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/******************************************************* |
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* |
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* Copyright (c) 2003-2009 by University of Queensland |
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* Earth Systems Science Computational Center (ESSCC) |
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* http://www.uq.edu.au/esscc |
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* |
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* Primary Business: Queensland, Australia |
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* Licensed under the Open Software License version 3.0 |
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* http://www.opensource.org/licenses/osl-3.0.php |
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* |
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*******************************************************/ |
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|
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|
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#if !defined escript_LocalOps_H |
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#define escript_LocalOps_H |
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#if defined(_WIN32) && defined(__INTEL_COMPILER) |
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# include <mathimf.h> |
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#else |
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# include <math.h> |
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#endif |
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#ifndef M_PI |
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# define M_PI 3.14159265358979323846 /* pi */ |
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#endif |
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|
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|
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/** |
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\file LocalOps.h |
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\brief Describes binary operations performed on double*. |
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|
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For operations on DataAbstract see BinaryOp.h. |
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For operations on DataVector see DataMaths.h. |
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*/ |
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|
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namespace escript { |
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|
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/** |
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\brief acts as a wrapper to isnan. |
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\warning if compiler does not support FP_NAN this function will always return false. |
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*/ |
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inline |
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bool nancheck(double d) |
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{ |
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#ifndef isnan // Q: so why not just test d!=d? |
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return false; // A: Coz it doesn't always work [I've checked]. |
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#else // One theory is that the optimizer skips the test. |
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return isnan(d); |
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#endif |
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} |
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|
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/** |
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\brief returns a NaN. |
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\warning Should probably only used where you know you can test for NaNs |
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*/ |
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inline |
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double makeNaN() |
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{ |
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#ifdef isnan |
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return nan(""); |
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#else |
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return sqrt(-1); |
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#endif |
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|
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} |
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|
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|
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/** |
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\brief |
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solves a 1x1 eigenvalue A*V=ev*V problem |
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|
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\param A00 Input - A_00 |
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\param ev0 Output - eigenvalue |
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*/ |
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inline |
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void eigenvalues1(const double A00,double* ev0) { |
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|
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*ev0=A00; |
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|
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} |
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/** |
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\brief |
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solves a 2x2 eigenvalue A*V=ev*V problem for symmetric A |
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|
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\param A00 Input - A_00 |
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\param A01 Input - A_01 |
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\param A11 Input - A_11 |
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\param ev0 Output - smallest eigenvalue |
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\param ev1 Output - largest eigenvalue |
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*/ |
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inline |
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void eigenvalues2(const double A00,const double A01,const double A11, |
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double* ev0, double* ev1) { |
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const register double trA=(A00+A11)/2.; |
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const register double A_00=A00-trA; |
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const register double A_11=A11-trA; |
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const register double s=sqrt(A01*A01-A_00*A_11); |
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*ev0=trA-s; |
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*ev1=trA+s; |
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} |
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/** |
