Carlos Hernandez | 7faaa9f | 2014-08-05 17:53:32 -0700 | [diff] [blame] | 1 | // This file is part of Eigen, a lightweight C++ template library |
| 2 | // for linear algebra. |
| 3 | // |
| 4 | // We used the "A Divide-And-Conquer Algorithm for the Bidiagonal SVD" |
| 5 | // research report written by Ming Gu and Stanley C.Eisenstat |
| 6 | // The code variable names correspond to the names they used in their |
| 7 | // report |
| 8 | // |
| 9 | // Copyright (C) 2013 Gauthier Brun <brun.gauthier@gmail.com> |
| 10 | // Copyright (C) 2013 Nicolas Carre <nicolas.carre@ensimag.fr> |
| 11 | // Copyright (C) 2013 Jean Ceccato <jean.ceccato@ensimag.fr> |
| 12 | // Copyright (C) 2013 Pierre Zoppitelli <pierre.zoppitelli@ensimag.fr> |
| 13 | // |
| 14 | // Source Code Form is subject to the terms of the Mozilla |
| 15 | // Public License v. 2.0. If a copy of the MPL was not distributed |
| 16 | // with this file, You can obtain one at http://mozilla.org/MPL/2.0/. |
| 17 | |
| 18 | #ifndef EIGEN_BDCSVD_H |
| 19 | #define EIGEN_BDCSVD_H |
| 20 | |
| 21 | #define EPSILON 0.0000000000000001 |
| 22 | |
| 23 | #define ALGOSWAP 32 |
| 24 | |
| 25 | namespace Eigen { |
| 26 | /** \ingroup SVD_Module |
| 27 | * |
| 28 | * |
| 29 | * \class BDCSVD |
| 30 | * |
| 31 | * \brief class Bidiagonal Divide and Conquer SVD |
| 32 | * |
| 33 | * \param MatrixType the type of the matrix of which we are computing the SVD decomposition |
| 34 | * We plan to have a very similar interface to JacobiSVD on this class. |
| 35 | * It should be used to speed up the calcul of SVD for big matrices. |
| 36 | */ |
| 37 | template<typename _MatrixType> |
| 38 | class BDCSVD : public SVDBase<_MatrixType> |
| 39 | { |
| 40 | typedef SVDBase<_MatrixType> Base; |
| 41 | |
| 42 | public: |
| 43 | using Base::rows; |
| 44 | using Base::cols; |
| 45 | |
| 46 | typedef _MatrixType MatrixType; |
| 47 | typedef typename MatrixType::Scalar Scalar; |
| 48 | typedef typename NumTraits<typename MatrixType::Scalar>::Real RealScalar; |
| 49 | typedef typename MatrixType::Index Index; |
| 50 | enum { |
| 51 | RowsAtCompileTime = MatrixType::RowsAtCompileTime, |
| 52 | ColsAtCompileTime = MatrixType::ColsAtCompileTime, |
| 53 | DiagSizeAtCompileTime = EIGEN_SIZE_MIN_PREFER_DYNAMIC(RowsAtCompileTime, ColsAtCompileTime), |
| 54 | MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime, |
| 55 | MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime, |
| 56 | MaxDiagSizeAtCompileTime = EIGEN_SIZE_MIN_PREFER_FIXED(MaxRowsAtCompileTime, MaxColsAtCompileTime), |
| 57 | MatrixOptions = MatrixType::Options |
| 58 | }; |
| 59 | |
| 60 | typedef Matrix<Scalar, RowsAtCompileTime, RowsAtCompileTime, |
| 61 | MatrixOptions, MaxRowsAtCompileTime, MaxRowsAtCompileTime> |
| 62 | MatrixUType; |
| 63 | typedef Matrix<Scalar, ColsAtCompileTime, ColsAtCompileTime, |
| 64 | MatrixOptions, MaxColsAtCompileTime, MaxColsAtCompileTime> |
| 65 | MatrixVType; |
| 66 | typedef typename internal::plain_diag_type<MatrixType, RealScalar>::type SingularValuesType; |
| 67 | typedef typename internal::plain_row_type<MatrixType>::type RowType; |
| 68 | typedef typename internal::plain_col_type<MatrixType>::type ColType; |
| 69 | typedef Matrix<Scalar, Dynamic, Dynamic> MatrixX; |
| 70 | typedef Matrix<RealScalar, Dynamic, Dynamic> MatrixXr; |
| 71 | typedef Matrix<RealScalar, Dynamic, 1> VectorType; |
| 72 | |
| 73 | /** \brief Default Constructor. |
| 74 | * |
| 75 | * The default constructor is useful in cases in which the user intends to |
| 76 | * perform decompositions via BDCSVD::compute(const MatrixType&). |
| 77 | */ |
| 78 | BDCSVD() |
| 79 | : SVDBase<_MatrixType>::SVDBase(), |
| 80 | algoswap(ALGOSWAP) |
| 81 | {} |
| 82 | |
| 83 | |
