
647     @type x: any object type allowing basic operations such as  L{numarray.NumArray}, L{Data}     @type x: any object type allowing basic operations such as
648     @param iter_max: maximum number of iteration steps              C{numarray.NumArray}, L{Data}
649       @param iter_max: maximum number of iteration steps
650     @type iter_max: positive C{int}     @type iter_max: positive C{int}
651     @param sub_iter_max:     @param sub_iter_max: maximum number of inner iteration steps
652     @type sub_iter_max:     @type sub_iter_max: positive C{int}
653     @param atol: absolute tolerance for the solution     @param atol: absolute tolerance for the solution
654     @type atol: positive C{float}     @type atol: positive C{float}
655     @param rtol: relative tolerance for the solution     @param rtol: relative tolerance for the solution
656     @type rtol: positive C{float}     @type rtol: positive C{float}
657     @param gamma: tolerance safety factor for inner iteration     @param gamma: tolerance safety factor for inner iteration
658     @type gamma: positive C{float}, less than 1     @type gamma: positive C{float}, less than 1
659     @param sub_tol_max: upper bound for inner tolerance.     @param sub_tol_max: upper bound for inner tolerance
660     @type sub_tol_max: positive C{float}, less than 1     @type sub_tol_max: positive C{float}, less than 1
661     @return: an approximation of the solution with the desired accuracy     @return: an approximation of the solution with the desired accuracy
662     @rtype: same type as the initial guess.     @rtype: same type as the initial guess
663     """     """
664     lmaxit=iter_max     lmaxit=iter_max
665     if atol<0: raise ValueError,"atol needs to be non-negative."     if atol<0: raise ValueError,"atol needs to be non-negative."
# Line 697  def NewtonGMRES(defect, x, iter_max=100, Line 679  def NewtonGMRES(defect, x, iter_max=100,
679     # main iteration loop     # main iteration loop
680     #     #
681     while not fnrm<=stop_tol:     while not fnrm<=stop_tol:
682              if iter  >= iter_max: raise MaxIterReached,"maximum number of %s steps reached."%iter_max              if iter  >= iter_max: raise MaxIterReached,"maximum number of %s steps reached."%iter_max
683              #              #
684          #   adjust sub_tol_          #   adjust sub_tol_
685          #          #
686              if iter > 1:              if iter > 1:
687             rat=fnrm/fnrmo             rat=fnrm/fnrmo
# Line 712  def NewtonGMRES(defect, x, iter_max=100, Line 694  def NewtonGMRES(defect, x, iter_max=100,
694              #     if iter_max in __FDGMRES is reached MaxIterReached is thrown              #     if iter_max in __FDGMRES is reached MaxIterReached is thrown
695              #     if iter_restart -1 is returned as sub_iter              #     if iter_restart -1 is returned as sub_iter
696              #     if  atol is reached sub_iter returns the numer of steps performed to get there              #     if  atol is reached sub_iter returns the numer of steps performed to get there
697              #              #
698              #                #
699              if verbose: print "             subiteration (GMRES) is called with relative tolerance %e."%sub_tol              if verbose: print "             subiteration (GMRES) is called with relative tolerance %e."%sub_tol
700              try:              try:
701                 xc, sub_iter=__FDGMRES(F, defect, x, sub_tol*fnrm, iter_max=iter_max-iter, iter_restart=sub_iter_max)                 xc, sub_iter=__FDGMRES(F, defect, x, sub_tol*fnrm, iter_max=iter_max-iter, iter_restart=sub_iter_max)
# Line 734  def NewtonGMRES(defect, x, iter_max=100, Line 716  def NewtonGMRES(defect, x, iter_max=100,
716    
717  def __givapp(c,s,vin):  def __givapp(c,s,vin):
718      """      """
719      apply a sequence of Givens rotations (c,s) to the recuirsively to the vector vin      Applies a sequence of Givens rotations (c,s) recursively to the vector
720        C{vin}
721    
722      @warning: C{vin} is altered.      @warning: C{vin} is altered.
723      """      """
724      vrot=vin      vrot=vin
725      if isinstance(c,float):      if isinstance(c,float):
726          vrot=[c*vrot[0]-s*vrot[1],s*vrot[0]+c*vrot[1]]          vrot=[c*vrot[0]-s*vrot[1],s*vrot[0]+c*vrot[1]]
727      else:      else:
# Line 756  def __FDGMRES(F0, defect, x0, atol, iter Line 740  def __FDGMRES(F0, defect, x0, atol, iter
740    
741     rho=defect.norm(F0)     rho=defect.norm(F0)
742     if rho<=0.: return x0*0     if rho<=0.: return x0*0
743      
744     v.append(-F0/rho)     v.append(-F0/rho)
745     g[0]=rho     g[0]=rho
746     iter=0     iter=0
747     while rho > atol and iter<iter_restart-1:     while rho > atol and iter<iter_restart-1:
748            if iter  >= iter_max:
749      if iter  >= iter_max: raise MaxIterReached,"maximum number of %s steps reached."%iter_max              raise MaxIterReached,"maximum number of %s steps reached."%iter_max
750    
751          p=defect.derivative(F0,x0,v[iter], v_is_normalised=True)          p=defect.derivative(F0,x0,v[iter], v_is_normalised=True)
752      v.append(p)          v.append(p)
753    
754      v_norm1=defect.norm(v[iter+1])          v_norm1=defect.norm(v[iter+1])
755    
756          # Modified Gram-Schmidt          # Modified Gram-Schmidt
757      for j in range(iter+1):          for j in range(iter+1):
758           h[j][iter]=defect.bilinearform(v[j],v[iter+1])                h[j,iter]=defect.bilinearform(v[j],v[iter+1])
759           v[iter+1]-=h[j][iter]*v[j]              v[iter+1]-=h[j,iter]*v[j]
760          
761      h[iter+1][iter]=defect.norm(v[iter+1])          h[iter+1,iter]=defect.norm(v[iter+1])
762      v_norm2=h[iter+1][iter]          v_norm2=h[iter+1,iter]
763    
764          # Reorthogonalize if needed          # Reorthogonalize if needed
765      if v_norm1 + 0.001*v_norm2 == v_norm1:   #Brown/Hindmarsh condition (default)          if v_norm1 + 0.001*v_norm2 == v_norm1:   #Brown/Hindmarsh condition (default)
766          for j in range(iter+1):                for j in range(iter+1):
767             hr=defect.bilinearform(v[j],v[iter+1])                  hr=defect.bilinearform(v[j],v[iter+1])
768                 h[j][iter]=h[j][iter]+hr                  h[j,iter]=h[j,iter]+hr
769                 v[iter+1] -= hr*v[j]                  v[iter+1] -= hr*v[j]
770    
771          v_norm2=defect.norm(v[iter+1])              v_norm2=defect.norm(v[iter+1])
772          h[iter+1][iter]=v_norm2              h[iter+1,iter]=v_norm2
773          #   watch out for happy breakdown          #   watch out for happy breakdown
774          if not v_norm2 == 0:          if not v_norm2 == 0:
775                  v[iter+1]=v[iter+1]/h[iter+1][iter]              v[iter+1]=v[iter+1]/h[iter+1,iter]
776    
777          #   Form and store the information for the new Givens rotation          #   Form and store the information for the new Givens rotation
778      if iter > 0 :          if iter > 0 :
779          hhat=numarray.zeros(iter+1,numarray.Float64)              hhat=numarray.zeros(iter+1,numarray.Float64)
780          for i in range(iter+1) : hhat[i]=h[i][iter]              for i in range(iter+1) : hhat[i]=h[i,iter]
781          hhat=__givapp(c[0:iter],s[0:iter],hhat);              hhat=__givapp(c[0:iter],s[0:iter],hhat);
782              for i in range(iter+1) : h[i][iter]=hhat[i]              for i in range(iter+1) : h[i,iter]=hhat[i]
783    
784      mu=math.sqrt(h[iter][iter]*h[iter][iter]+h[iter+1][iter]*h[iter+1][iter])          mu=math.sqrt(h[iter,iter]*h[iter,iter]+h[iter+1,iter]*h[iter+1,iter])
785    
786      if mu!=0 :          if mu!=0 :
787          c[iter]=h[iter][iter]/mu              c[iter]=h[iter,iter]/mu
788          s[iter]=-h[iter+1][iter]/mu              s[iter]=-h[iter+1,iter]/mu
789          h[iter][iter]=c[iter]*h[iter][iter]-s[iter]*h[iter+1][iter]              h[iter,iter]=c[iter]*h[iter,iter]-s[iter]*h[iter+1,iter]
790          h[iter+1][iter]=0.0              h[iter+1,iter]=0.0
791          g[iter:iter+2]=__givapp(c[iter],s[iter],g[iter:iter+2])              g[iter:iter+2]=__givapp(c[iter],s[iter],g[iter:iter+2])
792    
793          # Update the residual norm          # Update the residual norm
794          rho=abs(g[iter+1])          rho=abs(g[iter+1])
795      iter+=1          iter+=1
796    
797     # At this point either iter > iter_max or rho < tol.     # At this point either iter > iter_max or rho < tol.
798     # It's time to compute x and leave.             # It's time to compute x and leave.
799     if iter > 0 :     if iter > 0 :
800       y=numarray.zeros(iter,numarray.Float64)           y=numarray.zeros(iter,numarray.Float64)
801       y[iter-1] = g[iter-1] / h[iter-1][iter-1]       y[iter-1] = g[iter-1] / h[iter-1,iter-1]
802       if iter > 1 :         if iter > 1 :
803          i=iter-2            i=iter-2
804          while i>=0 :          while i>=0 :
805            y[i] = ( g[i] - numarray.dot(h[i][i+1:iter], y[i+1:iter])) / h[i][i]            y[i] = ( g[i] - numarray.dot(h[i,i+1:iter], y[i+1:iter])) / h[i,i]
806            i=i-1            i=i-1
807       xhat=v[iter-1]*y[iter-1]       xhat=v[iter-1]*y[iter-1]
808       for i in range(iter-1):       for i in range(iter-1):
809      xhat += v[i]*y[i]      xhat += v[i]*y[i]
810     else :     else :
811        xhat=v[0] * 0        xhat=v[0] * 0
812    
813     if iter<iter_restart-1:     if iter<iter_restart-1:
814        stopped=iter        stopped=iter
815     else:     else:
816        stopped=-1        stopped=-1
817    
818     return xhat,stopped     return xhat,stopped
819    
820  ##############################################  def GMRES(r, Aprod, x, bilinearform, atol=0, rtol=1.e-8, iter_max=100, iter_restart=20, verbose=False):
821  def GMRES(b, Aprod, Msolve, bilinearform, stoppingcriterium, x=None, iter_max=100, iter_restart=20):     """
822  ################################################     Solver for
823    
824       M{Ax=b}
825    
826       with a general operator A (more details required!).
827       It uses the generalized minimum residual method (GMRES).
828    
829       The iteration is terminated if
830    
831       M{|r| <= atol+rtol*|r0|}
832    
833       where M{r0} is the initial residual and M{|.|} is the energy norm. In fact
834    
835       M{|r| = sqrt( bilinearform(r,r))}
836    
837       @param r: initial residual M{r=b-Ax}. C{r} is altered.
838       @type r: any object supporting inplace add (x+=y) and scaling (x=scalar*y)
839       @param x: an initial guess for the solution
840       @type x: same like C{r}
841       @param Aprod: returns the value Ax
842       @type Aprod: function C{Aprod(x)} where C{x} is of the same object like
843                    argument C{x}. The returned object needs to be of the same
844                    type like argument C{r}.
845       @param bilinearform: inner product C{<x,r>}
846       @type bilinearform: function C{bilinearform(x,r)} where C{x} is of the same
847                           type like argument C{x} and C{r}. The returned value is
848                           a C{float}.
849       @param atol: absolute tolerance
850       @type atol: non-negative C{float}
851       @param rtol: relative tolerance
852       @type rtol: non-negative C{float}
853       @param iter_max: maximum number of iteration steps
854       @type iter_max: C{int}
855       @param iter_restart: in order to save memory the orthogonalization process
856                            is terminated after C{iter_restart} steps and the
857                            iteration is restarted.
858       @type iter_restart: C{int}
859       @return: the solution approximation and the corresponding residual
860       @rtype: C{tuple}
861       @warning: C{r} and C{x} are altered.
862       """
863     m=iter_restart     m=iter_restart
864       restarted=False
865     iter=0     iter=0
866     xc=x     if rtol>0:
867          r_dot_r = bilinearform(r, r)
868          if r_dot_r<0: raise NegativeNorm,"negative norm."
869          atol2=atol+rtol*math.sqrt(r_dot_r)
870          if verbose: print "GMRES: norm of right hand side = %e (absolute tolerance = %e)"%(math.sqrt(r_dot_r), atol2)
871       else:
872          atol2=atol
873          if verbose: print "GMRES: absolute tolerance = %e"%atol2
874       if atol2<=0:
875          raise ValueError,"Non-positive tolarance."
876    
877     while True:     while True:
878        if iter  >= iter_max: raise MaxIterReached,"maximum number of %s steps reached"%iter_max        if iter  >= iter_max: raise MaxIterReached,"maximum number of %s steps reached"%iter_max
879        xc,stopped=__GMRESm(b*1, Aprod, Msolve, bilinearform, stoppingcriterium, x=xc*1, iter_max=iter_max-iter, iter_restart=m)        if restarted:
880             r2 = r-Aprod(x-x2)
881          else:
882             r2=1*r
883          x2=x*1.
884          x,stopped=_GMRESm(r2, Aprod, x, bilinearform, atol2, iter_max=iter_max-iter, iter_restart=m, verbose=verbose)
885          iter+=iter_restart
886        if stopped: break        if stopped: break
887        iter+=iter_restart            if verbose: print "GMRES: restart."
888     return xc        restarted=True
889       if verbose: print "GMRES: tolerance has been reached."
890       return x
891    
892  def __GMRESm(b, Aprod, Msolve, bilinearform, stoppingcriterium, x=None, iter_max=100, iter_restart=20):  def _GMRESm(r, Aprod, x, bilinearform, atol, iter_max=100, iter_restart=20, verbose=False):
893     iter=0     iter=0
    r=Msolve(b)  
    r_dot_r = bilinearform(r, r)  
    if r_dot_r<0: raise NegativeNorm,"negative norm."  
    norm_b=math.sqrt(r_dot_r)  
894    
895     if x==None:     h=numarray.zeros((iter_restart+1,iter_restart),numarray.Float64)
       x=0*b  
    else:  
       r=Msolve(b-Aprod(x))  
       r_dot_r = bilinearform(r, r)  
       if r_dot_r<0: raise NegativeNorm,"negative norm."  
     
