svd_common.h
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1 // This file is part of Eigen, a lightweight C++ template library
2 // for linear algebra.
3 //
4 // Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
5 // Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
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 #ifndef SVD_DEFAULT
12 #error a macro SVD_DEFAULT(MatrixType) must be defined prior to including svd_common.h
13 #endif
14 
15 #ifndef SVD_FOR_MIN_NORM
16 #error a macro SVD_FOR_MIN_NORM(MatrixType) must be defined prior to including svd_common.h
17 #endif
18 
19 #include "svd_fill.h"
20 #include "solverbase.h"
21 
22 // Check that the matrix m is properly reconstructed and that the U and V factors are unitary
23 // The SVD must have already been computed.
24 template<typename SvdType, typename MatrixType>
25 void svd_check_full(const MatrixType& m, const SvdType& svd)
26 {
27  Index rows = m.rows();
28  Index cols = m.cols();
29 
30  enum {
31  RowsAtCompileTime = MatrixType::RowsAtCompileTime,
32  ColsAtCompileTime = MatrixType::ColsAtCompileTime
33  };
34 
35  typedef typename MatrixType::Scalar Scalar;
36  typedef typename MatrixType::RealScalar RealScalar;
37  typedef Matrix<Scalar, RowsAtCompileTime, RowsAtCompileTime> MatrixUType;
38  typedef Matrix<Scalar, ColsAtCompileTime, ColsAtCompileTime> MatrixVType;
39 
40  MatrixType sigma = MatrixType::Zero(rows,cols);
41  sigma.diagonal() = svd.singularValues().template cast<Scalar>();
42  MatrixUType u = svd.matrixU();
43  MatrixVType v = svd.matrixV();
44  RealScalar scaling = m.cwiseAbs().maxCoeff();
46  {
47  VERIFY(sigma.cwiseAbs().maxCoeff() <= (std::numeric_limits<RealScalar>::min)());
48  }
49  else
50  {
51  VERIFY_IS_APPROX(m/scaling, u * (sigma/scaling) * v.adjoint());
52  }
55 }
56 
57 // Compare partial SVD defined by computationOptions to a full SVD referenceSvd
58 template<typename SvdType, typename MatrixType>
60  unsigned int computationOptions,
61  const SvdType& referenceSvd)
62 {
63  typedef typename MatrixType::RealScalar RealScalar;
64  Index rows = m.rows();
65  Index cols = m.cols();
66  Index diagSize = (std::min)(rows, cols);
67  RealScalar prec = test_precision<RealScalar>();
68 
69  SvdType svd(m, computationOptions);
70 
71  VERIFY_IS_APPROX(svd.singularValues(), referenceSvd.singularValues());
72 
73  if(computationOptions & (ComputeFullV|ComputeThinV))
74  {
75  VERIFY( (svd.matrixV().adjoint()*svd.matrixV()).isIdentity(prec) );
76  VERIFY_IS_APPROX( svd.matrixV().leftCols(diagSize) * svd.singularValues().asDiagonal() * svd.matrixV().leftCols(diagSize).adjoint(),
77  referenceSvd.matrixV().leftCols(diagSize) * referenceSvd.singularValues().asDiagonal() * referenceSvd.matrixV().leftCols(diagSize).adjoint());
78  }
79 
80  if(computationOptions & (ComputeFullU|ComputeThinU))
81  {
82  VERIFY( (svd.matrixU().adjoint()*svd.matrixU()).isIdentity(prec) );
83  VERIFY_IS_APPROX( svd.matrixU().leftCols(diagSize) * svd.singularValues().cwiseAbs2().asDiagonal() * svd.matrixU().leftCols(diagSize).adjoint(),
84  referenceSvd.matrixU().leftCols(diagSize) * referenceSvd.singularValues().cwiseAbs2().asDiagonal() * referenceSvd.matrixU().leftCols(diagSize).adjoint());
85  }
86 
87  // The following checks are not critical.