| 84 | /** \brief Default Constructor with memory preallocation |
| 85 | * |
| 86 | * Like the default constructor but with preallocation of the internal data |
| 87 | * according to the specified problem size. |
| 88 | * \sa BDCSVD() |
| 89 | */ |
| 90 | BDCSVD(Index rows, Index cols, unsigned int computationOptions = 0) |
| 91 | : SVDBase<_MatrixType>::SVDBase(), |
| 92 | algoswap(ALGOSWAP) |
| 93 | { |
| 94 | allocate(rows, cols, computationOptions); |
| 95 | } |
| 96 | |
| 97 | /** \brief Constructor performing the decomposition of given matrix. |
| 98 | * |
| 99 | * \param matrix the matrix to decompose |
| 100 | * \param computationOptions optional parameter allowing to specify if you want full or thin U or V unitaries to be computed. |
| 101 | * By default, none is computed. This is a bit - field, the possible bits are #ComputeFullU, #ComputeThinU, |
| 102 | * #ComputeFullV, #ComputeThinV. |
| 103 | * |
| 104 | * Thin unitaries are only available if your matrix type has a Dynamic number of columns (for example MatrixXf). They also are not |
| 105 | * available with the (non - default) FullPivHouseholderQR preconditioner. |
| 106 | */ |
| 107 | BDCSVD(const MatrixType& matrix, unsigned int computationOptions = 0) |
| 108 | : SVDBase<_MatrixType>::SVDBase(), |
| 109 | algoswap(ALGOSWAP) |
| 110 | { |
| 111 | compute(matrix, computationOptions); |
| 112 | } |
| 113 | |
| 114 | ~BDCSVD() |
| 115 | { |
| 116 | } |
| 117 | /** \brief Method performing the decomposition of given matrix using custom options. |
| 118 | * |
| 119 | * \param matrix the matrix to decompose |
| 120 | * \param computationOptions optional parameter allowing to specify if you want full or thin U or V unitaries to be computed. |
| 121 | * By default, none is computed. This is a bit - field, the possible bits are #ComputeFullU, #ComputeThinU, |
| 122 | * #ComputeFullV, #ComputeThinV. |
| 123 | * |
| 124 | * Thin unitaries are only available if your matrix type has a Dynamic number of columns (for example MatrixXf). They also are not |
| 125 | * available with the (non - default) FullPivHouseholderQR preconditioner. |
| 126 | */ |
| 127 | SVDBase<MatrixType>& compute(const MatrixType& matrix, unsigned int computationOptions); |
| 128 | |
| 129 | /** \brief Method performing the decomposition of given matrix using current options. |
| 130 | * |
| 131 | * \param matrix the matrix to decompose |
| 132 | * |
| 133 | * This method uses the current \a computationOptions, as already passed to the constructor or to compute(const MatrixType&, unsigned int). |
| 134 | */ |
| 135 | SVDBase<MatrixType>& compute(const MatrixType& matrix) |
| 136 | { |
| 137 | return compute(matrix, this->m_computationOptions); |
| 138 | } |
| 139 | |
| 140 | void setSwitchSize(int s) |
| 141 | { |
| 142 | eigen_assert(s>3 && "BDCSVD the size of the algo switch has to be greater than 4"); |
| 143 | algoswap = s; |
| 144 | } |
| 145 | |
| 146 | |
| 147 | /** \returns a (least squares) solution of \f$ A x = b \f$ using the current SVD decomposition of A. |
| 148 | * |
| 149 | * \param b the right - hand - side of the equation to solve. |
| 150 | * |
| 151 | * \note Solving requires both U and V to be computed. Thin U and V are enough, there is no need for full U or V. |
| 152 | * |
| 153 | * \note SVD solving is implicitly least - squares. Thus, this method serves both purposes of exact solving and least - squares solving. |
| 154 | * In other words, the returned solution is guaranteed to minimize the Euclidean norm \f$ \Vert A x - b \Vert \f$. |
| 155 | */ |
| 156 | template<typename Rhs> |
| 157 | inline const internal::solve_retval<BDCSVD, Rhs> |
| 158 | solve(const MatrixBase<Rhs>& b) const |
| 159 | { |
| 160 | eigen_assert(this->m_isInitialized && "BDCSVD is not initialized."); |