    h=numarray.zeros((iter_restart,iter_restart),numarray.Float64)  
896     c=numarray.zeros(iter_restart,numarray.Float64)     c=numarray.zeros(iter_restart,numarray.Float64)
897     s=numarray.zeros(iter_restart,numarray.Float64)     s=numarray.zeros(iter_restart,numarray.Float64)
898     g=numarray.zeros(iter_restart,numarray.Float64)     g=numarray.zeros(iter_restart+1,numarray.Float64)
899     v=[]     v=[]
900    
901       r_dot_r = bilinearform(r, r)
902       if r_dot_r<0: raise NegativeNorm,"negative norm."
903     rho=math.sqrt(r_dot_r)     rho=math.sqrt(r_dot_r)
904      
905     v.append(r/rho)     v.append(r/rho)
906     g[0]=rho     g[0]=rho
907    
908     while not (stoppingcriterium(rho,norm_b) or iter==iter_restart-1):     if verbose: print "GMRES: initial residual %e (absolute tolerance = %e)"%(rho,atol)
909       while not (rho<=atol or iter==iter_restart):
910    
911      if iter  >= iter_max: raise MaxIterReached,"maximum number of %s steps reached."%iter_max      if iter  >= iter_max: raise MaxIterReached,"maximum number of %s steps reached."%iter_max
912    
913      p=Msolve(Aprod(v[iter]))      p=Aprod(v[iter])
   
914      v.append(p)      v.append(p)
915    
916      v_norm1=math.sqrt(bilinearform(v[iter+1], v[iter+1]))        v_norm1=math.sqrt(bilinearform(v[iter+1], v[iter+1]))
917    
918    # Modified Gram-Schmidt
919        for j in range(iter+1):
920          h[j,iter]=bilinearform(v[j],v[iter+1])
921          v[iter+1]-=h[j,iter]*v[j]
922    
923  # Modified Gram-Schmidt      h[iter+1,iter]=math.sqrt(bilinearform(v[iter+1],v[iter+1]))
924      for j in range(iter+1):      v_norm2=h[iter+1,iter]
       h[j][iter]=bilinearform(v[j],v[iter+1])    
       v[iter+1]-=h[j][iter]*v[j]  
         
     h[iter+1][iter]=math.sqrt(bilinearform(v[iter+1],v[iter+1]))  
     v_norm2=h[iter+1][iter]  
925    
926  # Reorthogonalize if needed  # Reorthogonalize if needed
927      if v_norm1 + 0.001*v_norm2 == v_norm1:   #Brown/Hindmarsh condition (default)      if v_norm1 + 0.001*v_norm2 == v_norm1:   #Brown/Hindmarsh condition (default)
928       for j in range(iter+1):         for j in range(iter+1):
929          hr=bilinearform(v[j],v[iter+1])          hr=bilinearform(v[j],v[iter+1])
930              h[j][iter]=h[j][iter]+hr              h[j,iter]=h[j,iter]+hr
931              v[iter+1] -= hr*v[j]              v[iter+1] -= hr*v[j]
932    
933       v_norm2=math.sqrt(bilinearform(v[iter+1], v[iter+1]))         v_norm2=math.sqrt(bilinearform(v[iter+1], v[iter+1]))
934       h[iter+1][iter]=v_norm2       h[iter+1,iter]=v_norm2
935    
936  #   watch out for happy breakdown  #   watch out for happy breakdown
937          if not v_norm2 == 0:          if not v_norm2 == 0:
938           v[iter+1]=v[iter+1]/h[iter+1][iter]           v[iter+1]=v[iter+1]/h[iter+1,iter]
939    
940  #   Form and store the information for the new Givens rotation  #   Form and store the information for the new Givens rotation
941      if iter > 0 :      if iter > 0: h[:iter+1,iter]=__givapp(c[:iter],s[:iter],h[:iter+1,iter])
942          hhat=numarray.zeros(iter+1,numarray.Float64)      mu=math.sqrt(h[iter,iter]*h[iter,iter]+h[iter+1,iter]*h[iter+1,iter])
         for i in range(iter+1) : hhat[i]=h[i][iter]  
         hhat=__givapp(c[0:iter],s[0:iter],hhat);  
             for i in range(iter+1) : h[i][iter]=hhat[i]  
   
     mu=math.sqrt(h[iter][iter]*h[iter][iter]+h[iter+1][iter]*h[iter+1][iter])  
943    
944      if mu!=0 :      if mu!=0 :
945          c[iter]=h[iter][iter]/mu          c[iter]=h[iter,iter]/mu
946          s[iter]=-h[iter+1][iter]/mu          s[iter]=-h[iter+1,iter]/mu
947          h[iter][iter]=c[iter]*h[iter][iter]-s[iter]*h[iter+1][iter]          h[iter,iter]=c[iter]*h[iter,iter]-s[iter]*h[iter+1,iter]
948          h[iter+1][iter]=0.0          h[iter+1,iter]=0.0
949          g[iter:iter+2]=__givapp(c[iter],s[iter],g[iter:iter+2])          g[iter:iter+2]=__givapp(c[iter],s[iter],g[iter:iter+2])
   
