GetFEM  5.4.2
gmm_dense_matrix_functions.h
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30 ===========================================================================*/
31 
32 /**@file gmm_dense_matrix_functions.h
33  @author Konstantinos Poulios <poulios.konstantinos@gmail.com>
34  @date December 10, 2014.
35  @brief Common matrix functions for dense matrices.
36 */
37 #ifndef GMM_DENSE_MATRIX_FUNCTIONS_H
38 #define GMM_DENSE_MATRIX_FUNCTIONS_H
39 
40 
41 namespace gmm {
42 
43 
44  /**
45  Matrix square root for upper triangular matrices (from GNU Octave).
46  */
47  template <typename T>
48  void sqrtm_utri_inplace(dense_matrix<T>& A)
49  {
50  typedef typename number_traits<T>::magnitude_type R;
51  bool singular = false;
52 
53  // The following code is equivalent to this triple loop:
54  //
55  // n = rows (A);
56  // for j = 1:n
57  // A(j,j) = sqrt (A(j,j));
58  // for i = j-1:-1:1
59  // A(i,j) /= (A(i,i) + A(j,j));
60  // k = 1:i-1;
61  // t storing a A(k,j) -= A(k,i) * A(i,j);
62  // endfor
63  // endfor
64 
65  R tol = R(0); // default_tol(R()) * gmm::mat_maxnorm(A);
66 
67  const size_type n = mat_nrows(A);
68  for (int j=0; j < int(n); j++) {
69  typename dense_matrix<T>::iterator colj = A.begin() + j*n;
70  if (gmm::abs(colj[j]) > tol)
71  colj[j] = gmm::sqrt(colj[j]);
72  else
73  singular = true;
74 
75  for (int i=j-1; i >= 0; i--) {
76  typename dense_matrix<T>::const_iterator coli = A.begin() + i*n;
77  T colji = colj[i] = safe_divide(colj[i], (coli[i] + colj[j]));
78  for (int k = 0; k < i; k++)
79  colj[k] -= coli[k] * colji;
80  }
81  }
82 
83  if (singular)
84  GMM_WARNING1("Matrix is singular, may not have a square root");
85  }
86 
87 
88  template <typename T>
89  void sqrtm(const dense_matrix<std::complex<T> >& A,
90  dense_matrix<std::complex<T> >& SQRTMA)
91  {
92  GMM_ASSERT1(gmm::mat_nrows(A) == gmm::mat_ncols(A),
93  "Matrix square root requires a square matrix");
94  gmm::resize(SQRTMA, gmm::mat_nrows(A), gmm::mat_ncols(A));
95  dense_matrix<std::complex<T> > S(A), Q(A), TMP(A);
96  #if defined(GMM_USES_LAPACK)
97  schur(TMP, S, Q);
98  #else
99  GMM_ASSERT1(false, "Please recompile with lapack and blas librairies "
100  "to use sqrtm matrix function.");
101  #endif
102  sqrtm_utri_inplace(S);
103  gmm::mult(Q, S, TMP);
104  gmm::mult(TMP, gmm::transposed(Q), SQRTMA);
105  }
106 
107  template <typename T>
108  void sqrtm(const dense_matrix<T>& A,
109  dense_matrix<std::complex<T> >& SQRTMA)
110  {
111  dense_matrix<std::complex<T> > cA(mat_nrows(A), mat_ncols(A));
112  gmm::copy(A, gmm::real_part(cA));
113  sqrtm(cA, SQRTMA);
114  }
115 
116  template <typename T>
117  void sqrtm(const dense_matrix<T>& A, dense_matrix<T>& SQRTMA)
118  {
119  dense_matrix<std::complex<T> > cA(mat_nrows(A), mat_ncols(A));
120  gmm::copy(A, gmm::real_part(cA));
121  dense_matrix<std::complex<T> > cSQRTMA(cA);
122  sqrtm(cA, cSQRTMA);
123  gmm::resize(SQRTMA, gmm::mat_nrows(A), gmm::mat_ncols(A));
124  gmm::copy(gmm::real_part(cSQRTMA), SQRTMA);
125 // dense_matrix<std::complex<T1> >::const_reference
126 // it = cSQRTMA.begin(), ite = cSQRTMA.end();
127 // dense_matrix<std::complex<T1> >::reference
128 // rit = SQRTMA.begin();
129 // for (; it != ite; ++it, ++rit) *rit = it->real();
130  }
131 
132 
133  /**
134  Matrix logarithm for upper triangular matrices (from GNU/Octave)
135  */
136  template <typename T>
137  void logm_utri_inplace(dense_matrix<T>& S)
138  {
139  typedef typename number_traits<T>::magnitude_type R;
140 