| 161 | eigen_assert(SVDBase<_MatrixType>::computeU() && SVDBase<_MatrixType>::computeV() && |
| 162 | "BDCSVD::solve() requires both unitaries U and V to be computed (thin unitaries suffice)."); |
| 163 | return internal::solve_retval<BDCSVD, Rhs>(*this, b.derived()); |
| 164 | } |
| 165 | |
| 166 | |
| 167 | const MatrixUType& matrixU() const |
| 168 | { |
| 169 | eigen_assert(this->m_isInitialized && "SVD is not initialized."); |
| 170 | if (isTranspose){ |
| 171 | eigen_assert(this->computeV() && "This SVD decomposition didn't compute U. Did you ask for it?"); |
| 172 | return this->m_matrixV; |
| 173 | } |
| 174 | else |
| 175 | { |
| 176 | eigen_assert(this->computeU() && "This SVD decomposition didn't compute U. Did you ask for it?"); |
| 177 | return this->m_matrixU; |
| 178 | } |
| 179 | |
| 180 | } |
| 181 | |
| 182 | |
| 183 | const MatrixVType& matrixV() const |
| 184 | { |
| 185 | eigen_assert(this->m_isInitialized && "SVD is not initialized."); |
| 186 | if (isTranspose){ |
| 187 | eigen_assert(this->computeU() && "This SVD decomposition didn't compute V. Did you ask for it?"); |
| 188 | return this->m_matrixU; |
| 189 | } |
| 190 | else |
| 191 | { |
| 192 | eigen_assert(this->computeV() && "This SVD decomposition didn't compute V. Did you ask for it?"); |
| 193 | return this->m_matrixV; |
| 194 | } |
| 195 | } |
| 196 | |
| 197 | private: |
| 198 | void allocate(Index rows, Index cols, unsigned int computationOptions); |
| 199 | void divide (Index firstCol, Index lastCol, Index firstRowW, |
| 200 | Index firstColW, Index shift); |
| 201 | void deflation43(Index firstCol, Index shift, Index i, Index size); |
| 202 | void deflation44(Index firstColu , Index firstColm, Index firstRowW, Index firstColW, Index i, Index j, Index size); |
| 203 | void deflation(Index firstCol, Index lastCol, Index k, Index firstRowW, Index firstColW, Index shift); |
| 204 | void copyUV(MatrixXr naiveU, MatrixXr naiveV, MatrixX householderU, MatrixX houseHolderV); |
| 205 | |
| 206 | protected: |
| 207 | MatrixXr m_naiveU, m_naiveV; |
| 208 | MatrixXr m_computed; |
| 209 | Index nRec; |
| 210 | int algoswap; |
| 211 | bool isTranspose, compU, compV; |
| 212 | |
| 213 | }; //end class BDCSVD |
| 214 | |
| 215 | |
| 216 | // Methode to allocate ans initialize matrix and attributs |
| 217 | template<typename MatrixType> |
| 218 | void BDCSVD<MatrixType>::allocate(Index rows, Index cols, unsigned int computationOptions) |
| 219 | { |
| 220 | isTranspose = (cols > rows); |
| 221 | if (SVDBase<MatrixType>::allocate(rows, cols, computationOptions)) return; |
| 222 | m_computed = MatrixXr::Zero(this->m_diagSize + 1, this->m_diagSize ); |
| 223 | if (isTranspose){ |
| 224 | compU = this->computeU(); |
| 225 | compV = this->computeV(); |
| 226 | } |
| 227 | else |
| 228 | { |
| 229 | compV = this->computeU(); |
| 230 | compU = this->computeV(); |
| 231 | } |
| 232 | if (compU) m_naiveU = MatrixXr::Zero(this->m_diagSize + 1, this->m_diagSize + 1 ); |
| 233 | else m_naiveU = MatrixXr::Zero(2, this->m_diagSize + 1 ); |
| 234 | |
| 235 | if (compV) m_naiveV = MatrixXr::Zero(this->m_diagSize, this->m_diagSize); |
| 236 | |
| 237 | |
| 238 | //should be changed for a cleaner implementation |
| 239 | if (isTranspose){ |
| 240 | bool aux; |
| 241 | if (this->computeU()||this->computeV()){ |
| 242 | aux = this->m_computeFullU; |
| 243 | this->m_computeFullU = this->m_computeFullV; |
| 244 | this->m_computeFullV = aux; |
| 245 | aux = this->m_computeThinU; |
| 246 | this->m_computeThinU = this->m_computeThinV; |
| 247 | this->m_computeThinV = aux; |
| 248 | } |
| 249 | } |
| 250 | }// end allocate |
| 251 | |
| 252 | // Methode which compute the BDCSVD for the int |
| 253 | template<> |