950  # Update the residual norm  # Update the residual norm
951                  
952          rho=abs(g[iter+1])          rho=abs(g[iter+1])
953            if verbose: print "GMRES: iteration step %s: residual %e"%(iter,rho)
954      iter+=1      iter+=1
955    
956  # At this point either iter > iter_max or rho < tol.  # At this point either iter > iter_max or rho < tol.
957  # It's time to compute x and leave.          # It's time to compute x and leave.
958    
959     if iter > 0 :     if verbose: print "GMRES: iteration stopped after %s step."%iter
960       y=numarray.zeros(iter,numarray.Float64)         if iter > 0 :
961       y[iter-1] = g[iter-1] / h[iter-1][iter-1]       y=numarray.zeros(iter,numarray.Float64)
962       if iter > 1 :         y[iter-1] = g[iter-1] / h[iter-1,iter-1]
963          i=iter-2         if iter > 1 :
964            i=iter-2
965          while i>=0 :          while i>=0 :
966            y[i] = ( g[i] - numarray.dot(h[i][i+1:iter], y[i+1:iter])) / h[i][i]            y[i] = ( g[i] - numarray.dot(h[i,i+1:iter], y[i+1:iter])) / h[i,i]
967            i=i-1            i=i-1
968       xhat=v[iter-1]*y[iter-1]       xhat=v[iter-1]*y[iter-1]
969       for i in range(iter-1):       for i in range(iter-1):
970      xhat += v[i]*y[i]      xhat += v[i]*y[i]
971     else : xhat=v[0]     else:
972         xhat=v[0] * 0
973    
974     x += xhat     x += xhat
975     if iter<iter_restart-1:     if iter<iter_restart-1:
976        stopped=True        stopped=True
977     else:     else:
978        stopped=False        stopped=False
979    
980     return x,stopped     return x,stopped
981    
982  #################################################  def MINRES(r, Aprod, x, Msolve, bilinearform, atol=0, rtol=1.e-8, iter_max=100):
983  def MINRES(b, Aprod, Msolve, bilinearform, stoppingcriterium, x=None, iter_max=100):      """
984  #################################################      Solver for
985      #  
986      #  minres solves the system of linear equations Ax = b      M{Ax=b}
987      #  where A is a symmetric matrix (possibly indefinite or singular)  
988      #  and b is a given vector.      with a symmetric and positive definite operator A (more details required!).
989      #        It uses the minimum residual method (MINRES) with preconditioner M
990      #  "A" may be a dense or sparse matrix (preferably sparse!)      providing an approximation of A.
991      #  or the name of a function such that  
992      #               y = A(x)      The iteration is terminated if
993      #  returns the product y = Ax for any given vector x.  
994      #      M{|r| <= atol+rtol*|r0|}
995      #  "M" defines a positive-definite preconditioner M = C C'.  
996      #  "M" may be a dense or sparse matrix (preferably sparse!)      where M{r0} is the initial residual and M{|.|} is the energy norm. In fact
997      #  or the name of a function such that  
998      #  solves the system My = x for any given vector x.      M{|r| = sqrt( bilinearform(Msolve(r),r))}
999      #  
1000      #      For details on the preconditioned conjugate gradient method see the book:
1001        
1002        I{Templates for the Solution of Linear Systems by R. Barrett, M. Berry,
1003        T.F. Chan, J. Demmel, J. Donato, J. Dongarra, V. Eijkhout, R. Pozo,
1004        C. Romine, and H. van der Vorst}.
1005    
1006        @param r: initial residual M{r=b-Ax}. C{r} is altered.
1007        @type r: any object supporting inplace add (x+=y) and scaling (x=scalar*y)
1008        @param x: an initial guess for the solution
1009        @type x: any object supporting inplace add (x+=y) and scaling (x=scalar*y)
1010        @param Aprod: returns the value Ax
1011        @type Aprod: function C{Aprod(x)} where C{x} is of the same object like
1012                     argument C{x}. The returned object needs to be of the same
1013                     type like argument C{r}.
1014        @param Msolve: solves Mx=r
1015        @type Msolve: function C{Msolve(r)} where C{r} is of the same type like
1016                      argument C{r}. The returned object needs to be of the same
1017                      type like argument C{x}.
1018        @param bilinearform: inner product C{<x,r>}
1019        @type bilinearform: function C{bilinearform(x,r)} where C{x} is of the same
1020                            type like argument C{x} and C{r} is. The returned value
1021                            is a C{float}.
1022        @param atol: absolute tolerance
1023        @type atol: non-negative C{float}
1024        @param rtol: relative tolerance
1025        @type rtol: non-negative C{float}
1026        @param iter_max: maximum number of iteration steps
1027        @type iter_max: C{int}
1028        @return: the solution approximation and the corresponding residual
1029        @rtype: C{tuple}
1030        @warning: C{r} and C{x} are altered.
1031        """
1032      #------------------------------------------------------------------      #------------------------------------------------------------------
1033      # Set up y and v for the first Lanczos vector v1.      # Set up y and v for the first Lanczos vector v1.
1034      # y  =  beta1 P' v1,  where  P = C**(-1).      # y  =  beta1 P' v1,  where  P = C**(-1).
1035      # v is really P' v1.      # v is really P' v1.
1036      #------------------------------------------------------------------      #------------------------------------------------------------------
1037      if x==None:      r1    = r
1038        x=0*b      y = Msolve(r)
1039      else:      beta1 = bilinearform(y,r)
       b += (-1)*Aprod(x)  
1040    
     r1    = b  
     y = Msolve(b)  
     beta1 = bilinearform(y,b)  
   