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\brief |
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solves a 3x3 eigenvalue A*V=ev*V problem for symmetric A |
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|
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\param A00 Input - A_00 |
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\param A01 Input - A_01 |
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\param A02 Input - A_02 |
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\param A11 Input - A_11 |
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\param A12 Input - A_12 |
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\param A22 Input - A_22 |
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\param ev0 Output - smallest eigenvalue |
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\param ev1 Output - eigenvalue |
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\param ev2 Output - largest eigenvalue |
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*/ |
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inline |
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void eigenvalues3(const double A00, const double A01, const double A02, |
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const double A11, const double A12, |
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const double A22, |
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double* ev0, double* ev1,double* ev2) { |
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|
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const register double trA=(A00+A11+A22)/3.; |
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const register double A_00=A00-trA; |
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const register double A_11=A11-trA; |
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const register double A_22=A22-trA; |
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const register double A01_2=A01*A01; |
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const register double A02_2=A02*A02; |
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const register double A12_2=A12*A12; |
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const register double p=A02_2+A12_2+A01_2+(A_00*A_00+A_11*A_11+A_22*A_22)/2.; |
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if (p<=0.) { |
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*ev2=trA; |
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*ev1=trA; |
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*ev0=trA; |
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|
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} else { |
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const register double q=(A02_2*A_11+A12_2*A_00+A01_2*A_22)-(A_00*A_11*A_22+2*A01*A12*A02); |
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const register double sq_p=sqrt(p/3.); |
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register double z=-q/(2*pow(sq_p,3)); |
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if (z<-1.) { |
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z=-1.; |
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} else if (z>1.) { |
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z=1.; |
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} |
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const register double alpha_3=acos(z)/3.; |
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*ev2=trA+2.*sq_p*cos(alpha_3); |
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*ev1=trA-2.*sq_p*cos(alpha_3+M_PI/3.); |
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*ev0=trA-2.*sq_p*cos(alpha_3-M_PI/3.); |
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} |
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} |
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/** |
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\brief |
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solves a 1x1 eigenvalue A*V=ev*V problem for symmetric A |
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|
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\param A00 Input - A_00 |
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\param ev0 Output - eigenvalue |
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\param V00 Output - eigenvector |
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\param tol Input - tolerance to identify to eigenvalues |
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*/ |
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inline |
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void eigenvalues_and_eigenvectors1(const double A00,double* ev0,double* V00,const double tol) |
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{ |
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eigenvalues1(A00,ev0); |
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*V00=1.; |
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return; |
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} |
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/** |
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\brief |
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returns a non-zero vector in the kernel of [[A00,A01],[A01,A11]] assuming that the kernel dimension is at least 1. |
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|
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\param A00 Input - matrix component |
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\param A10 Input - matrix component |
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\param A01 Input - matrix component |
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\param A11 Input - matrix component |
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\param V0 Output - vector component |
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\param V1 Output - vector component |
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*/ |
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inline |
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void vectorInKernel2(const double A00,const double A10,const double A01,const double A11, |
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double* V0, double*V1) |
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{ |
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register double absA00=fabs(A00); |
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register double absA10=fabs(A10); |
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register double absA01=fabs(A01); |
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register double absA11=fabs(A11); |
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register double m=absA11>absA10 ? absA11 : absA10; |
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if (absA00>m || absA01>m) { |
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*V0=-A01; |
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*V1=A00; |
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} else { |
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if (m<=0) { |
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*V0=1.; |
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*V1=0.; |
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} else { |
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*V0=A11; |
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*V1=-A10; |