| 254 | SVDBase<Matrix<int, Dynamic, Dynamic> >& |
| 255 | BDCSVD<Matrix<int, Dynamic, Dynamic> >::compute(const MatrixType& matrix, unsigned int computationOptions) { |
| 256 | allocate(matrix.rows(), matrix.cols(), computationOptions); |
| 257 | this->m_nonzeroSingularValues = 0; |
| 258 | m_computed = Matrix<int, Dynamic, Dynamic>::Zero(rows(), cols()); |
| 259 | for (int i=0; i<this->m_diagSize; i++) { |
| 260 | this->m_singularValues.coeffRef(i) = 0; |
| 261 | } |
| 262 | if (this->m_computeFullU) this->m_matrixU = Matrix<int, Dynamic, Dynamic>::Zero(rows(), rows()); |
| 263 | if (this->m_computeFullV) this->m_matrixV = Matrix<int, Dynamic, Dynamic>::Zero(cols(), cols()); |
| 264 | this->m_isInitialized = true; |
| 265 | return *this; |
| 266 | } |
| 267 | |
| 268 | |
| 269 | // Methode which compute the BDCSVD |
| 270 | template<typename MatrixType> |
| 271 | SVDBase<MatrixType>& |
| 272 | BDCSVD<MatrixType>::compute(const MatrixType& matrix, unsigned int computationOptions) |
| 273 | { |
| 274 | allocate(matrix.rows(), matrix.cols(), computationOptions); |
| 275 | using std::abs; |
| 276 | |
| 277 | //**** step 1 Bidiagonalization isTranspose = (matrix.cols()>matrix.rows()) ; |
| 278 | MatrixType copy; |
| 279 | if (isTranspose) copy = matrix.adjoint(); |
| 280 | else copy = matrix; |
| 281 | |
| 282 | internal::UpperBidiagonalization<MatrixX > bid(copy); |
| 283 | |
| 284 | //**** step 2 Divide |
| 285 | // this is ugly and has to be redone (care of complex cast) |
| 286 | MatrixXr temp; |
| 287 | temp = bid.bidiagonal().toDenseMatrix().transpose(); |
| 288 | m_computed.setZero(); |
| 289 | for (int i=0; i<this->m_diagSize - 1; i++) { |
| 290 | m_computed(i, i) = temp(i, i); |
| 291 | m_computed(i + 1, i) = temp(i + 1, i); |
| 292 | } |
| 293 | m_computed(this->m_diagSize - 1, this->m_diagSize - 1) = temp(this->m_diagSize - 1, this->m_diagSize - 1); |
| 294 | divide(0, this->m_diagSize - 1, 0, 0, 0); |
| 295 | |
| 296 | //**** step 3 copy |
| 297 | for (int i=0; i<this->m_diagSize; i++) { |
| 298 | RealScalar a = abs(m_computed.coeff(i, i)); |
| 299 | this->m_singularValues.coeffRef(i) = a; |
| 300 | if (a == 0){ |
| 301 | this->m_nonzeroSingularValues = i; |
| 302 | break; |
| 303 | } |
| 304 | else if (i == this->m_diagSize - 1) |
| 305 | { |
| 306 | this->m_nonzeroSingularValues = i + 1; |
| 307 | break; |
| 308 | } |
| 309 | } |
| 310 | copyUV(m_naiveV, m_naiveU, bid.householderU(), bid.householderV()); |
| 311 | this->m_isInitialized = true; |
| 312 | return *this; |
| 313 | }// end compute |
| 314 | |
| 315 | |
| 316 | template<typename MatrixType> |
| 317 | void BDCSVD<MatrixType>::copyUV(MatrixXr naiveU, MatrixXr naiveV, MatrixX householderU, MatrixX householderV){ |
| 318 | if (this->computeU()){ |
| 319 | MatrixX temp = MatrixX::Zero(naiveU.rows(), naiveU.cols()); |
| 320 | temp.real() = naiveU; |
| 321 | if (this->m_computeThinU){ |
| 322 | this->m_matrixU = MatrixX::Identity(householderU.cols(), this->m_nonzeroSingularValues ); |
| 323 | this->m_matrixU.block(0, 0, this->m_diagSize, this->m_nonzeroSingularValues) = |
| 324 | temp.block(0, 0, this->m_diagSize, this->m_nonzeroSingularValues); |
| 325 | this->m_matrixU = householderU * this->m_matrixU ; |
| 326 | } |
| 327 | else |
| 328 | { |
| 329 | this->m_matrixU = MatrixX::Identity(householderU.cols(), householderU.cols()); |
| 330 | this->m_matrixU.block(0, 0, this->m_diagSize, this->m_diagSize) = temp.block(0, 0, this->m_diagSize, this->m_diagSize); |
| 331 | this->m_matrixU = householderU * this->m_matrixU ; |
| 332 | } |
| 333 | } |
| 334 | if (this->computeV()){ |
| 335 | MatrixX temp = MatrixX::Zero(naiveV.rows(), naiveV.cols()); |
| 336 | temp.real() = naiveV; |
| 337 | if (this->m_computeThinV){ |