1041      if beta1< 0: raise NegativeNorm,"negative norm."      if beta1< 0: raise NegativeNorm,"negative norm."
1042    
1043      #  If b = 0 exactly, stop with x = 0.      #  If r = 0 exactly, stop with x
1044      if beta1==0: return x*0.      if beta1==0: return x
1045    
1046      if beta1> 0:      if beta1> 0: beta1  = math.sqrt(beta1)
       beta1  = math.sqrt(beta1)        
1047    
1048      #------------------------------------------------------------------      #------------------------------------------------------------------
1049      # Initialize quantities.      # Initialize quantities.
# Line 1008  def MINRES(b, Aprod, Msolve, bilinearfor Line 1063  def MINRES(b, Aprod, Msolve, bilinearfor
1063      ynorm2 = 0      ynorm2 = 0
1064      cs     = -1      cs     = -1
1065      sn     = 0      sn     = 0
1066      w      = b*0.      w      = r*0.
1067      w2     = b*0.      w2     = r*0.
1068      r2     = r1      r2     = r1
1069      eps    = 0.0001      eps    = 0.0001
1070    
1071      #---------------------------------------------------------------------      #---------------------------------------------------------------------
1072      # Main iteration loop.      # Main iteration loop.
1073      # --------------------------------------------------------------------      # --------------------------------------------------------------------
1074      while not stoppingcriterium(rnorm,Anorm*ynorm,'MINRES'):    #  checks ||r|| < (||A|| ||x||) * TOL      while not rnorm<=atol+rtol*Anorm*ynorm:    #  checks ||r|| < (||A|| ||x||) * TOL
1075    
1076      if iter  >= iter_max: raise MaxIterReached,"maximum number of %s steps reached."%iter_max      if iter  >= iter_max: raise MaxIterReached,"maximum number of %s steps reached."%iter_max
1077          iter    = iter  +  1          iter    = iter  +  1
# Line 1035  def MINRES(b, Aprod, Msolve, bilinearfor Line 1090  def MINRES(b, Aprod, Msolve, bilinearfor
1090          #-----------------------------------------------------------------          #-----------------------------------------------------------------
1091          s = 1/beta                 # Normalize previous vector (in y).          s = 1/beta                 # Normalize previous vector (in y).
1092          v = s*y                    # v = vk if P = I          v = s*y                    # v = vk if P = I
1093        
1094          y      = Aprod(v)          y      = Aprod(v)
1095        
1096          if iter >= 2:          if iter >= 2:
1097            y = y - (beta/oldb)*r1            y = y - (beta/oldb)*r1
1098    
1099          alfa   = bilinearform(v,y)              # alphak          alfa   = bilinearform(v,y)              # alphak
1100          y      += (- alfa/beta)*r2          y      += (- alfa/beta)*r2
1101          r1     = r2          r1     = r2
1102          r2     = y          r2     = y
1103          y = Msolve(r2)          y = Msolve(r2)
# Line 1052  def MINRES(b, Aprod, Msolve, bilinearfor Line 1107  def MINRES(b, Aprod, Msolve, bilinearfor
1107    
1108          beta   = math.sqrt( beta )          beta   = math.sqrt( beta )
1109          tnorm2 = tnorm2 + alfa*alfa + oldb*oldb + beta*beta          tnorm2 = tnorm2 + alfa*alfa + oldb*oldb + beta*beta
1110            
1111          if iter==1:                 # Initialize a few things.          if iter==1:                 # Initialize a few things.
1112            gmax   = abs( alfa )      # alpha1            gmax   = abs( alfa )      # alpha1
1113            gmin   = gmax             # alpha1            gmin   = gmax             # alpha1
# Line 1060  def MINRES(b, Aprod, Msolve, bilinearfor Line 1115  def MINRES(b, Aprod, Msolve, bilinearfor
1115          # Apply previous rotation Qk-1 to get          # Apply previous rotation Qk-1 to get
1116          #   [deltak epslnk+1] = [cs  sn][dbark    0   ]          #   [deltak epslnk+1] = [cs  sn][dbark    0   ]
1117          #   [gbar k dbar k+1]   [sn -cs][alfak betak+1].          #   [gbar k dbar k+1]   [sn -cs][alfak betak+1].
1118        
1119          oldeps = epsln          oldeps = epsln
1120          delta  = cs * dbar  +  sn * alfa  # delta1 = 0         deltak          delta  = cs * dbar  +  sn * alfa  # delta1 = 0         deltak
1121          gbar   = sn * dbar  -  cs * alfa  # gbar 1 = alfa1     gbar k          gbar   = sn * dbar  -  cs * alfa  # gbar 1 = alfa1     gbar k
# Line 1070  def MINRES(b, Aprod, Msolve, bilinearfor Line 1125  def MINRES(b, Aprod, Msolve, bilinearfor
1125          # Compute the next plane rotation Qk          # Compute the next plane rotation Qk
1126    
1127          gamma  = math.sqrt(gbar*gbar+beta*beta)  # gammak          gamma  = math.sqrt(gbar*gbar+beta*beta)  # gammak
1128          gamma  = max(gamma,eps)          gamma  = max(gamma,eps)
1129          cs     = gbar / gamma             # ck          cs     = gbar / gamma             # ck
1130          sn     = beta / gamma             # sk          sn     = beta / gamma             # sk
1131          phi    = cs * phibar              # phik          phi    = cs * phibar              # phik
# Line 1078  def MINRES(b, Aprod, Msolve, bilinearfor Line 1133  def MINRES(b, Aprod, Msolve, bilinearfor
1133    
1134          # Update  x.          # Update  x.
1135    
1136          denom = 1/gamma          denom = 1/gamma
1137          w1    = w2          w1    = w2
1138          w2    = w          w2    = w
1139          w     = (v - oldeps*w1 - delta*w2) * denom          w     = (v - oldeps*w1 - delta*w2) * denom
1140          x     +=  phi*w          x     +=  phi*w
1141    
# Line 1095  def MINRES(b, Aprod, Msolve, bilinearfor Line 1150  def MINRES(b, Aprod, Msolve, bilinearfor
1150    
1151          # Estimate various norms and test for convergence.          # Estimate various norms and test for convergence.
1152    
1153          Anorm  = math.sqrt( tnorm2 )          Anorm  = math.sqrt( tnorm2 )
1154          ynorm  = math.sqrt( ynorm2 )          ynorm  = math.sqrt( ynorm2 )
1155    
1156          rnorm  = phibar          rnorm  = phibar
1157    
1158      return x      return x
1159    
1160  def TFQMR(b, Aprod, Msolve, bilinearform, stoppingcriterium, x=None, iter_max=100):  def TFQMR(r, Aprod, x, bilinearform, atol=0, rtol=1.e-8, iter_max=100):
1161      """
1162      Solver for
1163    
1164  # TFQMR solver for linear systems    M{Ax=b}
1165  #  
1166  #    with a general operator A (more details required!).
1167  # initialization    It uses the Transpose-Free Quasi-Minimal Residual method (TFQMR).
 #  
   errtol = math.sqrt(bilinearform(b,b))  
   norm_b=errtol  
   kmax  = iter_max  
   error = []  
   
   if math.sqrt(bilinearform(x,x)) != 0.0:  
     r = b - Aprod(x)  
   else:  
     r = b  
1168    
1169    r=Msolve(r)    The iteration is terminated if
1170    
1171      M{|r| <= atol+rtol*|r0|}
1172    
1173      where M{r0} is the initial residual and M{|.|} is the energy norm. In fact
1174    
1175      M{|r| = sqrt( bilinearform(r,r))}
1176    
1177      @param r: initial residual M{r=b-Ax}. C{r} is altered.
1178      @type r: any object supporting inplace add (x+=y) and scaling (x=scalar*y)
1179      @param x: an initial guess for the solution
1180      @type x: same like C{r}
1181      @param Aprod: returns the value Ax
1182      @type Aprod: function C{Aprod(x)} where C{x} is of the same object like
1183                   argument C{x}. The returned object needs to be of the same type
1184                   like argument C{r}.
1185      @param bilinearform: inner product C{<x,r>}
1186      @type bilinearform: function C{bilinearform(x,r)} where C{x} is of the same
1187                          type like argument C{x} and C{r}. The returned value is
1188                          a C{float}.
1189      @param atol: absolute tolerance
1190      @type atol: non-negative C{float}
1191      @param rtol: relative tolerance
1192      @type rtol: non-negative C{float}
1193      @param iter_max: maximum number of iteration steps
1194      @type iter_max: C{int}
1195      @rtype: C{tuple}
1196      @warning: C{r} and C{x} are altered.
1197      """
1198    u1=0    u1=0
1199    u2=0    u2=0
1200    y1=0    y1=0
1201    y2=0    y2=0
1202    
1203    w = r    w = r
1204    y1 = r    y1 = r
1205    iter = 0    iter = 0
1206    d = 0    d = 0
1207        v = Aprod(y1)
   v = Msolve(Aprod(y1))  
1208    u1 = v    u1 = v
1209      
1210    theta = 0.0;    theta = 0.0;
1211    eta = 0.0;    eta = 0.0;
1212    tau = math.sqrt(bilinearform(r,r))    rho=bilinearform(r,r)
1213    error = [ error, tau ]    if rho < 0: raise NegativeNorm,"negative norm."
1214    rho = tau * tau    tau = math.sqrt(rho)
1215    m=1    norm_r0=tau
1216  #    while tau>atol+rtol*norm_r0:
 #  TFQMR iteration  
 #  
 #  while ( iter < kmax-1 ):  
     
   while not stoppingcriterium(tau*math.sqrt ( m + 1 ),norm_b,'TFQMR'):  
1217      if iter  >= iter_max: raise MaxIterReached,"maximum number of %s steps reached."%iter_max      if iter  >= iter_max: raise MaxIterReached,"maximum number of %s steps reached."%iter_max
1218    
1219      sigma = bilinearform(r,v)      sigma = bilinearform(r,v)
1220        if sigma == 0.0: raise IterationBreakDown,'TFQMR breakdown, sigma=0'
     if ( sigma == 0.0 ):  
       raise 'TFQMR breakdown, sigma=0'  
       
1221    
1222      alpha = rho / sigma      alpha = rho / sigma
1223    
# Line 1162  def TFQMR(b, Aprod, Msolve, bilinearform Line 1227  def TFQMR(b, Aprod, Msolve, bilinearform
1227  #  #
1228        if ( j == 1 ):        if ( j == 1 ):
1229          y2 = y1 - alpha * v          y2 = y1 - alpha * v
1230          u2 = Msolve(Aprod(y2))          u2 = Aprod(y2)
1231    
1232        m = 2 * (iter+1) - 2 + (j+1)        m = 2 * (iter+1) - 2 + (j+1)
1233        if j==0:        if j==0:
1234           w = w - alpha * u1           w = w - alpha * u1
1235           d = y1 + ( theta * theta * eta / alpha ) * d           d = y1 + ( theta * theta * eta / alpha ) * d
1236        if j==1:        if j==1:
# Line 1180  def TFQMR(b, Aprod, Msolve, bilinearform Line 1245  def TFQMR(b, Aprod, Msolve, bilinearform
1245  #  #
1246  #  Try to terminate the iteration at each pass through the loop  #  Try to terminate the iteration at each pass through the loop
1247  #  #
1248       # if ( tau * math.sqrt ( m + 1 ) <= errtol ):      if rho == 0.0: raise IterationBreakDown,'TFQMR breakdown, rho=0'
      #   error = [ error, tau ]  
      #   total_iters = iter  
      #   break  
         
   
     if ( rho == 0.0 ):  
       raise 'TFQMR breakdown, rho=0'  
       
