1 // This file is part of Eigen, a lightweight C++ template library
2 // for linear algebra.
3 //
4 // Copyright (C) 2008 Gael Guennebaud <[email protected]>
5 // Copyright (C) 2006-2008 Benoit Jacob <[email protected]>
6 //
7 // This Source Code Form is subject to the terms of the Mozilla
8 // Public License v. 2.0. If a copy of the MPL was not distributed
9 // with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
10
11 // discard stack allocation as that too bypasses malloc
12 #define EIGEN_STACK_ALLOCATION_LIMIT 0
13 // heap allocation will raise an assert if enabled at runtime
14 #define EIGEN_RUNTIME_NO_MALLOC
15
16 #include "main.h"
17 #include <Eigen/Cholesky>
18 #include <Eigen/Eigenvalues>
19 #include <Eigen/LU>
20 #include <Eigen/QR>
21 #include <Eigen/SVD>
22
nomalloc(const MatrixType & m)23 template<typename MatrixType> void nomalloc(const MatrixType& m)
24 {
25 /* this test check no dynamic memory allocation are issued with fixed-size matrices
26 */
27 typedef typename MatrixType::Scalar Scalar;
28
29 Index rows = m.rows();
30 Index cols = m.cols();
31
32 MatrixType m1 = MatrixType::Random(rows, cols),
33 m2 = MatrixType::Random(rows, cols),
34 m3(rows, cols);
35
36 Scalar s1 = internal::random<Scalar>();
37
38 Index r = internal::random<Index>(0, rows-1),
39 c = internal::random<Index>(0, cols-1);
40
41 VERIFY_IS_APPROX((m1+m2)*s1, s1*m1+s1*m2);
42 VERIFY_IS_APPROX((m1+m2)(r,c), (m1(r,c))+(m2(r,c)));
43 VERIFY_IS_APPROX(m1.cwiseProduct(m1.block(0,0,rows,cols)), (m1.array()*m1.array()).matrix());
44 VERIFY_IS_APPROX((m1*m1.transpose())*m2, m1*(m1.transpose()*m2));
45
46 m2.col(0).noalias() = m1 * m1.col(0);
47 m2.col(0).noalias() -= m1.adjoint() * m1.col(0);
48 m2.col(0).noalias() -= m1 * m1.row(0).adjoint();
49 m2.col(0).noalias() -= m1.adjoint() * m1.row(0).adjoint();
50
51 m2.row(0).noalias() = m1.row(0) * m1;
52 m2.row(0).noalias() -= m1.row(0) * m1.adjoint();
53 m2.row(0).noalias() -= m1.col(0).adjoint() * m1;
54 m2.row(0).noalias() -= m1.col(0).adjoint() * m1.adjoint();
55 VERIFY_IS_APPROX(m2,m2);
56
57 m2.col(0).noalias() = m1.template triangularView<Upper>() * m1.col(0);
58 m2.col(0).noalias() -= m1.adjoint().template triangularView<Upper>() * m1.col(0);
59 m2.col(0).noalias() -= m1.template triangularView<Upper>() * m1.row(0).adjoint();
60 m2.col(0).noalias() -= m1.adjoint().template triangularView<Upper>() * m1.row(0).adjoint();
61
62 m2.row(0).noalias() = m1.row(0) * m1.template triangularView<Upper>();
63 m2.row(0).noalias() -= m1.row(0) * m1.adjoint().template triangularView<Upper>();
64 m2.row(0).noalias() -= m1.col(0).adjoint() * m1.template triangularView<Upper>();
65 m2.row(0).noalias() -= m1.col(0).adjoint() * m1.adjoint().template triangularView<Upper>();
66 VERIFY_IS_APPROX(m2,m2);
67
68 m2.col(0).noalias() = m1.template selfadjointView<Upper>() * m1.col(0);
69 m2.col(0).noalias() -= m1.adjoint().template selfadjointView<Upper>() * m1.col(0);