141  size_type n = gmm::mat_nrows(S);
142  GMM_ASSERT1(n == gmm::mat_ncols(S),
143  "Matrix logarithm is not defined for non-square matrices");
144  for (size_type i=0; i < n-1; ++i)
145  if (gmm::abs(S(i+1,i)) > default_tol(T())) {
146  GMM_ASSERT1(false, "An upper triangular matrix is expected");
147  break;
148  }
149  for (size_type i=0; i < n-1; ++i)
150  if (gmm::real(S(i,i)) <= -default_tol(R()) &&
151  gmm::abs(gmm::imag(S(i,i))) <= default_tol(R())) {
152  GMM_ASSERT1(false, "Principal matrix logarithm is not defined "
153  "for matrices with negative eigenvalues");
154  break;
155  }
156 
157  // Algorithm 11.9 in "Function of matrices", by N. Higham
158  R theta[] = { R(0),R(0),R(1.61e-2),R(5.38e-2),R(1.13e-1),R(1.86e-1),R(2.6429608311114350e-1) };
159 
160  R scaling(1);
161  size_type p(0), m(6), opt_iters(100);
162  for (size_type k=0; k < opt_iters; ++k, scaling *= R(2)) {
163  dense_matrix<T> auxS(S);
164  for (size_type i = 0; i < n; ++i) auxS(i,i) -= R(1);
165  R tau = gmm::mat_norm1(auxS);
166  if (tau <= theta[6]) {
167  ++p;
168  size_type j1(6), j2(6);
169  for (size_type j=0; j < 6; ++j)
170  if (tau <= theta[j]) { j1 = j; break; }
171  for (size_type j=0; j < j1; ++j)
172  if (tau <= 2*theta[j]) { j2 = j; break; }
173  if (j1 - j2 <= 1 || p == 2) { m = j1; break; }
174  }
175  sqrtm_utri_inplace(S);
176  if (k == opt_iters-1)
177  GMM_WARNING1 ("Maximum number of square roots exceeded; "
178  "the calculated matrix logarithm may still be accurate");
179  }
180 
181  for (size_type i = 0; i < n; ++i) S(i,i) -= R(1);
182 
183  if (m > 0) {
184 
185  std::vector<R> nodes, wts;
186  switch(m) {
187  case 0: {
188  R nodes_[] = { R(0.5) };
189  R wts_[] = { R(1) };
190  nodes.assign(nodes_, nodes_+m+1);
191  wts.assign(wts_, wts_+m+1);
192  } break;
193  case 1: {
194  R nodes_[] = { R(0.211324865405187),R(0.788675134594813) };
195  R wts_[] = { R(0.5),R(0.5) };
196  nodes.assign(nodes_, nodes_+m+1);
197  wts.assign(wts_, wts_+m+1);
198  } break;
199  case 2: {
200  R nodes_[] = { R(0.112701665379258),R(0.500000000000000),R(0.887298334620742) };
201  R wts_[] = { R(0.277777777777778),R(0.444444444444444),R(0.277777777777778) };
202  nodes.assign(nodes_, nodes_+m+1);
203  wts.assign(wts_, wts_+m+1);
204  } break;
205  case 3: {
206  R nodes_[] = { R(0.0694318442029737),R(0.3300094782075718),R(0.6699905217924281),R(0.9305681557970263) };
207  R wts_[] = { R(0.173927422568727),R(0.326072577431273),R(0.326072577431273),R(0.173927422568727) };
208  nodes.assign(nodes_, nodes_+m+1);
209  wts.assign(wts_, wts_+m+1);
210  } break;
211  case 4: {
212  R nodes_[] = { R(0.0469100770306681),R(0.2307653449471584),R(0.5000000000000000),R(0.7692346550528415),R(0.9530899229693319) };
213  R wts_[] = { R(0.118463442528095),R(0.239314335249683),R(0.284444444444444),R(0.239314335249683),R(0.118463442528094) };
214  nodes.assign(nodes_, nodes_+m+1);
215  wts.assign(wts_, wts_+m+1);
216  } break;
217  case 5: {
218  R nodes_[] = { R(0.0337652428984240),R(0.1693953067668678),R(0.3806904069584015),R(0.6193095930415985),R(0.8306046932331322),R(0.9662347571015761) };
219  R wts_[] = { R(0.0856622461895853),R(0.1803807865240693),R(0.2339569672863452),R(0.2339569672863459),R(0.1803807865240693),R(0.0856622461895852) };
220  nodes.assign(nodes_, nodes_+m+1);
221  wts.assign(wts_, wts_+m+1);
222  } break;
223  case 6: {
224  R nodes_[] = { R(0.0254460438286208),R(0.1292344072003028),R(0.2970774243113015),R(0.4999999999999999),R(0.7029225756886985),R(0.8707655927996973),R(0.9745539561713792) };