1249    
1250      rhon = bilinearform(r,w)      rhon = bilinearform(r,w)
1251      beta = rhon / rho;      beta = rhon / rho;
1252      rho = rhon;      rho = rhon;
1253      y1 = w + beta * y2;      y1 = w + beta * y2;
1254      u1 = Msolve(Aprod(y1))      u1 = Aprod(y1)
1255      v = u1 + beta * ( u2 + beta * v )      v = u1 + beta * ( u2 + beta * v )
1256      error = [ error, tau ]  
1257      total_iters = iter      iter += 1
       
     iter = iter + 1  
1258    
1259    return x    return x
1260    
# Line 1208  def TFQMR(b, Aprod, Msolve, bilinearform Line 1263  def TFQMR(b, Aprod, Msolve, bilinearform
1263    
1264  class ArithmeticTuple(object):  class ArithmeticTuple(object):
1265     """     """
1266     tuple supporting inplace update x+=y and scaling x=a*y where x,y is an ArithmeticTuple and a is a float.     Tuple supporting inplace update x+=y and scaling x=a*y where C{x,y} is an
1267       ArithmeticTuple and C{a} is a float.
1268    
1269     example of usage:     Example of usage::
1270    
1271     from esys.escript import Data         from esys.escript import Data
1272     from numarray import array         from numarray import array
1273     a=Data(...)         a=Data(...)
1274     b=array([1.,4.])         b=array([1.,4.])
1275     x=ArithmeticTuple(a,b)         x=ArithmeticTuple(a,b)
1276     y=5.*x         y=5.*x
1277    
1278     """     """
1279     def __init__(self,*args):     def __init__(self,*args):
1280         """         """
1281         initialize object with elements args.         Initializes object with elements C{args}.
1282    
1283         @param args: tuple of object that support implace add (x+=y) and scaling (x=a*y)         @param args: tuple of objects that support inplace add (x+=y) and
1284                        scaling (x=a*y)
1285         """         """
1286         self.__items=list(args)         self.__items=list(args)
1287    
1288     def __len__(self):     def __len__(self):
1289         """         """
1290         number of items         Returns the number of items.
1291    
1292         @return: number of items         @return: number of items
1293         @rtype: C{int}         @rtype: C{int}
# Line 1239  class ArithmeticTuple(object): Line 1296  class ArithmeticTuple(object):
1296    
1297     def __getitem__(self,index):     def __getitem__(self,index):
1298         """         """
1299         get an item         Returns item at specified position.
1300    
1301         @param index: item to be returned         @param index: index of item to be returned
1302         @type index: C{int}         @type index: C{int}
1303         @return: item with index C{index}         @return: item with index C{index}
1304         """         """
# Line 1249  class ArithmeticTuple(object): Line 1306  class ArithmeticTuple(object):
1306    
1307     def __mul__(self,other):     def __mul__(self,other):
1308         """         """
1309         scaling from the right         Scales by C{other} from the right.
1310    
1311         @param other: scaling factor         @param other: scaling factor
1312         @type other: C{float}         @type other: C{float}
# Line 1257  class ArithmeticTuple(object): Line 1314  class ArithmeticTuple(object):
1314         @rtype: L{ArithmeticTuple}         @rtype: L{ArithmeticTuple}
1315         """         """
1316         out=[]         out=[]
1317         try:           try:
1318             l=len(other)             l=len(other)
1319             if l!=len(self):             if l!=len(self):
1320                 raise ValueError,"length of of arguments don't match."                 raise ValueError,"length of arguments don't match."
1321             for i in range(l): out.append(self[i]*other[i])             for i in range(l): out.append(self[i]*other[i])
1322         except TypeError:         except TypeError:
1323         for i in range(len(self)): out.append(self[i]*other)             for i in range(len(self)): out.append(self[i]*other)
1324         return ArithmeticTuple(*tuple(out))         return ArithmeticTuple(*tuple(out))
1325    
1326     def __rmul__(self,other):     def __rmul__(self,other):
1327         """         """
1328         scaling from the left         Scales by C{other} from the left.
1329    
1330         @param other: scaling factor         @param other: scaling factor
1331         @type other: C{float}         @type other: C{float}
# Line 1276  class ArithmeticTuple(object): Line 1333  class ArithmeticTuple(object):
1333         @rtype: L{ArithmeticTuple}         @rtype: L{ArithmeticTuple}
1334         """         """
1335         out=[]         out=[]
1336         try:           try:
1337             l=len(other)             l=len(other)
1338             if l!=len(self):             if l!=len(self):
1339                 raise ValueError,"length of of arguments don't match."                 raise ValueError,"length of arguments don't match."
1340             for i in range(l): out.append(other[i]*self[i])             for i in range(l): out.append(other[i]*self[i])
1341         except TypeError:         except TypeError:
1342         for i in range(len(self)): out.append(other*self[i])             for i in range(len(self)): out.append(other*self[i])
1343         return ArithmeticTuple(*tuple(out))         return ArithmeticTuple(*tuple(out))
1344    
1345     def __div__(self,other):     def __div__(self,other):
1346         """         """
1347         dividing from the right         Scales by (1/C{other}) from the right.
1348    
1349         @param other: scaling factor         @param other: scaling factor
1350         @type other: C{float}         @type other: C{float}
# Line 1298  class ArithmeticTuple(object): Line 1355  class ArithmeticTuple(object):
1355    
1356     def __rdiv__(self,other):     def __rdiv__(self,other):
1357         """         """
1358         dividing from the left         Scales by (1/C{other}) from the left.
1359    
1360         @param other: scaling factor         @param other: scaling factor
1361         @type other: C{float}         @type other: C{float}
# Line 1306  class ArithmeticTuple(object): Line 1363  class ArithmeticTuple(object):
1363         @rtype: L{ArithmeticTuple}         @rtype: L{ArithmeticTuple}
1364         """         """
1365         out=[]         out=[]
1366         try:           try:
1367             l=len(other)             l=len(other)
1368             if l!=len(self):             if l!=len(self):
1369                 raise ValueError,"length of of arguments don't match."                 raise ValueError,"length of arguments don't match."
1370             for i in range(l): out.append(other[i]/self[i])             for i in range(l): out.append(other[i]/self[i])
1371         except TypeError:         except TypeError:
1372         for i in range(len(self)): out.append(other/self[i])             for i in range(len(self)): out.append(other/self[i])
1373         return ArithmeticTuple(*tuple(out))         return ArithmeticTuple(*tuple(out))
1374      
1375     def __iadd__(self,other):     def __iadd__(self,other):
1376         """         """
1377         in-place add of other to self         Inplace addition of C{other} to self.
1378    
1379         @param other: increment         @param other: increment
1380         @type other: C{ArithmeticTuple}         @type other: C{ArithmeticTuple}
1381         """         """
1382         if len(self) != len(other):         if len(self) != len(other):
1383             raise ValueError,"tuple length must match."             raise ValueError,"tuple lengths must match."
1384         for i in range(len(self)):         for i in range(len(self)):
1385             self.__items[i]+=other[i]             self.__items[i]+=other[i]
1386         return self         return self
1387    
1388     def __add__(self,other):     def __add__(self,other):
1389         """         """
1390         add other to self         Adds C{other} to self.
1391    
1392         @param other: increment         @param other: increment
1393         @type other: C{ArithmeticTuple}         @type other: C{ArithmeticTuple}
1394         """         """
1395         out=[]         out=[]
1396         try:           try:
1397             l=len(other)             l=len(other)
1398             if l!=len(self):             if l!=len(self):
1399                 raise ValueError,"length of of arguments don't match."                 raise ValueError,"length of arguments don't match."
1400             for i in range(l): out.append(self[i]+other[i])             for i in range(l): out.append(self[i]+other[i])
1401         except TypeError:         except TypeError:
1402         for i in range(len(self)): out.append(self[i]+other)             for i in range(len(self)): out.append(self[i]+other)
1403         return ArithmeticTuple(*tuple(out))         return ArithmeticTuple(*tuple(out))
1404    
1405     def __sub__(self,other):     def __sub__(self,other):
1406         """         """
1407         subtract other from self         Subtracts C{other} from self.
1408    
1409         @param other: increment         @param other: decrement
1410         @type other: C{ArithmeticTuple}         @type other: C{ArithmeticTuple}
1411         """         """
1412         out=[]         out=[]
1413         try:           try:
1414             l=len(other)             l=len(other)
1415             if l!=len(self):             if l!=len(self):
1416                 raise ValueError,"length of of arguments don't match."                 raise ValueError,"length of arguments don't match."
1417             for i in range(l): out.append(self[i]-other[i])             for i in range(l): out.append(self[i]-other[i])
1418         except TypeError:         except TypeError:
1419         for i in range(len(self)): out.append(self[i]-other)             for i in range(len(self)): out.append(self[i]-other)
1420         return ArithmeticTuple(*tuple(out))         return ArithmeticTuple(*tuple(out))
1421      
1422     def __isub__(self,other):     def __isub__(self,other):
1423         """         """
1424         subtract other from self         Inplace subtraction of C{other} from self.
1425    
1426         @param other: increment         @param other: decrement
1427         @type other: C{ArithmeticTuple}         @type other: C{ArithmeticTuple}
1428         """         """
1429         if len(self) != len(other):         if len(self) != len(other):
# Line 1377  class ArithmeticTuple(object): Line 1434  class ArithmeticTuple(object):
1434    
1435     def __neg__(self):     def __neg__(self):
1436         """         """
1437         negate         Negates values.
   