| 338 | this->m_matrixV = MatrixX::Identity(householderV.cols(),this->m_nonzeroSingularValues ); |
| 339 | this->m_matrixV.block(0, 0, this->m_nonzeroSingularValues, this->m_nonzeroSingularValues) = |
| 340 | temp.block(0, 0, this->m_nonzeroSingularValues, this->m_nonzeroSingularValues); |
| 341 | this->m_matrixV = householderV * this->m_matrixV ; |
| 342 | } |
| 343 | else |
| 344 | { |
| 345 | this->m_matrixV = MatrixX::Identity(householderV.cols(), householderV.cols()); |
| 346 | this->m_matrixV.block(0, 0, this->m_diagSize, this->m_diagSize) = temp.block(0, 0, this->m_diagSize, this->m_diagSize); |
| 347 | this->m_matrixV = householderV * this->m_matrixV; |
| 348 | } |
| 349 | } |
| 350 | } |
| 351 | |
| 352 | // The divide algorithm is done "in place", we are always working on subsets of the same matrix. The divide methods takes as argument the |
| 353 | // place of the submatrix we are currently working on. |
| 354 | |
| 355 | //@param firstCol : The Index of the first column of the submatrix of m_computed and for m_naiveU; |
| 356 | //@param lastCol : The Index of the last column of the submatrix of m_computed and for m_naiveU; |
| 357 | // lastCol + 1 - firstCol is the size of the submatrix. |
| 358 | //@param firstRowW : The Index of the first row of the matrix W that we are to change. (see the reference paper section 1 for more information on W) |
| 359 | //@param firstRowW : Same as firstRowW with the column. |
| 360 | //@param shift : Each time one takes the left submatrix, one must add 1 to the shift. Why? Because! We actually want the last column of the U submatrix |
| 361 | // to become the first column (*coeff) and to shift all the other columns to the right. There are more details on the reference paper. |
| 362 | template<typename MatrixType> |
| 363 | void BDCSVD<MatrixType>::divide (Index firstCol, Index lastCol, Index firstRowW, |
| 364 | Index firstColW, Index shift) |
| 365 | { |
| 366 | // requires nbRows = nbCols + 1; |
| 367 | using std::pow; |
| 368 | using std::sqrt; |
| 369 | using std::abs; |
| 370 | const Index n = lastCol - firstCol + 1; |
| 371 | const Index k = n/2; |
| 372 | RealScalar alphaK; |
| 373 | RealScalar betaK; |
| 374 | RealScalar r0; |
| 375 | RealScalar lambda, phi, c0, s0; |
| 376 | MatrixXr l, f; |
| 377 | // We use the other algorithm which is more efficient for small |
| 378 | // matrices. |
| 379 | if (n < algoswap){ |
| 380 | JacobiSVD<MatrixXr> b(m_computed.block(firstCol, firstCol, n + 1, n), |
| 381 | ComputeFullU | (ComputeFullV * compV)) ; |
| 382 | if (compU) m_naiveU.block(firstCol, firstCol, n + 1, n + 1).real() << b.matrixU(); |
| 383 | else |
| 384 | { |
| 385 | m_naiveU.row(0).segment(firstCol, n + 1).real() << b.matrixU().row(0); |
| 386 | m_naiveU.row(1).segment(firstCol, n + 1).real() << b.matrixU().row(n); |
| 387 | } |
| 388 | if (compV) m_naiveV.block(firstRowW, firstColW, n, n).real() << b.matrixV(); |
| 389 | m_computed.block(firstCol + shift, firstCol + shift, n + 1, n).setZero(); |
| 390 | for (int i=0; i<n; i++) |
| 391 | { |
| 392 | m_computed(firstCol + shift + i, firstCol + shift +i) = b.singularValues().coeffRef(i); |
| 393 | } |
| 394 | return; |
| 395 | } |
| 396 | // We use the divide and conquer algorithm |
| 397 | alphaK = m_computed(firstCol + k, firstCol + k); |
| 398 | betaK = m_computed(firstCol + k + 1, firstCol + k); |
| 399 | // The divide must be done in that order in order to have good results. Divide change the data inside the submatrices |
| 400 | // and the divide of the right submatrice reads one column of the left submatrice. That's why we need to treat the |
| 401 | // right submatrix before the left one. |
| 402 | divide(k + 1 + firstCol, lastCol, k + 1 + firstRowW, k + 1 + firstColW, shift); |
