1c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// This file is part of Eigen, a lightweight C++ template library
2c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// for linear algebra.
3c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath//
4c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
5c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath//
6c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// This Source Code Form is subject to the terms of the Mozilla
7c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// Public License v. 2.0. If a copy of the MPL was not distributed
8c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
9c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
10c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath#include "sparse.h"
11c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
12c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename Scalar> void
13c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan KamathinitSPD(double density,
14c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath        Matrix<Scalar,Dynamic,Dynamic>& refMat,
15c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath        SparseMatrix<Scalar>& sparseMat)
16c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{
17c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  Matrix<Scalar,Dynamic,Dynamic> aux(refMat.rows(),refMat.cols());
18c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  initSparse(density,refMat,sparseMat);
19c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  refMat = refMat * refMat.adjoint();
20c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  for (int k=0; k<2; ++k)
21c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  {
22c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    initSparse(density,aux,sparseMat,ForceNonZeroDiag);
23c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    refMat += aux * aux.adjoint();
24c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  }
25c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  sparseMat.setZero();
26c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  for (int j=0 ; j<sparseMat.cols(); ++j)
27c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    for (int i=j ; i<sparseMat.rows(); ++i)
28c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      if (refMat(i,j)!=Scalar(0))
29c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath        sparseMat.insert(i,j) = refMat(i,j);
30c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  sparseMat.finalize();
31c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath}
32c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
33c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename Scalar> void sparse_solvers(int rows, int cols)
34c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{
35c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  double density = (std::max)(8./(rows*cols), 0.01);
36c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix;
37c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  typedef Matrix<Scalar,Dynamic,1> DenseVector;
38c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  // Scalar eps = 1e-6;
39c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
40c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  DenseVector vec1 = DenseVector::Random(rows);
41c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
42c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  std::vector<Vector2i> zeroCoords;
43c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  std::vector<Vector2i> nonzeroCoords;
44c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
45c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  // test triangular solver
46c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  {
47c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    DenseVector vec2 = vec1, vec3 = vec1;
48c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    SparseMatrix<Scalar> m2(rows, cols);
49c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    DenseMatrix refMat2 = DenseMatrix::Zero(rows, cols);
50c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
51c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    // lower - dense
52c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    initSparse<Scalar>(density, refMat2, m2, ForceNonZeroDiag|MakeLowerTriangular, &zeroCoords, &nonzeroCoords);
53c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    VERIFY_IS_APPROX(refMat2.template triangularView<Lower>().solve(vec2),
54c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath                     m2.template triangularView<Lower>().solve(vec3));
55c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
56c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    // upper - dense
57c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    initSparse<Scalar>(density, refMat2, m2, ForceNonZeroDiag|MakeUpperTriangular, &zeroCoords, &nonzeroCoords);
58c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    VERIFY_IS_APPROX(refMat2.template triangularView<Upper>().solve(vec2),
59c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath                     m2.template triangularView<Upper>().solve(vec3));
60c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    VERIFY_IS_APPROX(refMat2.conjugate().template triangularView<Upper>().solve(vec2),
61c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath                     m2.conjugate().template triangularView<Upper>().solve(vec3));
62c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    {
63c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      SparseMatrix<Scalar> cm2(m2);
64c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      //Index rows, Index cols, Index nnz, Index* outerIndexPtr, Index* innerIndexPtr, Scalar* valuePtr
65c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      MappedSparseMatrix<Scalar> mm2(rows, cols, cm2.nonZeros(), cm2.outerIndexPtr(), cm2.innerIndexPtr(), cm2.valuePtr());
66c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      VERIFY_IS_APPROX(refMat2.conjugate().template triangularView<Upper>().solve(vec2),
67c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath                       mm2.conjugate().template triangularView<Upper>().solve(vec3));
68c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    }
69c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
70c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    // lower - transpose
71c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    initSparse<Scalar>(density, refMat2, m2, ForceNonZeroDiag|MakeLowerTriangular, &zeroCoords, &nonzeroCoords);
72c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    VERIFY_IS_APPROX(refMat2.transpose().template triangularView<Upper>().solve(vec2),
73c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath                     m2.transpose().template triangularView<Upper>().solve(vec3));
74c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
75c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    // upper - transpose
76c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    initSparse<Scalar>(density, refMat2, m2, ForceNonZeroDiag|MakeUpperTriangular, &zeroCoords, &nonzeroCoords);
77c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    VERIFY_IS_APPROX(refMat2.transpose().template triangularView<Lower>().solve(vec2),
78c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath                     m2.transpose().template triangularView<Lower>().solve(vec3));
79c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
80c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    SparseMatrix<Scalar> matB(rows, rows);
81c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    DenseMatrix refMatB = DenseMatrix::Zero(rows, rows);
82c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
83c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    // lower - sparse
84c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    initSparse<Scalar>(density, refMat2, m2, ForceNonZeroDiag|MakeLowerTriangular);
85c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    initSparse<Scalar>(density, refMatB, matB);
86c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    refMat2.template triangularView<Lower>().solveInPlace(refMatB);
87c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    m2.template triangularView<Lower>().solveInPlace(matB);
88c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    VERIFY_IS_APPROX(matB.toDense(), refMatB);
89c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
90c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    // upper - sparse
91c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    initSparse<Scalar>(density, refMat2, m2, ForceNonZeroDiag|MakeUpperTriangular);
92c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    initSparse<Scalar>(density, refMatB, matB);
93c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    refMat2.template triangularView<Upper>().solveInPlace(refMatB);
94c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    m2.template triangularView<Upper>().solveInPlace(matB);
95c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    VERIFY_IS_APPROX(matB, refMatB);
96c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
97c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    // test deprecated API
98c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    initSparse<Scalar>(density, refMat2, m2, ForceNonZeroDiag|MakeLowerTriangular, &zeroCoords, &nonzeroCoords);
99c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    VERIFY_IS_APPROX(refMat2.template triangularView<Lower>().solve(vec2),
100c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath                     m2.template triangularView<Lower>().solve(vec3));
101c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  }
102c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath}
103c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
104c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathvoid test_sparse_solvers()
105c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{
106c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  for(int i = 0; i < g_repeat; i++) {
107c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    CALL_SUBTEST_1(sparse_solvers<double>(8, 8) );
108c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    int s = internal::random<int>(1,300);
109c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    CALL_SUBTEST_2(sparse_solvers<std::complex<double> >(s,s) );
110c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    CALL_SUBTEST_1(sparse_solvers<double>(s,s) );
111c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  }
112c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath}
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