1438         """         """
1439         out=[]         out=[]
1440         for i in range(len(self)):         for i in range(len(self)):
# Line 1388  class ArithmeticTuple(object): Line 1444  class ArithmeticTuple(object):
1444    
1445  class HomogeneousSaddlePointProblem(object):  class HomogeneousSaddlePointProblem(object):
1446        """        """
1447        This provides a framwork for solving linear homogeneous saddle point problem of the form        This class provides a framework for solving linear homogeneous saddle
1448          point problems of the form::
              Av+B^*p=f  
              Bv    =0  
1449    
1450        for the unknowns v and p and given operators A and B and given right hand side f.            M{Av+B^*p=f}
1451        B^* is the adjoint operator of B is the given inner product.            M{Bv     =0}
1452    
1453          for the unknowns M{v} and M{p} and given operators M{A} and M{B} and
1454          given right hand side M{f}. M{B^*} is the adjoint operator of M{B}.
1455        """        """
1456        def __init__(self,**kwargs):        def __init__(self,**kwargs):
1457          self.setTolerance()          self.setTolerance()
1458          self.setToleranceReductionFactor()          self.setAbsoluteTolerance()
1459            self.setSubProblemTolerance()
1460    
1461          #=============================================================
1462        def initialize(self):        def initialize(self):
1463          """          """
1464          initialize the problem (overwrite)          Initializes the problem (overwrite).
1465          """          """
1466          pass          pass
1467        def B(self,v):  
1468          def inner_pBv(self,p,v):
1469           """           """
1470           returns Bv (overwrite)           Returns inner product of element p and Bv (overwrite).
          @rtype: equal to the type of p  
1471    
1472           @note: boundary conditions on p should be zero!           @param p: a pressure increment
1473             @param v: a residual
1474             @return: inner product of element p and Bv
1475             @rtype: C{float}
1476             @note: used if PCG is applied.
1477           """           """
1478           pass           raise NotImplementedError,"no inner product for p implemented."
1479    
1480        def inner(self,p0,p1):        def inner_p(self,p0,p1):
1481           """           """
1482           returns inner product of two element p0 and p1  (overwrite)           Returns inner product of p0 and p1 (overwrite).
1483            
1484           @type p0: equal to the type of p           @param p0: a pressure
1485           @type p1: equal to the type of p           @param p1: a pressure
1486             @return: inner product of p0 and p1
1487           @rtype: C{float}           @rtype: C{float}
1488             """
1489             raise NotImplementedError,"no inner product for p implemented."
1490      
1491          def norm_v(self,v):
1492             """
1493             Returns the norm of v (overwrite).
1494    
1495           @rtype: equal to the type of p           @param v: a velovity
1496             @return: norm of v
1497             @rtype: non-negative C{float}
1498             """
1499             raise NotImplementedError,"no norm of v implemented."
1500    
1501    
1502          def getV(self, p, v0):
1503           """           """
1504           pass           return the value for v for a given p (overwrite)
1505    
1506             @param p: a pressure
1507             @param v0: a initial guess for the value v to return.
1508             @return: v given as M{v= A^{-1} (f-B^*p)}
1509             """
1510             raise NotImplementedError,"no v calculation implemented."
1511            
1512          def norm_Bv(self,v):
1513            """
1514            Returns Bv (overwrite).
1515    
1516            @rtype: equal to the type of p
1517            @note: boundary conditions on p should be zero!
1518            """
1519            raise NotImplementedError, "no operator B implemented."
1520    
1521        def solve_A(self,u,p):        def solve_AinvBt(self,p):
1522           """           """
1523           solves Av=f-Au-B^*p with accuracy self.getReducedTolerance() (overwrite)           Solves M{Av=B^*p} with accuracy L{self.getSubProblemTolerance()}
1524             (overwrite).
1525    
1526           @rtype: equal to the type of v           @param p: a pressure increment
1527             @return: the solution of M{Av=B^*p}
1528           @note: boundary conditions on v should be zero!           @note: boundary conditions on v should be zero!
1529           """           """
1530           pass           raise NotImplementedError,"no operator A implemented."
1531    
1532        def solve_prec(self,p):        def solve_precB(self,v):
1533           """           """
1534           provides a preconditioner for BA^{-1}B^* with accuracy self.getReducedTolerance() (overwrite)           Provides a preconditioner for M{BA^{-1}B^*} with accuracy
1535             L{self.getSubProblemTolerance()} (overwrite).
1536    
1537           @rtype: equal to the type of p           @rtype: equal to the type of p
1538             @note: boundary conditions on p should be zero!
1539           """           """
1540           pass           raise NotImplementedError,"no preconditioner for Schur complement implemented."
1541          #=============================================================
1542          def __Aprod_PCG(self,p):
1543              return self.solve_AinvBt(p)
1544    
1545          def __inner_PCG(self,p,v):
1546             return self.inner_pBv(p,v)
1547    
1548          def __Msolve_PCG(self,v):
1549              return self.solve_precB(v)
1550          #=============================================================
1551          def __Aprod_GMRES(self,p):
1552              return self.solve_precB(self.solve_AinvBt(p))
1553          def __inner_GMRES(self,p0,p1):
1554             return self.inner_p(p0,p1)
1555          #=============================================================
1556          def norm_p(self,p):
1557              """
1558              calculates the norm of C{p}
1559              
1560              @param p: a pressure
1561              @return: the norm of C{p} using the inner product for pressure
1562              @rtype: C{float}
1563              """
1564              f=self.inner_p(p,p)
1565              if f<0: raise ValueError,"negative pressure norm."
1566              return math.sqrt(f)
1567              
1568    
1569        def stoppingcriterium(self,Bv,v,p):        def solve(self,v,p,max_iter=20, verbose=False, show_details=False, usePCG=True, iter_restart=20, max_correction_steps=10):
1570           """           """
1571           returns a True if iteration is terminated. (overwrite)           Solves the saddle point problem using initial guesses v and p.
1572    
1573           @rtype: C{bool}           @param v: initial guess for velocity
1574             @param p: initial guess for pressure
1575             @type v: L{Data}
1576             @type p: L{Data}
1577             @param usePCG: indicates the usage of the PCG rather than GMRES scheme.
1578             @param max_iter: maximum number of iteration steps per correction
1579                              attempt
1580             @param verbose: if True, shows information on the progress of the
1581                             saddlepoint problem solver.
1582             @param show_details: if True, shows details of the sub problem solver
1583             @param iter_restart: restart the iteration after C{iter_restart} steps
1584                                  (only used if useUzaw=False)
1585             @type usePCG: C{bool}
1586             @type max_iter: C{int}
1587             @type verbose: C{bool}
1588             @type show_details: C{bool}
1589             @type iter_restart: C{int}
1590             @rtype: C{tuple} of L{Data} objects
1591           """           """
1592           pass           self.verbose=verbose
1593                         self.show_details=show_details and self.verbose
1594        def __inner(self,p,r):           rtol=self.getTolerance()
1595           return self.inner(p,r[1])           atol=self.getAbsoluteTolerance()
1596             correction_step=0
1597             converged=False
1598             while not converged:
1599                  # calculate velocity for current pressure:
1600                  v=self.getV(p,v)
1601                  #
1602                  norm_v=self.norm_v(v)
1603                  norm_Bv=self.norm_Bv(v)
1604                  ATOL=norm_v*rtol+atol
1605                  if self.verbose: print "saddle point solver: norm v= %e, norm_Bv= %e, tolerance = %e."%(norm_v, norm_Bv,ATOL)
1606                  if not ATOL>0: raise ValueError,"overall absolute tolerance needs to be positive."
1607                  if norm_Bv <= ATOL:
1608                     converged=True
1609                  else:
1610                     correction_step+=1
1611                     if correction_step>max_correction_steps:
1612                          raise CorrectionFailed,"Given up after %d correction steps."%correction_step
1613                     dp=self.solve_precB(v)
1614                     if usePCG:
1615                       norm2=self.inner_pBv(dp,v)
1616                       if norm2<0: raise ValueError,"negative PCG norm."
1617                       norm2=math.sqrt(norm2)
1618                     else:
1619                       norm2=self.norm_p(dp)
1620                     ATOL_ITER=ATOL/norm_Bv*norm2
1621                     if self.verbose: print "saddle point solver: tolerance for solver: %e"%ATOL_ITER
1622                     if usePCG:
1623                           p,v0=PCG(v,self.__Aprod_PCG,p,self.__Msolve_PCG,self.__inner_PCG,atol=ATOL_ITER, rtol=0.,iter_max=max_iter, verbose=self.verbose)
1624                     else:
1625                           p=GMRES(dp,self.__Aprod_GMRES, p, self.__inner_GMRES,atol=ATOL_ITER, rtol=0.,iter_max=max_iter, iter_restart=iter_restart, verbose=self.verbose)
1626             if self.verbose: print "saddle point solver: tolerance reached."
1627         return v,p
1628    
1629        def __inner_p(self,p1,p2):        #========================================================================
1630           return self.inner(p1,p2)        def setTolerance(self,tolerance=1.e-4):
1631                   """
1632        def __inner_a(self,a1,a2):           Sets the relative tolerance for (v,p).
          return self.inner_a(a1,a2)  
1633    
1634        def __inner_a1(self,a1,a2):           @param tolerance: tolerance to be used
1635           return self.inner(a1[1],a2[1])           @type tolerance: non-negative C{float}
1636             """
1637             if tolerance<0:
1638                 raise ValueError,"tolerance must be positive."
1639             self.__rtol=tolerance
1640             self.setSubProblemTolerance()
1641    
1642        def __stoppingcriterium(self,norm_r,r,p):        def getTolerance(self):
1643            return self.stoppingcriterium(r[1],r[0],p)           """
1644             Returns the relative tolerance.
1645    
1646        def __stoppingcriterium2(self,norm_r,norm_b,solver='GMRES',TOL=None):           @return: relative tolerance
1647            return self.stoppingcriterium2(norm_r,norm_b,solver,TOL)           @rtype: C{float}
1648             """
1649             return self.__rtol
1650    
1651        def setTolerance(self,tolerance=1.e-8):        def setAbsoluteTolerance(self,tolerance=0.):
1652                self.__tol=tolerance           """
1653        def getTolerance(self):           Sets the absolute tolerance.
               return self.__tol  
       def setToleranceReductionFactor(self,reduction=0.01):  
               self.__reduction=reduction  
       def getSubProblemTolerance(self):  
               return self.__reduction*self.getTolerance()  
1654    
1655        def solve(self,v,p,max_iter=20, verbose=False, show_details=False, solver='PCG',iter_restart=20):           @param tolerance: tolerance to be used
1656                """           @type tolerance: non-negative C{float}
1657                solves the saddle point problem using initial guesses v and p.           """
1658             if tolerance<0:
1659                @param max_iter: maximum number of iteration steps.               raise ValueError,"tolerance must be non-negative."
1660                """           self.__atol=tolerance
               self.verbose=verbose  
               self.show_details=show_details and self.verbose  
   