| 403 | divide(firstCol, k - 1 + firstCol, firstRowW, firstColW + 1, shift + 1); |
| 404 | if (compU) |
| 405 | { |
| 406 | lambda = m_naiveU(firstCol + k, firstCol + k); |
| 407 | phi = m_naiveU(firstCol + k + 1, lastCol + 1); |
| 408 | } |
| 409 | else |
| 410 | { |
| 411 | lambda = m_naiveU(1, firstCol + k); |
| 412 | phi = m_naiveU(0, lastCol + 1); |
| 413 | } |
| 414 | r0 = sqrt((abs(alphaK * lambda) * abs(alphaK * lambda)) |
| 415 | + abs(betaK * phi) * abs(betaK * phi)); |
| 416 | if (compU) |
| 417 | { |
| 418 | l = m_naiveU.row(firstCol + k).segment(firstCol, k); |
| 419 | f = m_naiveU.row(firstCol + k + 1).segment(firstCol + k + 1, n - k - 1); |
| 420 | } |
| 421 | else |
| 422 | { |
| 423 | l = m_naiveU.row(1).segment(firstCol, k); |
| 424 | f = m_naiveU.row(0).segment(firstCol + k + 1, n - k - 1); |
| 425 | } |
| 426 | if (compV) m_naiveV(firstRowW+k, firstColW) = 1; |
| 427 | if (r0 == 0) |
| 428 | { |
| 429 | c0 = 1; |
| 430 | s0 = 0; |
| 431 | } |
| 432 | else |
| 433 | { |
| 434 | c0 = alphaK * lambda / r0; |
| 435 | s0 = betaK * phi / r0; |
| 436 | } |
| 437 | if (compU) |
| 438 | { |
| 439 | MatrixXr q1 (m_naiveU.col(firstCol + k).segment(firstCol, k + 1)); |
| 440 | // we shiftW Q1 to the right |
| 441 | for (Index i = firstCol + k - 1; i >= firstCol; i--) |
| 442 | { |
| 443 | m_naiveU.col(i + 1).segment(firstCol, k + 1) << m_naiveU.col(i).segment(firstCol, k + 1); |
| 444 | } |
| 445 | // we shift q1 at the left with a factor c0 |
| 446 | m_naiveU.col(firstCol).segment( firstCol, k + 1) << (q1 * c0); |
| 447 | // last column = q1 * - s0 |
| 448 | m_naiveU.col(lastCol + 1).segment(firstCol, k + 1) << (q1 * ( - s0)); |
| 449 | // first column = q2 * s0 |
| 450 | m_naiveU.col(firstCol).segment(firstCol + k + 1, n - k) << |
| 451 | m_naiveU.col(lastCol + 1).segment(firstCol + k + 1, n - k) *s0; |
| 452 | // q2 *= c0 |
| 453 | m_naiveU.col(lastCol + 1).segment(firstCol + k + 1, n - k) *= c0; |
| 454 | } |
| 455 | else |
| 456 | { |
| 457 | RealScalar q1 = (m_naiveU(0, firstCol + k)); |
| 458 | // we shift Q1 to the right |
| 459 | for (Index i = firstCol + k - 1; i >= firstCol; i--) |
| 460 | { |
| 461 | m_naiveU(0, i + 1) = m_naiveU(0, i); |
| 462 | } |
| 463 | // we shift q1 at the left with a factor c0 |
| 464 | m_naiveU(0, firstCol) = (q1 * c0); |
| 465 | // last column = q1 * - s0 |
| 466 | m_naiveU(0, lastCol + 1) = (q1 * ( - s0)); |
| 467 | // first column = q2 * s0 |
| 468 | m_naiveU(1, firstCol) = m_naiveU(1, lastCol + 1) *s0; |
| 469 | // q2 *= c0 |
| 470 | m_naiveU(1, lastCol + 1) *= c0; |
| 471 | m_naiveU.row(1).segment(firstCol + 1, k).setZero(); |
| 472 | m_naiveU.row(0).segment(firstCol + k + 1, n - k - 1).setZero(); |
| 473 | } |
| 474 | m_computed(firstCol + shift, firstCol + shift) = r0; |
| 475 | m_computed.col(firstCol + shift).segment(firstCol + shift + 1, k) << alphaK * l.transpose().real(); |
| 476 | m_computed.col(firstCol + shift).segment(firstCol + shift + k + 1, n - k - 1) << betaK * f.transpose().real(); |
| 477 | |
| 478 | |
| 479 | // the line below do the deflation of the matrix for the third part of the algorithm |
| 480 | // Here the deflation is commented because the third part of the algorithm is not implemented |
| 481 | // the third part of the algorithm is a fast SVD on the matrix m_computed which works thanks to the deflation |
| 482 | |
| 483 | deflation(firstCol, lastCol, k, firstRowW, firstColW, shift); |
| 484 | |
| 485 | // Third part of the algorithm, since the real third part of the algorithm is not implemeted we use a JacobiSVD |
| 486 | JacobiSVD<MatrixXr> res= JacobiSVD<MatrixXr>(m_computed.block(firstCol + shift, firstCol +shift, n + 1, n), |
| 487 | ComputeFullU | (ComputeFullV * compV)) ; |
| 488 | if (compU) m_naiveU.block(firstCol, firstCol, n + 1, n + 1) *= res.matrixU(); |