88  // For instance, with Dived&Conquer SVD, if only the factor 'V' is computedt then different matrix-matrix product implementation will be used
89  // and the resulting 'V' factor might be significantly different when the SVD decomposition is not unique, especially with single precision float.
90  ++g_test_level;
91  if(computationOptions & ComputeFullU) VERIFY_IS_APPROX(svd.matrixU(), referenceSvd.matrixU());
92  if(computationOptions & ComputeThinU) VERIFY_IS_APPROX(svd.matrixU(), referenceSvd.matrixU().leftCols(diagSize));
93  if(computationOptions & ComputeFullV) VERIFY_IS_APPROX(svd.matrixV().cwiseAbs(), referenceSvd.matrixV().cwiseAbs());
94  if(computationOptions & ComputeThinV) VERIFY_IS_APPROX(svd.matrixV(), referenceSvd.matrixV().leftCols(diagSize));
95  --g_test_level;
96 }
97 
98 //
99 template<typename SvdType, typename MatrixType>
100 void svd_least_square(const MatrixType& m, unsigned int computationOptions)
101 {
102  typedef typename MatrixType::Scalar Scalar;
103  typedef typename MatrixType::RealScalar RealScalar;
104  Index rows = m.rows();
105  Index cols = m.cols();
106 
107  enum {
108  RowsAtCompileTime = MatrixType::RowsAtCompileTime,
109  ColsAtCompileTime = MatrixType::ColsAtCompileTime
110  };
111 
112  typedef Matrix<Scalar, RowsAtCompileTime, Dynamic> RhsType;
113  typedef Matrix<Scalar, ColsAtCompileTime, Dynamic> SolutionType;
114 
115  RhsType rhs = RhsType::Random(rows, internal::random<Index>(1, cols));
116  SvdType svd(m, computationOptions);
117 
119  else if(internal::is_same<RealScalar,float>::value) svd.setThreshold(2e-4);
120 
121  SolutionType x = svd.solve(rhs);
122 
123  RealScalar residual = (m*x-rhs).norm();
124  RealScalar rhs_norm = rhs.norm();
125  if(!test_isMuchSmallerThan(residual,rhs.norm()))
126  {
127  // ^^^ If the residual is very small, then we have an exact solution, so we are already good.
128 
129  // evaluate normal equation which works also for least-squares solutions
130  if(internal::is_same<RealScalar,double>::value || svd.rank()==m.diagonal().size())
131  {
132  using std::sqrt;
133  // This test is not stable with single precision.
134  // This is probably because squaring m signicantly affects the precision.
136 
137  VERIFY_IS_APPROX(m.adjoint()*(m*x),m.adjoint()*rhs);
138 
140  }
141 
142  // Check that there is no significantly better solution in the neighborhood of x
143  for(Index k=0;k<x.rows();++k)
144  {
145  using std::abs;
146 
147  SolutionType y(x);
148  y.row(k) = (RealScalar(1)+2*NumTraits<RealScalar>::epsilon())*x.row(k);
149  RealScalar residual_y = (m*y-rhs).norm();
150  VERIFY( test_isMuchSmallerThan(abs(residual_y-residual), rhs_norm) || residual < residual_y );
152  VERIFY( test_isApprox(residual_y,residual) || residual < residual_y );
154 
155  y.row(k) = (RealScalar(1)-2*NumTraits<RealScalar>::epsilon())*x.row(k);
156  residual_y = (m*y-rhs).norm();
157  VERIFY( test_isMuchSmallerThan(abs(residual_y-residual), rhs_norm) || residual < residual_y );
159  VERIFY( test_isApprox(residual_y,residual) || residual < residual_y );
161  }
162  }
163 }
164 
165 // check minimal norm solutions, the inoput matrix m is only used to recover problem size
166 template<typename MatrixType>