70 m2.col(0).noalias() -= m1.template selfadjointView<Upper>() * m1.row(0).adjoint();
71 m2.col(0).noalias() -= m1.adjoint().template selfadjointView<Upper>() * m1.row(0).adjoint();
72
73 m2.row(0).noalias() = m1.row(0) * m1.template selfadjointView<Upper>();
74 m2.row(0).noalias() -= m1.row(0) * m1.adjoint().template selfadjointView<Upper>();
75 m2.row(0).noalias() -= m1.col(0).adjoint() * m1.template selfadjointView<Upper>();
76 m2.row(0).noalias() -= m1.col(0).adjoint() * m1.adjoint().template selfadjointView<Upper>();
77 VERIFY_IS_APPROX(m2,m2);
78
79 m2.template selfadjointView<Lower>().rankUpdate(m1.col(0),-1);
80 m2.template selfadjointView<Upper>().rankUpdate(m1.row(0),-1);
81 m2.template selfadjointView<Lower>().rankUpdate(m1.col(0), m1.col(0)); // rank-2
82
83 // The following fancy matrix-matrix products are not safe yet regarding static allocation
84 m2.template selfadjointView<Lower>().rankUpdate(m1);
85 m2 += m2.template triangularView<Upper>() * m1;
86 m2.template triangularView<Upper>() = m2 * m2;
87 m1 += m1.template selfadjointView<Lower>() * m2;
88 VERIFY_IS_APPROX(m2,m2);
89 }
90
91 template<typename Scalar>
ctms_decompositions()92 void ctms_decompositions()
93 {
94 const int maxSize = 16;
95 const int size = 12;
96
97 typedef Eigen::Matrix<Scalar,
98 Eigen::Dynamic, Eigen::Dynamic,
99 0,
100 maxSize, maxSize> Matrix;
101
102 typedef Eigen::Matrix<Scalar,
103 Eigen::Dynamic, 1,
104 0,
105 maxSize, 1> Vector;
106
107 typedef Eigen::Matrix<std::complex<Scalar>,
108 Eigen::Dynamic, Eigen::Dynamic,
109 0,
110 maxSize, maxSize> ComplexMatrix;
111
112 const Matrix A(Matrix::Random(size, size)), B(Matrix::Random(size, size));
113 Matrix X(size,size);
114 const ComplexMatrix complexA(ComplexMatrix::Random(size, size));
115 const Matrix saA = A.adjoint() * A;
116 const Vector b(Vector::Random(size));
117 Vector x(size);
118
119 // Cholesky module
120 Eigen::LLT<Matrix> LLT; LLT.compute(A);
121 X = LLT.solve(B);
122 x = LLT.solve(b);
123 Eigen::LDLT<Matrix> LDLT; LDLT.compute(A);
124 X = LDLT.solve(B);
125 x = LDLT.solve(b);
126
127 // Eigenvalues module
128 Eigen::HessenbergDecomposition<ComplexMatrix> hessDecomp; hessDecomp.compute(complexA);
129 Eigen::ComplexSchur<ComplexMatrix> cSchur(size); cSchur.compute(complexA);
130 Eigen::ComplexEigenSolver<ComplexMatrix> cEigSolver; cEigSolver.compute(complexA);
131 Eigen::EigenSolver<Matrix> eigSolver; eigSolver.compute(A);
132 Eigen::SelfAdjointEigenSolver<Matrix> saEigSolver(size); saEigSolver.compute(saA);
133 Eigen::Tridiagonalization<Matrix> tridiag; tridiag.compute(saA);
134
135 // LU module
136 Eigen::PartialPivLU<Matrix> ppLU; ppLU.compute(A);
137 X = ppLU.solve(B);
138 x = ppLU.solve(b);
139 Eigen::FullPivLU<Matrix> fpLU; fpLU.compute(A);
140 X = fpLU.solve(B);
141 x = fpLU.solve(b);
142
143 // QR module
144 Eigen::HouseholderQR<Matrix> hQR; hQR.compute(A);
145 X = hQR.solve(B);
146 x = hQR.solve(b);
147 Eigen::ColPivHouseholderQR<Matrix> cpQR; cpQR.compute(A);
148 X = cpQR.solve(B);