| 489 | else m_naiveU.block(0, firstCol, 2, n + 1) *= res.matrixU(); |
| 490 | |
| 491 | if (compV) m_naiveV.block(firstRowW, firstColW, n, n) *= res.matrixV(); |
| 492 | m_computed.block(firstCol + shift, firstCol + shift, n, n) << MatrixXr::Zero(n, n); |
| 493 | for (int i=0; i<n; i++) |
| 494 | m_computed(firstCol + shift + i, firstCol + shift +i) = res.singularValues().coeffRef(i); |
| 495 | // end of the third part |
| 496 | |
| 497 | |
| 498 | }// end divide |
| 499 | |
| 500 | |
| 501 | // page 12_13 |
| 502 | // i >= 1, di almost null and zi non null. |
| 503 | // We use a rotation to zero out zi applied to the left of M |
| 504 | template <typename MatrixType> |
| 505 | void BDCSVD<MatrixType>::deflation43(Index firstCol, Index shift, Index i, Index size){ |
| 506 | using std::abs; |
| 507 | using std::sqrt; |
| 508 | using std::pow; |
| 509 | RealScalar c = m_computed(firstCol + shift, firstCol + shift); |
| 510 | RealScalar s = m_computed(i, firstCol + shift); |
| 511 | RealScalar r = sqrt(pow(abs(c), 2) + pow(abs(s), 2)); |
| 512 | if (r == 0){ |
| 513 | m_computed(i, i)=0; |
| 514 | return; |
| 515 | } |
| 516 | c/=r; |
| 517 | s/=r; |
| 518 | m_computed(firstCol + shift, firstCol + shift) = r; |
| 519 | m_computed(i, firstCol + shift) = 0; |
| 520 | m_computed(i, i) = 0; |
| 521 | if (compU){ |
| 522 | m_naiveU.col(firstCol).segment(firstCol,size) = |
| 523 | c * m_naiveU.col(firstCol).segment(firstCol, size) - |
| 524 | s * m_naiveU.col(i).segment(firstCol, size) ; |
| 525 | |
| 526 | m_naiveU.col(i).segment(firstCol, size) = |
| 527 | (c + s*s/c) * m_naiveU.col(i).segment(firstCol, size) + |
| 528 | (s/c) * m_naiveU.col(firstCol).segment(firstCol,size); |
| 529 | } |
| 530 | }// end deflation 43 |
| 531 | |
| 532 | |
| 533 | // page 13 |
| 534 | // i,j >= 1, i != j and |di - dj| < epsilon * norm2(M) |
| 535 | // We apply two rotations to have zj = 0; |
| 536 | template <typename MatrixType> |
| 537 | void BDCSVD<MatrixType>::deflation44(Index firstColu , Index firstColm, Index firstRowW, Index firstColW, Index i, Index j, Index size){ |
| 538 | using std::abs; |
| 539 | using std::sqrt; |
| 540 | using std::conj; |
| 541 | using std::pow; |
| 542 | RealScalar c = m_computed(firstColm, firstColm + j - 1); |
| 543 | RealScalar s = m_computed(firstColm, firstColm + i - 1); |
| 544 | RealScalar r = sqrt(pow(abs(c), 2) + pow(abs(s), 2)); |
| 545 | if (r==0){ |
| 546 | m_computed(firstColm + i, firstColm + i) = m_computed(firstColm + j, firstColm + j); |
| 547 | return; |
| 548 | } |
| 549 | c/=r; |
| 550 | s/=r; |
| 551 | m_computed(firstColm + i, firstColm) = r; |
| 552 | m_computed(firstColm + i, firstColm + i) = m_computed(firstColm + j, firstColm + j); |
| 553 | m_computed(firstColm + j, firstColm) = 0; |
| 554 | if (compU){ |
| 555 | m_naiveU.col(firstColu + i).segment(firstColu, size) = |
| 556 | c * m_naiveU.col(firstColu + i).segment(firstColu, size) - |
| 557 | s * m_naiveU.col(firstColu + j).segment(firstColu, size) ; |
| 558 | |
| 559 | m_naiveU.col(firstColu + j).segment(firstColu, size) = |
| 560 | (c + s*s/c) * m_naiveU.col(firstColu + j).segment(firstColu, size) + |
| 561 | (s/c) * m_naiveU.col(firstColu + i).segment(firstColu, size); |
| 562 | } |
| 563 | if (compV){ |
| 564 | m_naiveV.col(firstColW + i).segment(firstRowW, size - 1) = |
| 565 | c * m_naiveV.col(firstColW + i).segment(firstRowW, size - 1) + |
| 566 | s * m_naiveV.col(firstColW + j).segment(firstRowW, size - 1) ; |
| 567 | |
| 568 | m_naiveV.col(firstColW + j).segment(firstRowW, size - 1) = |
| 569 | (c + s*s/c) * m_naiveV.col(firstColW + j).segment(firstRowW, size - 1) - |
| 570 | (s/c) * m_naiveV.col(firstColW + i).segment(firstRowW, size - 1); |
| 571 | } |