               # assume p is known: then v=A^-1(f-B^*p)  
               # which leads to BA^-1B^*p = BA^-1f    
   
           # Az=f is solved as A(z-v)=f-Av (z-v = 0 on fixed_u_mask)        
           self.__z=v+self.solve_A(v,p*0)  
               Bz=self.B(self.__z)  
               #  
           #   solve BA^-1B^*p = Bz  
               #  
               #  
               #  
               self.iter=0  
           if solver=='GMRES':        
                 if self.verbose: print "enter GMRES method (iter_max=%s)"%max_iter  
                 p=GMRES(Bz,self.__Aprod2,self.__Msolve2,self.__inner_p,self.__stoppingcriterium2,iter_max=max_iter, x=p*1.,iter_restart=iter_restart)  
                 # solve Au=f-B^*p  
                 #       A(u-v)=f-B^*p-Av  
                 #       u=v+(u-v)  
         u=v+self.solve_A(v,p)  
   
           if solver=='TFQMR':        
                 if self.verbose: print "enter TFQMR method (iter_max=%s)"%max_iter  
                 p=TFQMR(Bz,self.__Aprod2,self.__Msolve2,self.__inner_p,self.__stoppingcriterium2,iter_max=max_iter, x=p*1.)  
                 # solve Au=f-B^*p  
                 #       A(u-v)=f-B^*p-Av  
                 #       u=v+(u-v)  
         u=v+self.solve_A(v,p)  
   
           if solver=='MINRES':        
                 if self.verbose: print "enter MINRES method (iter_max=%s)"%max_iter  
                 p=MINRES(Bz,self.__Aprod2,self.__Msolve2,self.__inner_p,self.__stoppingcriterium2,iter_max=max_iter, x=p*1.)  
                 # solve Au=f-B^*p  
                 #       A(u-v)=f-B^*p-Av  
                 #       u=v+(u-v)  
         u=v+self.solve_A(v,p)  
                 
           if solver=='GMRESC':        
                 if self.verbose: print "enter GMRES coupled method (iter_max=%s)"%max_iter  
                 p0=self.solve_prec1(Bz)  
             #alfa=(1/self.vol)*util.integrate(util.interpolate(p,escript.Function(self.domain)))  
                 #p-=alfa  
                 x=GMRES(ArithmeticTuple(self.__z*1.,p0*1),self.__Anext,self.__Mempty,self.__inner_a,self.__stoppingcriterium2,iter_max=max_iter, x=ArithmeticTuple(v*1,p*1),iter_restart=20)  
                 #x=NewtonGMRES(ArithmeticTuple(self.__z*1.,p0*1),self.__Aprod_Newton2,self.__Mempty,self.__inner_a,self.__stoppingcriterium2,iter_max=max_iter, x=ArithmeticTuple(v*1,p*1),atol=0,rtol=self.getTolerance())  
   
                 # solve Au=f-B^*p  
                 #       A(u-v)=f-B^*p-Av  
                 #       u=v+(u-v)  
             p=x[1]  
         u=v+self.solve_A(v,p)        
         #p=x[1]  
         #u=x[0]  
   
               if solver=='PCG':  
                 #   note that the residual r=Bz-BA^-1B^*p = B(z-A^-1B^*p) = Bv  
                 #  
                 #   with                    Av=Az-B^*p = f - B^*p (v=z on fixed_u_mask)  
                 #                           A(v-z)= f -Az - B^*p (v-z=0 on fixed_u_mask)  
                 if self.verbose: print "enter PCG method (iter_max=%s)"%max_iter  
                 p,r=PCG(ArithmeticTuple(self.__z*1.,Bz),self.__Aprod,self.__Msolve,self.__inner,self.__stoppingcriterium,iter_max=max_iter, x=p)  
             u=r[0]    
                 # print "DIFF=",util.integrate(p)  
   
               # print "RESULT div(u)=",util.Lsup(self.B(u)),util.Lsup(u)  
   
           return u,p  
   
       def __Msolve(self,r):  
           return self.solve_prec(r[1])  
   
       def __Msolve2(self,r):  
           return self.solve_prec(r*1)  
   
       def __Mempty(self,r):  
           return r  
   
   
       def __Aprod(self,p):  
           # return BA^-1B*p  
           #solve Av =B^*p as Av =f-Az-B^*(-p)  
           v=self.solve_A(self.__z,-p)  
           return ArithmeticTuple(v, self.B(v))  
   
       def __Anext(self,x):  
           # return next v,p  
           #solve Av =-B^*p as Av =f-Az-B^*p  
   
       pc=x[1]  
           v=self.solve_A(self.__z,-pc)  
       p=self.solve_prec1(self.B(v))  
   
           return ArithmeticTuple(v,p)  
   
   
       def __Aprod2(self,p):  
           # return BA^-1B*p  
           #solve Av =B^*p as Av =f-Az-B^*(-p)  
       v=self.solve_A(self.__z,-p)  
           return self.B(v)  
   
       def __Aprod_Newton(self,p):  
           # return BA^-1B*p - Bz  
           #solve Av =-B^*p as Av =f-Az-B^*p  
       v=self.solve_A(self.__z,-p)  
           return self.B(v-self.__z)  
   