149 x = cpQR.solve(b);
150 Eigen::FullPivHouseholderQR<Matrix> fpQR; fpQR.compute(A);
151 // FIXME X = fpQR.solve(B);
152 x = fpQR.solve(b);
153
154 // SVD module
155 Eigen::JacobiSVD<Matrix> jSVD; jSVD.compute(A, ComputeFullU | ComputeFullV);
156 }
157
test_zerosized()158 void test_zerosized() {
159 // default constructors:
160 Eigen::MatrixXd A;
161 Eigen::VectorXd v;
162 // explicit zero-sized:
163 Eigen::ArrayXXd A0(0,0);
164 Eigen::ArrayXd v0(0);
165
166 // assigning empty objects to each other:
167 A=A0;
168 v=v0;
169 }
170
test_reference(const MatrixType & m)171 template<typename MatrixType> void test_reference(const MatrixType& m) {
172 typedef typename MatrixType::Scalar Scalar;
173 enum { Flag = MatrixType::IsRowMajor ? Eigen::RowMajor : Eigen::ColMajor};
174 enum { TransposeFlag = !MatrixType::IsRowMajor ? Eigen::RowMajor : Eigen::ColMajor};
175 Index rows = m.rows(), cols=m.cols();
176 typedef Eigen::Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic, Flag > MatrixX;
177 typedef Eigen::Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic, TransposeFlag> MatrixXT;
178 // Dynamic reference:
179 typedef Eigen::Ref<const MatrixX > Ref;
180 typedef Eigen::Ref<const MatrixXT > RefT;
181
182 Ref r1(m);
183 Ref r2(m.block(rows/3, cols/4, rows/2, cols/2));
184 RefT r3(m.transpose());
185 RefT r4(m.topLeftCorner(rows/2, cols/2).transpose());
186
187 VERIFY_RAISES_ASSERT(RefT r5(m));
188 VERIFY_RAISES_ASSERT(Ref r6(m.transpose()));
189 VERIFY_RAISES_ASSERT(Ref r7(Scalar(2) * m));
190
191 // Copy constructors shall also never malloc
192 Ref r8 = r1;
193 RefT r9 = r3;
194
195 // Initializing from a compatible Ref shall also never malloc
196 Eigen::Ref<const MatrixX, Unaligned, Stride<Dynamic, Dynamic> > r10=r8, r11=m;
197
198 // Initializing from an incompatible Ref will malloc:
199 typedef Eigen::Ref<const MatrixX, Aligned> RefAligned;
200 VERIFY_RAISES_ASSERT(RefAligned r12=r10);
201 VERIFY_RAISES_ASSERT(Ref r13=r10); // r10 has more dynamic strides
202
203 }
204
EIGEN_DECLARE_TEST(nomalloc)205 EIGEN_DECLARE_TEST(nomalloc)
206 {
207 // create some dynamic objects
208 Eigen::MatrixXd M1 = MatrixXd::Random(3,3);
209 Ref<const MatrixXd> R1 = 2.0*M1; // Ref requires temporary
210
211 // from here on prohibit malloc:
212 Eigen::internal::set_is_malloc_allowed(false);
213
214 // check that our operator new is indeed called:
215 VERIFY_RAISES_ASSERT(MatrixXd dummy(MatrixXd::Random(3,3)));
216 CALL_SUBTEST_1(nomalloc(Matrix<float, 1, 1>()) );
217 CALL_SUBTEST_2(nomalloc(Matrix4d()) );
218 CALL_SUBTEST_3(nomalloc(Matrix<float,32,32>()) );
219
220 // Check decomposition modules with dynamic matrices that have a known compile-time max size (ctms)
221 CALL_SUBTEST_4(ctms_decompositions<float>());
222
223 CALL_SUBTEST_5(test_zerosized());
224
225 CALL_SUBTEST_6(test_reference(Matrix<float,32,32>()));
226 CALL_SUBTEST_7(test_reference(R1));
227 CALL_SUBTEST_8(Ref<MatrixXd> R2 = M1.topRows<2>(); test_reference(R2));
228 }
229