225  R wts_[] = { R(0.0647424830844348),R(0.1398526957446384),R(0.1909150252525594),R(0.2089795918367343),R(0.1909150252525595),R(0.1398526957446383),R(0.0647424830844349) };
226  nodes.assign(nodes_, nodes_+m+1);
227  wts.assign(wts_, wts_+m+1);
228  } break;
229  }
230 
231  dense_matrix<T> auxS1(S), auxS2(S);
232  std::vector<T> auxvec(n);
233  gmm::clear(S);
234  for (size_type j=0; j <= m; ++j) {
235  gmm::copy(gmm::scaled(auxS1, nodes[j]), auxS2);
236  gmm::add(gmm::identity_matrix(), auxS2);
237  // S += wts[i] * auxS1 * inv(auxS2)
238  for (size_type i=0; i < n; ++i) {
239  gmm::copy(gmm::mat_row(auxS1, i), auxvec);
240  gmm::lower_tri_solve(gmm::transposed(auxS2), auxvec, false);
241  gmm::add(gmm::scaled(auxvec, wts[j]), gmm::mat_row(S, i));
242  }
243  }
244  }
245  gmm::scale(S, scaling);
246  }
247 
248  /**
249  Matrix logarithm (from GNU/Octave)
250  */
251  template <typename T>
252  void logm(const dense_matrix<T>& A, dense_matrix<T>& LOGMA)
253  {
254  typedef typename number_traits<T>::magnitude_type R;
255  size_type n = gmm::mat_nrows(A);
256  GMM_ASSERT1(n == gmm::mat_ncols(A),
257  "Matrix logarithm is not defined for non-square matrices");
258  dense_matrix<T> S(A), Q(A);
259  #if defined(GMM_USES_LAPACK)
260  schur(A, S, Q); // A = Q * S * Q^T
261  #else
262  GMM_ASSERT1(false, "Please recompile with lapack and blas librairies "
263  "to use logm matrix function.");
264  #endif
265 
266  bool convert_to_complex(false);
267  if (!is_complex(T()))
268  for (size_type i=0; i < n-1; ++i)
269  if (gmm::abs(S(i+1,i)) > default_tol(T())) {
270  convert_to_complex = true;
271  break;
272  }
273 
274  gmm::resize(LOGMA, n, n);
275  if (convert_to_complex) {
276  dense_matrix<std::complex<R> > cS(n,n), cQ(n,n), auxmat(n,n);
277  gmm::copy(gmm::real_part(S), gmm::real_part(cS));
278  gmm::copy(gmm::real_part(Q), gmm::real_part(cQ));
279  block2x2_reduction(cS, cQ, default_tol(R())*R(3));
280  for (size_type j=0; j < n-1; ++j)
281  for (size_type i=j+1; i < n; ++i)
282  cS(i,j) = T(0);
283  logm_utri_inplace(cS);
284  gmm::mult(cQ, cS, auxmat);
285  gmm::mult(auxmat, gmm::transposed(cQ), cS);
286  // Remove small complex values which may have entered calculation
287  gmm::copy(gmm::real_part(cS), LOGMA);
288 // GMM_ASSERT1(gmm::mat_norm1(gmm::imag_part(cS)) < n*default_tol(T()),
289 // "Internal error, imag part should be zero");
290  } else {
291  dense_matrix<T> auxmat(n,n);
292  logm_utri_inplace(S);
293  gmm::mult(Q, S, auxmat);
294  gmm::mult(auxmat, gmm::transposed(Q), LOGMA);
295  }
296 
297  }
298 
299 }
300 
301 #endif
302 
gmm::resize
void resize(M &v, size_type m, size_type n)
*‍/
Definition: gmm_blas.h:231
gmm::clear
void clear(L &l)
clear (fill with zeros) a vector or matrix.
Definition: gmm_blas.h:59
gmm::mat_norm1
number_traits< typename linalg_traits< M >::value_type >::magnitude_type mat_norm1(const M &m)
*‍/
Definition: gmm_blas.h:782
gmm::sqrtm_utri_inplace
void sqrtm_utri_inplace(dense_matrix< T > &A)
Matrix square root for upper triangular matrices (from GNU Octave).
Definition: gmm_dense_matrix_functions.h:48
gmm::logm_utri_inplace
void logm_utri_inplace(dense_matrix< T > &S)
Matrix logarithm for upper triangular matrices (from GNU/Octave)
Definition: gmm_dense_matrix_functions.h:137
gmm::logm
void logm(const dense_matrix< T > &A, dense_matrix< T > &LOGMA)
Matrix logarithm (from GNU/Octave)
Definition: gmm_dense_matrix_functions.h:252

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