167 void svd_min_norm(const MatrixType& m, unsigned int computationOptions)
168 {
169  typedef typename MatrixType::Scalar Scalar;
170  Index cols = m.cols();
171 
172  enum {
173  ColsAtCompileTime = MatrixType::ColsAtCompileTime
174  };
175 
176  typedef Matrix<Scalar, ColsAtCompileTime, Dynamic> SolutionType;
177 
178  // generate a full-rank m x n problem with m<n
179  enum {
180  RankAtCompileTime2 = ColsAtCompileTime==Dynamic ? Dynamic : (ColsAtCompileTime)/2+1,
181  RowsAtCompileTime3 = ColsAtCompileTime==Dynamic ? Dynamic : ColsAtCompileTime+1
182  };
183  typedef Matrix<Scalar, RankAtCompileTime2, ColsAtCompileTime> MatrixType2;
184  typedef Matrix<Scalar, RankAtCompileTime2, 1> RhsType2;
185  typedef Matrix<Scalar, ColsAtCompileTime, RankAtCompileTime2> MatrixType2T;
186  Index rank = RankAtCompileTime2==Dynamic ? internal::random<Index>(1,cols) : Index(RankAtCompileTime2);
187  MatrixType2 m2(rank,cols);
188  int guard = 0;
189  do {
190  m2.setRandom();
191  } while(SVD_FOR_MIN_NORM(MatrixType2)(m2).setThreshold(test_precision<Scalar>()).rank()!=rank && (++guard)<10);
192  VERIFY(guard<10);
193 
194  RhsType2 rhs2 = RhsType2::Random(rank);
195  // use QR to find a reference minimal norm solution
196  HouseholderQR<MatrixType2T> qr(m2.adjoint());
197  Matrix<Scalar,Dynamic,1> tmp = qr.matrixQR().topLeftCorner(rank,rank).template triangularView<Upper>().adjoint().solve(rhs2);
198  tmp.conservativeResize(cols);
199  tmp.tail(cols-rank).setZero();
200  SolutionType x21 = qr.householderQ() * tmp;
201  // now check with SVD
202  SVD_FOR_MIN_NORM(MatrixType2) svd2(m2, computationOptions);
203  SolutionType x22 = svd2.solve(rhs2);
204  VERIFY_IS_APPROX(m2*x21, rhs2);
205  VERIFY_IS_APPROX(m2*x22, rhs2);
206  VERIFY_IS_APPROX(x21, x22);
207 
208  // Now check with a rank deficient matrix
209  typedef Matrix<Scalar, RowsAtCompileTime3, ColsAtCompileTime> MatrixType3;
210  typedef Matrix<Scalar, RowsAtCompileTime3, 1> RhsType3;
211  Index rows3 = RowsAtCompileTime3==Dynamic ? internal::random<Index>(rank+1,2*cols) : Index(RowsAtCompileTime3);
212  Matrix<Scalar,RowsAtCompileTime3,Dynamic> C = Matrix<Scalar,RowsAtCompileTime3,Dynamic>::Random(rows3,rank);
213  MatrixType3 m3 = C * m2;
214  RhsType3 rhs3 = C * rhs2;
215  SVD_FOR_MIN_NORM(MatrixType3) svd3(m3, computationOptions);
216  SolutionType x3 = svd3.solve(rhs3);
217  VERIFY_IS_APPROX(m3*x3, rhs3);
218  VERIFY_IS_APPROX(m3*x21, rhs3);
219  VERIFY_IS_APPROX(m2*x3, rhs2);
220  VERIFY_IS_APPROX(x21, x3);
221 }
222 
223 template<typename MatrixType, typename SolverType>
224 void svd_test_solvers(const MatrixType& m, const SolverType& solver) {
225  Index rows, cols, cols2;
226 
227  rows = m.rows();
228  cols = m.cols();
229 
230  if(MatrixType::ColsAtCompileTime==Dynamic)
231  {
232  cols2 = internal::random<int>(2,EIGEN_TEST_MAX_SIZE);
233  }
234  else
235  {
236  cols2 = cols;
237  }
238  typedef Matrix<typename MatrixType::Scalar, MatrixType::ColsAtCompileTime, MatrixType::ColsAtCompileTime> CMatrixType;
239  check_solverbase<CMatrixType, MatrixType>(m, solver, rows, cols, cols2);
240 }
241 
242 // Check full, compare_to_full, least_square, and min_norm for all possible compute-options
243 template<typename SvdType, typename MatrixType>