       def __Aprod_Newton2(self,x):  
           # return BA^-1B*p - Bz  
           #solve Av =-B^*p as Av =f-Az-B^*p  
           pc=x[1]  
       v=self.solve_A(self.__z,-pc)  
           p=self.solve_prec1(self.B(v-self.__z))  
           return ArithmeticTuple(v,p)  
1661    
1662          def getAbsoluteTolerance(self):
1663             """
1664             Returns the absolute tolerance.
1665    
1666             @return: absolute tolerance
1667             @rtype: C{float}
1668             """
1669             return self.__atol
1670    
1671          def setSubProblemTolerance(self,rtol=None):
1672             """
1673             Sets the relative tolerance to solve the subproblem(s).
1674    
1675             @param rtol: relative tolerence
1676             @type rtol: positive C{float}
1677             """
1678             if rtol == None:
1679                  rtol=max(200.*util.EPSILON,self.getTolerance()**2)
1680             if rtol<=0:
1681                 raise ValueError,"tolerance must be positive."
1682             self.__sub_tol=rtol
1683    
1684          def getSubProblemTolerance(self):
1685             """
1686             Returns the subproblem reduction factor.
1687    
1688             @return: subproblem reduction factor
1689             @rtype: C{float}
1690             """
1691             return self.__sub_tol
1692    
1693  def MaskFromBoundaryTag(domain,*tags):  def MaskFromBoundaryTag(domain,*tags):
1694     """     """
1695     creates a mask on the Solution(domain) function space which one for samples     Creates a mask on the Solution(domain) function space where the value is
1696     that touch the boundary tagged by tags.     one for samples that touch the boundary tagged by tags.
1697    
1698     usage: m=MaskFromBoundaryTag(domain,"left", "right")     Usage: m=MaskFromBoundaryTag(domain, "left", "right")
1699    
1700     @param domain: a given domain     @param domain: domain to be used
1701     @type domain: L{escript.Domain}     @type domain: L{escript.Domain}
1702     @param tags: boundray tags     @param tags: boundary tags
1703     @type tags: C{str}     @type tags: C{str}
1704     @return: a mask which marks samples that are touching the boundary tagged by any of the given tags.     @return: a mask which marks samples that are touching the boundary tagged
1705                by any of the given tags
1706     @rtype: L{escript.Data} of rank 0     @rtype: L{escript.Data} of rank 0
1707     """     """
1708     pde=linearPDEs.LinearPDE(domain,numEquations=1, numSolutions=1)     pde=linearPDEs.LinearPDE(domain,numEquations=1, numSolutions=1)
# Line 1621  def MaskFromBoundaryTag(domain,*tags): Line 1710  def MaskFromBoundaryTag(domain,*tags):
1710     for t in tags: d.setTaggedValue(t,1.)     for t in tags: d.setTaggedValue(t,1.)
1711     pde.setValue(y=d)     pde.setValue(y=d)
1712     return util.whereNonZero(pde.getRightHandSide())     return util.whereNonZero(pde.getRightHandSide())
1713  #============================================================================================================================================  
1714    #==============================================================================
1715  class SaddlePointProblem(object):  class SaddlePointProblem(object):
1716     """     """
1717     This implements a solver for a saddlepoint problem     This implements a solver for a saddle point problem
1718    
1719     M{f(u,p)=0}     M{f(u,p)=0}
1720     M{g(u)=0}     M{g(u)=0}
# Line 1634  class SaddlePointProblem(object): Line 1724  class SaddlePointProblem(object):
1724     M{Q_f (u^{k+1}-u^{k}) = - f(u^{k},p^{k})}     M{Q_f (u^{k+1}-u^{k}) = - f(u^{k},p^{k})}
1725     M{Q_g (p^{k+1}-p^{k}) =   g(u^{k+1})}     M{Q_g (p^{k+1}-p^{k}) =   g(u^{k+1})}
1726    
1727     where Q_f is an approximation of the Jacobiean A_f of f with respect to u  and Q_f is an approximation of     where Q_f is an approximation of the Jacobian A_f of f with respect to u and
1728     A_g A_f^{-1} A_g with A_g is the jacobiean of g with respect to p. As a the construction of a 'proper'     Q_f is an approximation of A_g A_f^{-1} A_g with A_g is the Jacobian of g
1729     Q_g can be difficult, non-linear conjugate gradient method is applied to solve for p, so Q_g plays     with respect to p. As a the construction of a 'proper' Q_g can be difficult,
1730       non-linear conjugate gradient method is applied to solve for p, so Q_g plays
1731     in fact the role of a preconditioner.     in fact the role of a preconditioner.
1732     """     """
1733     def __init__(self,verbose=False,*args):     def __init__(self,verbose=False,*args):
1734         """         """
1735         initializes the problem         Initializes the problem.
1736    
1737         @param verbose: switches on the printing out some information         @param verbose: if True, some information is printed in the process
1738         @type verbose: C{bool}         @type verbose: C{bool}
1739         @note: this method may be overwritten by a particular saddle point problem         @note: this method may be overwritten by a particular saddle point
1740                  problem
1741         """         """
1742         print "SaddlePointProblem should not be used anymore!"         print "SaddlePointProblem should not be used anymore!"
1743         if not isinstance(verbose,bool):         if not isinstance(verbose,bool):
# Line 1656  class SaddlePointProblem(object): Line 1748  class SaddlePointProblem(object):
1748    
1749     def trace(self,text):     def trace(self,text):
1750         """         """
1751         prints text if verbose has been set         Prints C{text} if verbose has been set.
1752    
1753         @param text: a text message         @param text: a text message
1754         @type text: C{str}         @type text: C{str}
# Line 1665  class SaddlePointProblem(object): Line 1757  class SaddlePointProblem(object):
1757    
1758     def solve_f(self,u,p,tol=1.e-8):     def solve_f(self,u,p,tol=1.e-8):
1759         """         """
1760         solves         Solves
1761    
1762         A_f du = f(u,p)         A_f du = f(u,p)
1763    
1764         with tolerance C{tol} and return du. A_f is Jacobiean of f with respect to u.         with tolerance C{tol} and returns du. A_f is Jacobian of f with respect
1765           to u.
1766    
1767         @param u: current approximation of u         @param u: current approximation of u
1768         @type u: L{escript.Data}         @type u: L{escript.Data}
# Line 1679  class SaddlePointProblem(object): Line 1772  class SaddlePointProblem(object):
1772         @type tol: C{float}         @type tol: C{float}
1773         @return: increment du         @return: increment du
1774         @rtype: L{escript.Data}         @rtype: L{escript.Data}
1775         @note: this method has to be overwritten by a particular saddle point problem         @note: this method has to be overwritten by a particular saddle point
1776                  problem
1777         """         """
1778         pass         pass
1779    
1780     def solve_g(self,u,tol=1.e-8):     def solve_g(self,u,tol=1.e-8):
1781         """         """
1782         solves         Solves
1783    
1784         Q_g dp = g(u)         Q_g dp = g(u)
1785    
1786         with Q_g is a preconditioner for A_g A_f^{-1} A_g with  A_g is the jacobiean of g with respect to p.         where Q_g is a preconditioner for A_g A_f^{-1} A_g with A_g is the
1787           Jacobian of g with respect to p.
1788    
1789         @param u: current approximation of u         @param u: current approximation of u
1790         @type u: L{escript.Data}         @type u: L{escript.Data}
# Line 1697  class SaddlePointProblem(object): Line 1792  class SaddlePointProblem(object):
1792         @type tol: C{float}         @type tol: C{float}
1793         @return: increment dp         @return: increment dp
1794         @rtype: L{escript.Data}         @rtype: L{escript.Data}
1795         @note: this method has to be overwritten by a particular saddle point problem         @note: this method has to be overwritten by a particular saddle point
1796                  problem
1797         """         """
1798         pass         pass
1799    
1800     def inner(self,p0,p1):     def inner(self,p0,p1):
1801         """         """
1802         inner product of p0 and p1 approximating p. Typically this returns integrate(p0*p1)         Inner product of p0 and p1 approximating p. Typically this returns
1803           C{integrate(p0*p1)}.
1804         @return: inner product of p0 and p1         @return: inner product of p0 and p1
1805         @rtype: C{float}         @rtype: C{float}
1806         """         """
# Line 1712  class SaddlePointProblem(object): Line 1809  class SaddlePointProblem(object):
1809     subiter_max=3     subiter_max=3
1810     def solve(self,u0,p0,tolerance=1.e-6,tolerance_u=None,iter_max=100,accepted_reduction=0.995,relaxation=None):     def solve(self,u0,p0,tolerance=1.e-6,tolerance_u=None,iter_max=100,accepted_reduction=0.995,relaxation=None):
1811          """          """
1812          runs the solver          Runs the solver.
1813    
1814          @param u0: initial guess for C{u}          @param u0: initial guess for C{u}
1815          @type u0: L{esys.escript.Data}          @type u0: L{esys.escript.Data}
# Line 1720  class SaddlePointProblem(object): Line 1817  class SaddlePointProblem(object):
1817          @type p0: L{esys.escript.Data}          @type p0: L{esys.escript.Data}
1818          @param tolerance: tolerance for relative error in C{u} and C{p}          @param tolerance: tolerance for relative error in C{u} and C{p}
1819          @type tolerance: positive C{float}          @type tolerance: positive C{float}
1820          @param tolerance_u: tolerance for relative error in C{u} if different from C{tolerance}          @param tolerance_u: tolerance for relative error in C{u} if different
1821                                from C{tolerance}
1822          @type tolerance_u: positive C{float}          @type tolerance_u: positive C{float}
1823          @param iter_max: maximum number of iteration steps.          @param iter_max: maximum number of iteration steps
1824          @type iter_max: C{int}          @type iter_max: C{int}
1825          @param accepted_reduction: if the norm  g cannot be reduced by C{accepted_reduction} backtracking to adapt the          @param accepted_reduction: if the norm g cannot be reduced by
1826                                     relaxation factor. If C{accepted_reduction=None} no backtracking is used.                                     C{accepted_reduction} backtracking to adapt
1827                                       the relaxation factor. If
1828                                       C{accepted_reduction=None} no backtracking
1829                                       is used.
1830          @type accepted_reduction: positive C{float} or C{None}          @type accepted_reduction: positive C{float} or C{None}
1831          @param relaxation: initial relaxation factor. If C{relaxation==None}, the last relaxation factor is used.          @param relaxation: initial relaxation factor. If C{relaxation==None},
1832                               the last relaxation factor is used.
1833          @type relaxation: C{float} or C{None}          @type relaxation: C{float} or C{None}
1834          """          """
1835          tol=1.e-2          tol=1.e-2
# Line 1826  class SaddlePointProblem(object): Line 1928  class SaddlePointProblem(object):
1928              f, norm_f, u, norm_u, g, norm_g, p, norm_p = f_new, norm_f_new, u_new, norm_u_new, g_new, norm_g_new, p_new, norm_p_new              f, norm_f, u, norm_u, g, norm_g, p, norm_p = f_new, norm_f_new, u_new, norm_u_new, g_new, norm_g_new, p_new, norm_p_new
1929          self.trace("convergence after %s steps."%self.iter)          self.trace("convergence after %s steps."%self.iter)
1930          return u,p          return u,p
1931    

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