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} |
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} |
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} |
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/** |
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\brief |
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returns a non-zero vector in the kernel of [[A00,A01,A02],[A10,A11,A12],[A20,A21,A22]] |
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assuming that the kernel dimension is at least 1 and A00 is non zero. |
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|
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\param A00 Input - matrix component |
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\param A10 Input - matrix component |
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\param A20 Input - matrix component |
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\param A01 Input - matrix component |
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\param A11 Input - matrix component |
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\param A21 Input - matrix component |
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\param A02 Input - matrix component |
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\param A12 Input - matrix component |
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\param A22 Input - matrix component |
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\param V0 Output - vector component |
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\param V1 Output - vector component |
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\param V2 Output - vector component |
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*/ |
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inline |
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void vectorInKernel3__nonZeroA00(const double A00,const double A10,const double A20, |
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const double A01,const double A11,const double A21, |
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const double A02,const double A12,const double A22, |
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double* V0,double* V1,double* V2) |
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{ |
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double TEMP0,TEMP1; |
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register const double I00=1./A00; |
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register const double IA10=I00*A10; |
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register const double IA20=I00*A20; |
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vectorInKernel2(A11-IA10*A01,A12-IA10*A02, |
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A21-IA20*A01,A22-IA20*A02,&TEMP0,&TEMP1); |
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*V0=-(A10*TEMP0+A20*TEMP1); |
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*V1=A00*TEMP0; |
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*V2=A00*TEMP1; |
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} |
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|
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/** |
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\brief |
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solves a 2x2 eigenvalue A*V=ev*V problem for symmetric A. Eigenvectors are |
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ordered by increasing value and eigen vectors are normalizeVector3d such that |
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length is zero and first non-zero component is positive. |
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|
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\param A00 Input - A_00 |
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\param A01 Input - A_01 |
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\param A11 Input - A_11 |
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\param ev0 Output - smallest eigenvalue |
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\param ev1 Output - eigenvalue |
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\param V00 Output - eigenvector componenent coresponding to ev0 |
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\param V10 Output - eigenvector componenent coresponding to ev0 |
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\param V01 Output - eigenvector componenent coresponding to ev1 |
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\param V11 Output - eigenvector componenent coresponding to ev1 |
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\param tol Input - tolerance to identify to eigenvalues |
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*/ |
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inline |
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void eigenvalues_and_eigenvectors2(const double A00,const double A01,const double A11, |
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double* ev0, double* ev1, |
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double* V00, double* V10, double* V01, double* V11, |
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const double tol) |
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{ |
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double TEMP0,TEMP1; |
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eigenvalues2(A00,A01,A11,ev0,ev1); |
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const register double absev0=fabs(*ev0); |
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const register double absev1=fabs(*ev1); |
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register double max_ev=absev0>absev1 ? absev0 : absev1; |
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if (fabs((*ev0)-(*ev1))<tol*max_ev) { |
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*V00=1.; |
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*V10=0.; |
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*V01=0.; |
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*V11=1.; |
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} else { |
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vectorInKernel2(A00-(*ev0),A01,A01,A11-(*ev0),&TEMP0,&TEMP1); |
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const register double scale=1./sqrt(TEMP0*TEMP0+TEMP1*TEMP1); |
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if (TEMP0<0.) { |
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*V00=-TEMP0*scale; |
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*V10=-TEMP1*scale; |
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if (TEMP1<0.) { |
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*V01= *V10; |
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*V11=-(*V00); |
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} else { |
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*V01=-(*V10); |
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*V11= (*V10); |
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} |
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} else if (TEMP0>0.) { |
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*V00=TEMP0*scale; |
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*V10=TEMP1*scale; |
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if (TEMP1<0.) { |
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*V01=-(*V10); |