244 void svd_test_all_computation_options(const MatrixType& m, bool full_only)
245 {
246 // if (QRPreconditioner == NoQRPreconditioner && m.rows() != m.cols())
247 // return;
249 
250  SvdType fullSvd(m, ComputeFullU|ComputeFullV);
251  CALL_SUBTEST(( svd_check_full(m, fullSvd) ));
252  CALL_SUBTEST(( svd_least_square<SvdType>(m, ComputeFullU | ComputeFullV) ));
254 
255  #if defined __INTEL_COMPILER
256  // remark #111: statement is unreachable
257  #pragma warning disable 111
258  #endif
259 
260  svd_test_solvers(m, fullSvd);
261 
262  if(full_only)
263  return;
264 
267  CALL_SUBTEST(( svd_compare_to_full(m, 0, fullSvd) ));
268 
269  if (MatrixType::ColsAtCompileTime == Dynamic) {
270  // thin U/V are only available with dynamic number of columns
276 
277  CALL_SUBTEST(( svd_least_square<SvdType>(m, ComputeFullU | ComputeThinV) ));
278  CALL_SUBTEST(( svd_least_square<SvdType>(m, ComputeThinU | ComputeFullV) ));
279  CALL_SUBTEST(( svd_least_square<SvdType>(m, ComputeThinU | ComputeThinV) ));
280 
284 
285  // test reconstruction
286  Index diagSize = (std::min)(m.rows(), m.cols());
287  SvdType svd(m, ComputeThinU | ComputeThinV);
288  VERIFY_IS_APPROX(m, svd.matrixU().leftCols(diagSize) * svd.singularValues().asDiagonal() * svd.matrixV().leftCols(diagSize).adjoint());
289  }
290 }
291 
292 
293 // work around stupid msvc error when constructing at compile time an expression that involves
294 // a division by zero, even if the numeric type has floating point
295 template<typename Scalar>
297 
298 // workaround aggressive optimization in ICC
299 template<typename T> EIGEN_DONT_INLINE T sub(T a, T b) { return a - b; }
300 
301 // This function verifies we don't iterate infinitely on nan/inf values,
302 // and that info() returns InvalidInput.
303 template<typename SvdType, typename MatrixType>
305 {
306  SvdType svd;
307  typedef typename MatrixType::Scalar Scalar;
308  Scalar some_inf = Scalar(1) / zero<Scalar>();
309  VERIFY(sub(some_inf, some_inf) != sub(some_inf, some_inf));
310  svd.compute(MatrixType::Constant(10,10,some_inf), ComputeFullU | ComputeFullV);
311  VERIFY(svd.info() == InvalidInput);
312 
313  Scalar nan = std::numeric_limits<Scalar>::quiet_NaN();
314  VERIFY(nan != nan);
315  svd.compute(MatrixType::Constant(10,10,nan), ComputeFullU | ComputeFullV);
316  VERIFY(svd.info() == InvalidInput);
317 
318  MatrixType m = MatrixType::Zero(10,10);
319  m(internal::random<int>(0,9), internal::random<int>(0,9)) = some_inf;
320  svd.compute(m, ComputeFullU | ComputeFullV);
321  VERIFY(svd.info() == InvalidInput);
322 
323  m = MatrixType::Zero(10,10);
324  m(internal::random<int>(0,9), internal::random<int>(0,9)) = nan;
325  svd.compute(m, ComputeFullU | ComputeFullV);
326  VERIFY(svd.info() == InvalidInput);
327 
328  // regression test for bug 791
329  m.resize(3,3);
331  0, -0.5, 0,
332  nan, 0, 0;
333  svd.compute(m, ComputeFullU | ComputeFullV);
334  VERIFY(svd.info() == InvalidInput);
335 
336  m.resize(4,4);
337  m << 1, 0, 0, 0,
338  0, 3, 1, 2e-308,
339  1, 0, 1, nan,
340  0, nan, nan, 0;
341  svd.compute(m, ComputeFullU | ComputeFullV);
342  VERIFY(svd.info() == InvalidInput);
343 }
344 
345 // Regression test for bug 286: JacobiSVD loops indefinitely with some
346 // matrices containing denormal numbers.
347 template<typename>
349 {
350 #if defined __INTEL_COMPILER
351 // shut up warning #239: floating point underflow
352 #pragma warning push
353 #pragma warning disable 239
354 #endif
355  Matrix2d M;
356  M << -7.90884e-313, -4.94e-324,
357  0, 5.60844e-313;
358  SVD_DEFAULT(Matrix2d) svd;
359  svd.compute(M,ComputeFullU|ComputeFullV);
361 
362  // Check all 2x2 matrices made with the following coefficients:
363  VectorXd value_set(9);
364  value_set << 0, 1, -1, 5.60844e-313, -5.60844e-313, 4.94e-324, -4.94e-324, -4.94e-223, 4.94e-223;
365  Array4i id(0,0,0,0);
366  int k = 0;
367  do
368  {
369  M << value_set(id(0)), value_set(id(1)), value_set(id(2)), value_set(id(3));
370  svd.compute(M,ComputeFullU|ComputeFullV);
372 
373  id(k)++;
374  if(id(k)>=value_set.size())
375  {
376  while(k<3 && id(k)>=value_set.size()) id(++k)++;
377  id.head(k).setZero();
378  k=0;
379  }
380 
381  } while((id<int(value_set.size())).all());
382 
383 #if defined __INTEL_COMPILER
384 #pragma warning pop
385 #endif
386 
387  // Check for overflow:
388  Matrix3d M3;
389  M3 << 4.4331978442502944e+307, -5.8585363752028680e+307, 6.4527017443412964e+307,
390  3.7841695601406358e+307, 2.4331702789740617e+306, -3.5235707140272905e+307,
391  -8.7190887618028355e+307, -7.3453213709232193e+307, -2.4367363684472105e+307;
392 
393  SVD_DEFAULT(Matrix3d) svd3;
394  svd3.compute(M3,ComputeFullU|ComputeFullV); // just check we don't loop indefinitely
395  CALL_SUBTEST( svd_check_full(M3,svd3) );
396 }
397 
398 // void jacobisvd(const MatrixType& a = MatrixType(), bool pickrandom = true)
399 
400 template<typename MatrixType>
401 void svd_all_trivial_2x2( void (*cb)(const MatrixType&,bool) )
402 {
403  MatrixType M;
404  VectorXd value_set(3);
405  value_set << 0, 1, -1;
406  Array4i id(0,0,0,0);
407  int k = 0;
408  do
409  {
410  M << value_set(id(0)), value_set(id(1)), value_set(id(2)), value_set(id(3));
411 
412  cb(M,false);
413 
414  id(k)++;
415  if(id(k)>=value_set.size())
416  {
417  while(k<3 && id(k)>=value_set.size()) id(++k)++;
418  id.head(k).setZero();
419  k=0;
420  }
421 
422  } while((id<int(value_set.size())).all());
423 }
424 
425 template<typename>
427 {
428  Vector3f v(3.f, 2.f, 1.f);
429  MatrixXf m = v.asDiagonal();
430 
431  internal::set_is_malloc_allowed(false);
432  VERIFY_RAISES_ASSERT(VectorXf tmp(10);)
433  SVD_DEFAULT(MatrixXf) svd;
434  internal::set_is_malloc_allowed(true);
435  svd.compute(m);
436  VERIFY_IS_APPROX(svd.singularValues(), v);
437 
438  SVD_DEFAULT(MatrixXf) svd2(3,3);
439  internal::set_is_malloc_allowed(false);
440  svd2.compute(m);
441  internal::set_is_malloc_allowed(true);
442  VERIFY_IS_APPROX(svd2.singularValues(), v);
443  VERIFY_RAISES_ASSERT(svd2.matrixU());
444  VERIFY_RAISES_ASSERT(svd2.matrixV());
445  svd2.compute(m, ComputeFullU | ComputeFullV);
446  VERIFY_IS_APPROX(svd2.matrixU(), Matrix3f::Identity());
447  VERIFY_IS_APPROX(svd2.matrixV(), Matrix3f::Identity());