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*V11= (*V00); |
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} else { |
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*V01= (*V10); |
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*V11=-(*V00); |
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} |
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} else { |
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*V00=0.; |
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*V10=1; |
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*V11=0.; |
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*V01=1.; |
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} |
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} |
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} |
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/** |
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\brief |
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nomalizes a 3-d vector such that length is one and first non-zero component is positive. |
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|
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\param V0 - vector componenent |
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\param V1 - vector componenent |
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\param V2 - vector componenent |
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*/ |
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inline |
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void normalizeVector3(double* V0,double* V1,double* V2) |
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{ |
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register double s; |
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if (*V0>0) { |
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s=1./sqrt((*V0)*(*V0)+(*V1)*(*V1)+(*V2)*(*V2)); |
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*V0*=s; |
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*V1*=s; |
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*V2*=s; |
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} else if (*V0<0) { |
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s=-1./sqrt((*V0)*(*V0)+(*V1)*(*V1)+(*V2)*(*V2)); |
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*V0*=s; |
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*V1*=s; |
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*V2*=s; |
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} else { |
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if (*V1>0) { |
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s=1./sqrt((*V1)*(*V1)+(*V2)*(*V2)); |
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*V1*=s; |
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*V2*=s; |
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} else if (*V1<0) { |
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s=-1./sqrt((*V1)*(*V1)+(*V2)*(*V2)); |
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*V1*=s; |
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*V2*=s; |
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} else { |
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*V2=1.; |
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} |
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} |
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} |
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/** |
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\brief |
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solves a 2x2 eigenvalue A*V=ev*V problem for symmetric A. Eigenvectors are |
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ordered by increasing value and eigen vectors are normalizeVector3d such that |
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length is zero and first non-zero component is positive. |
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|
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\param A00 Input - A_00 |
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\param A01 Input - A_01 |
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\param A02 Input - A_02 |
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\param A11 Input - A_11 |
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\param A12 Input - A_12 |
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\param A22 Input - A_22 |
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\param ev0 Output - smallest eigenvalue |
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\param ev1 Output - eigenvalue |
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\param ev2 Output - |
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\param V00 Output - eigenvector componenent coresponding to ev0 |
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\param V10 Output - eigenvector componenent coresponding to ev0 |
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\param V20 Output - |
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\param V01 Output - eigenvector componenent coresponding to ev1 |
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\param V11 Output - eigenvector componenent coresponding to ev1 |
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\param V21 Output - |
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\param V02 Output - |
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\param V12 Output - |
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\param V22 Output - |
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\param tol Input - tolerance to identify to eigenvalues |
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*/ |
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inline |
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void eigenvalues_and_eigenvectors3(const double A00, const double A01, const double A02, |
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const double A11, const double A12, const double A22, |
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double* ev0, double* ev1, double* ev2, |
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double* V00, double* V10, double* V20, |
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double* V01, double* V11, double* V21, |
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double* V02, double* V12, double* V22, |
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const double tol) |
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{ |
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register const double absA01=fabs(A01); |
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register const double absA02=fabs(A02); |
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register const double m=absA01>absA02 ? absA01 : absA02; |
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if (m<=0) { |
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double TEMP_V00,TEMP_V10,TEMP_V01,TEMP_V11,TEMP_EV0,TEMP_EV1; |
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eigenvalues_and_eigenvectors2(A11,A12,A22, |
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&TEMP_EV0,&TEMP_EV1, |
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&TEMP_V00,&TEMP_V10,&TEMP_V01,&TEMP_V11,tol); |
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if (A00<=TEMP_EV0) { |
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*V00=1.; |