448  internal::set_is_malloc_allowed(false);
449  svd2.compute(m);
450  internal::set_is_malloc_allowed(true);
451 
452  SVD_DEFAULT(MatrixXf) svd3(3,3,ComputeFullU|ComputeFullV);
453  internal::set_is_malloc_allowed(false);
454  svd2.compute(m);
455  internal::set_is_malloc_allowed(true);
456  VERIFY_IS_APPROX(svd2.singularValues(), v);
457  VERIFY_IS_APPROX(svd2.matrixU(), Matrix3f::Identity());
458  VERIFY_IS_APPROX(svd2.matrixV(), Matrix3f::Identity());
459  internal::set_is_malloc_allowed(false);
460  svd2.compute(m, ComputeFullU|ComputeFullV);
461  internal::set_is_malloc_allowed(true);
462 }
463 
464 template<typename SvdType,typename MatrixType>
465 void svd_verify_assert(const MatrixType& m, bool fullOnly = false)
466 {
467  typedef typename MatrixType::Scalar Scalar;
468  Index rows = m.rows();
469  Index cols = m.cols();
470 
471  enum {
472  RowsAtCompileTime = MatrixType::RowsAtCompileTime,
473  ColsAtCompileTime = MatrixType::ColsAtCompileTime
474  };
475 
476  typedef Matrix<Scalar, RowsAtCompileTime, 1> RhsType;
477  RhsType rhs(rows);
478  SvdType svd;
479  VERIFY_RAISES_ASSERT(svd.matrixU())
480  VERIFY_RAISES_ASSERT(svd.singularValues())
481  VERIFY_RAISES_ASSERT(svd.matrixV())
482  VERIFY_RAISES_ASSERT(svd.solve(rhs))
483  VERIFY_RAISES_ASSERT(svd.transpose().solve(rhs))
484  VERIFY_RAISES_ASSERT(svd.adjoint().solve(rhs))
485  MatrixType a = MatrixType::Zero(rows, cols);
486  a.setZero();
487  svd.compute(a, 0);
488  VERIFY_RAISES_ASSERT(svd.matrixU())
489  VERIFY_RAISES_ASSERT(svd.matrixV())
490  svd.singularValues();
491  VERIFY_RAISES_ASSERT(svd.solve(rhs))
492 
493  svd.compute(a, ComputeFullU);
494  svd.matrixU();
495  VERIFY_RAISES_ASSERT(svd.matrixV())
496  VERIFY_RAISES_ASSERT(svd.solve(rhs))
497  svd.compute(a, ComputeFullV);
498  svd.matrixV();
499  VERIFY_RAISES_ASSERT(svd.matrixU())
500  VERIFY_RAISES_ASSERT(svd.solve(rhs))
501 
502  if (!fullOnly && ColsAtCompileTime == Dynamic)
503  {
504  svd.compute(a, ComputeThinU);
505  svd.matrixU();
506  VERIFY_RAISES_ASSERT(svd.matrixV())
507  VERIFY_RAISES_ASSERT(svd.solve(rhs))
508  svd.compute(a, ComputeThinV);
509  svd.matrixV();
510  VERIFY_RAISES_ASSERT(svd.matrixU())
511  VERIFY_RAISES_ASSERT(svd.solve(rhs))
512  }
513  else
514  {
517  }
518 }
519 
520 #undef SVD_DEFAULT
521 #undef SVD_FOR_MIN_NORM
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Definition: svd_common.h:224
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Definition: bench_gemm.cpp:50
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Definition: datatypes.h:19
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Definition: datatypes.h:17
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Definition: test_callbacks.py:158
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Definition: jet.h:418
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autogenerated on Thu Jun 13 2024 03:06:53