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*V10=0.; |
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*V20=0.; |
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*V01=0.; |
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*V11=TEMP_V00; |
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*V21=TEMP_V10; |
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*V02=0.; |
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*V12=TEMP_V01; |
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*V22=TEMP_V11; |
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*ev0=A00; |
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*ev1=TEMP_EV0; |
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*ev2=TEMP_EV1; |
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} else if (A00>TEMP_EV1) { |
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*V02=1.; |
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*V12=0.; |
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*V22=0.; |
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*V00=0.; |
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*V10=TEMP_V00; |
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*V20=TEMP_V10; |
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*V01=0.; |
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*V11=TEMP_V01; |
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*V21=TEMP_V11; |
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*ev0=TEMP_EV0; |
| 399 |
*ev1=TEMP_EV1; |
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*ev2=A00; |
| 401 |
} else { |
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*V01=1.; |
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*V11=0.; |
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*V21=0.; |
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*V00=0.; |
| 406 |
*V10=TEMP_V00; |
| 407 |
*V20=TEMP_V10; |
| 408 |
*V02=0.; |
| 409 |
*V12=TEMP_V01; |
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*V22=TEMP_V11; |
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*ev0=TEMP_EV0; |
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*ev1=A00; |
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*ev2=TEMP_EV1; |
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} |
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} else { |
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eigenvalues3(A00,A01,A02,A11,A12,A22,ev0,ev1,ev2); |
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const register double absev0=fabs(*ev0); |
| 418 |
const register double absev1=fabs(*ev1); |
| 419 |
const register double absev2=fabs(*ev2); |
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register double max_ev=absev0>absev1 ? absev0 : absev1; |
| 421 |
max_ev=max_ev>absev2 ? max_ev : absev2; |
| 422 |
const register double d_01=fabs((*ev0)-(*ev1)); |
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const register double d_12=fabs((*ev1)-(*ev2)); |
| 424 |
const register double max_d=d_01>d_12 ? d_01 : d_12; |
| 425 |
if (max_d<=tol*max_ev) { |
| 426 |
*V00=1.; |
| 427 |
*V10=0; |
| 428 |
*V20=0; |
| 429 |
*V01=0; |
| 430 |
*V11=1.; |
| 431 |
*V21=0; |
| 432 |
*V02=0; |
| 433 |
*V12=0; |
| 434 |
*V22=1.; |
| 435 |
} else { |
| 436 |
const register double S00=A00-(*ev0); |
| 437 |
const register double absS00=fabs(S00); |
| 438 |
if (absS00>m) { |
| 439 |
vectorInKernel3__nonZeroA00(S00,A01,A02,A01,A11-(*ev0),A12,A02,A12,A22-(*ev0),V00,V10,V20); |
| 440 |
} else if (absA02<m) { |
| 441 |
vectorInKernel3__nonZeroA00(A01,A11-(*ev0),A12,S00,A01,A02,A02,A12,A22-(*ev0),V00,V10,V20); |
| 442 |
} else { |
| 443 |
vectorInKernel3__nonZeroA00(A02,A12,A22-(*ev0),S00,A01,A02,A01,A11-(*ev0),A12,V00,V10,V20); |
| 444 |
} |
| 445 |
normalizeVector3(V00,V10,V20);; |
| 446 |
const register double T00=A00-(*ev2); |
| 447 |
const register double absT00=fabs(T00); |
| 448 |
if (absT00>m) { |
| 449 |
vectorInKernel3__nonZeroA00(T00,A01,A02,A01,A11-(*ev2),A12,A02,A12,A22-(*ev2),V02,V12,V22); |
| 450 |
} else if (absA02<m) { |
| 451 |
vectorInKernel3__nonZeroA00(A01,A11-(*ev2),A12,T00,A01,A02,A02,A12,A22-(*ev2),V02,V12,V22); |
| 452 |
} else { |
| 453 |
vectorInKernel3__nonZeroA00(A02,A12,A22-(*ev2),T00,A01,A02,A01,A11-(*ev2),A12,V02,V12,V22); |
| 454 |
} |
| 455 |
const register double dot=(*V02)*(*V00)+(*V12)*(*V10)+(*V22)*(*V20); |
| 456 |
*V02-=dot*(*V00); |
| 457 |
*V12-=dot*(*V10); |
| 458 |
*V22-=dot*(*V20); |
| 459 |
normalizeVector3(V02,V12,V22); |
| 460 |
*V01=(*V10)*(*V22)-(*V12)*(*V20); |
| 461 |
*V11=(*V20)*(*V02)-(*V00)*(*V22); |
| 462 |
*V21=(*V00)*(*V12)-(*V02)*(*V10); |
| 463 |
normalizeVector3(V01,V11,V21); |
| 464 |
} |
| 465 |
} |
| 466 |
} |
| 467 |
|
| 468 |
// General tensor product: arg_2(SL x SR) = arg_0(SL x SM) * arg_1(SM x SR) |
| 469 |
// SM is the product of the last axis_offset entries in arg_0.getShape(). |
| 470 |
inline |
| 471 |
void matrix_matrix_product(const int SL, const int SM, const int SR, const double* A, const double* B, double* C, int transpose) |
| 472 |
{ |
| 473 |
if (transpose == 0) { |
| 474 |
for (int i=0; i<SL; i++) { |
| 475 |
for (int j=0; j<SR; j++) { |
| 476 |
double sum = 0.0; |
| 477 |
for (int l=0; l<SM; l++) { |
| 478 |
sum += A[i+SL*l] * B[l+SM*j]; |
| 479 |
} |
| 480 |
C[i+SL*j] = sum; |
| 481 |
} |
| 482 |
} |
| 483 |
} |
| 484 |
else if (transpose == 1) { |
| 485 |
for (int i=0; i<SL; i++) { |
| 486 |
for (int j=0; j<SR; j++) { |
| 487 |
double sum = 0.0; |
| 488 |
for (int l=0; l<SM; l++) { |
| 489 |
sum += A[i*SM+l] * B[l+SM*j]; |
| 490 |
} |
| 491 |
C[i+SL*j] = sum; |
| 492 |
} |
| 493 |
} |
| 494 |
} |
| 495 |
else if (transpose == 2) { |
| 496 |
for (int i=0; i<SL; i++) { |
| 497 |
for (int j=0; j<SR; j++) { |
| 498 |
double sum = 0.0; |
| 499 |
for (int l=0; l<SM; l++) { |
| 500 |
sum += A[i+SL*l] * B[l*SR+j]; |
| 501 |
} |
| 502 |
C[i+SL*j] = sum; |
| 503 |
} |
| 504 |
} |
| 505 |
} |
| 506 |
} |
| 507 |
|
| 508 |
template <typename UnaryFunction> |
| 509 |
inline void tensor_unary_operation(const int size, |
| 510 |
const double *arg1, |
| 511 |
double * argRes, |
| 512 |
UnaryFunction operation) |
| 513 |
{ |
| 514 |
for (int i = 0; i < size; ++i) { |
| 515 |
argRes[i] = operation(arg1[i]); |
| 516 |
} |
| 517 |
return; |
| 518 |
} |
| 519 |
|
| 520 |
template <typename BinaryFunction> |
| 521 |
inline void tensor_binary_operation(const int size, |
| 522 |
const double *arg1, |
| 523 |
const double *arg2, |
| 524 |
double * argRes, |
| 525 |
BinaryFunction operation) |
| 526 |
{ |
| 527 |
for (int i = 0; i < size; ++i) { |
| 528 |
argRes[i] = operation(arg1[i], arg2[i]); |
| 529 |
} |
| 530 |
return; |
| 531 |
} |
| 532 |
|
| 533 |
template <typename BinaryFunction> |
| 534 |
inline void tensor_binary_operation(const int size, |
| 535 |
double arg1, |
| 536 |
const double *arg2, |
| 537 |
double *argRes, |
| 538 |
BinaryFunction operation) |
| 539 |
{ |
| 540 |
for (int i = 0; i < size; ++i) { |
| 541 |
argRes[i] = operation(arg1, arg2[i]); |
| 542 |
} |
| 543 |
return; |
| 544 |
} |
| 545 |
|
| 546 |
template <typename BinaryFunction> |
| 547 |
inline void tensor_binary_operation(const int size, |
| 548 |
const double *arg1, |
| 549 |
double arg2, |
| 550 |
double *argRes, |
| 551 |
BinaryFunction operation) |
| 552 |
{ |
| 553 |
for (int i = 0; i < size; ++i) { |
| 554 |
argRes[i] = operation(arg1[i], arg2); |
| 555 |
} |
| 556 |
return; |
| 557 |
} |
| 558 |
|
| 559 |
} // end of namespace |
| 560 |
#endif |
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