| 572 | }// end deflation 44 |
| 573 | |
| 574 | |
| 575 | |
| 576 | template <typename MatrixType> |
| 577 | void BDCSVD<MatrixType>::deflation(Index firstCol, Index lastCol, Index k, Index firstRowW, Index firstColW, Index shift){ |
| 578 | //condition 4.1 |
| 579 | RealScalar EPS = EPSILON * (std::max<RealScalar>(m_computed(firstCol + shift + 1, firstCol + shift + 1), m_computed(firstCol + k, firstCol + k))); |
| 580 | const Index length = lastCol + 1 - firstCol; |
| 581 | if (m_computed(firstCol + shift, firstCol + shift) < EPS){ |
| 582 | m_computed(firstCol + shift, firstCol + shift) = EPS; |
| 583 | } |
| 584 | //condition 4.2 |
| 585 | for (Index i=firstCol + shift + 1;i<=lastCol + shift;i++){ |
| 586 | if (std::abs(m_computed(i, firstCol + shift)) < EPS){ |
| 587 | m_computed(i, firstCol + shift) = 0; |
| 588 | } |
| 589 | } |
| 590 | |
| 591 | //condition 4.3 |
| 592 | for (Index i=firstCol + shift + 1;i<=lastCol + shift; i++){ |
| 593 | if (m_computed(i, i) < EPS){ |
| 594 | deflation43(firstCol, shift, i, length); |
| 595 | } |
| 596 | } |
| 597 | |
| 598 | //condition 4.4 |
| 599 | |
| 600 | Index i=firstCol + shift + 1, j=firstCol + shift + k + 1; |
| 601 | //we stock the final place of each line |
| 602 | Index *permutation = new Index[length]; |
| 603 | |
| 604 | for (Index p =1; p < length; p++) { |
| 605 | if (i> firstCol + shift + k){ |
| 606 | permutation[p] = j; |
| 607 | j++; |
| 608 | } else if (j> lastCol + shift) |
| 609 | { |
| 610 | permutation[p] = i; |
| 611 | i++; |
| 612 | } |
| 613 | else |
| 614 | { |
| 615 | if (m_computed(i, i) < m_computed(j, j)){ |
| 616 | permutation[p] = j; |
| 617 | j++; |
| 618 | } |
| 619 | else |
| 620 | { |
| 621 | permutation[p] = i; |
| 622 | i++; |
| 623 | } |
| 624 | } |
| 625 | } |
| 626 | //we do the permutation |
| 627 | RealScalar aux; |
| 628 | //we stock the current index of each col |
| 629 | //and the column of each index |
| 630 | Index *realInd = new Index[length]; |
| 631 | Index *realCol = new Index[length]; |
| 632 | for (int pos = 0; pos< length; pos++){ |
| 633 | realCol[pos] = pos + firstCol + shift; |
| 634 | realInd[pos] = pos; |
| 635 | } |
| 636 | const Index Zero = firstCol + shift; |
| 637 | VectorType temp; |
| 638 | for (int i = 1; i < length - 1; i++){ |
| 639 | const Index I = i + Zero; |
| 640 | const Index realI = realInd[i]; |
| 641 | const Index j = permutation[length - i] - Zero; |
| 642 | const Index J = realCol[j]; |
| 643 | |
| 644 | //diag displace |
| 645 | aux = m_computed(I, I); |
| 646 | m_computed(I, I) = m_computed(J, J); |
| 647 | m_computed(J, J) = aux; |
| 648 | |
| 649 | //firstrow displace |
| 650 | aux = m_computed(I, Zero); |
| 651 | m_computed(I, Zero) = m_computed(J, Zero); |
| 652 | m_computed(J, Zero) = aux; |
| 653 | |
| 654 | // change columns |
| 655 | if (compU) { |
| 656 | temp = m_naiveU.col(I - shift).segment(firstCol, length + 1); |
| 657 | m_naiveU.col(I - shift).segment(firstCol, length + 1) << |
| 658 | m_naiveU.col(J - shift).segment(firstCol, length + 1); |
| 659 | m_naiveU.col(J - shift).segment(firstCol, length + 1) << temp; |
| 660 | } |
| 661 | else |
| 662 | { |
| 663 | temp = m_naiveU.col(I - shift).segment(0, 2); |
| 664 | m_naiveU.col(I - shift).segment(0, 2) << |
| 665 | m_naiveU.col(J - shift).segment(0, 2); |
| 666 | m_naiveU.col(J - shift).segment(0, 2) << temp; |
| 667 | } |
| 668 | if (compV) { |
| 669 | const Index CWI = I + firstColW - Zero; |
| 670 | const Index CWJ = J + firstColW - Zero; |
| 671 | temp = m_naiveV.col(CWI).segment(firstRowW, length); |
| 672 | m_naiveV.col(CWI).segment(firstRowW, length) << m_naiveV.col(CWJ).segment(firstRowW, length); |
| 673 | m_naiveV.col(CWJ).segment(firstRowW, length) << temp; |
| 674 | } |
| 675 | |
| 676 | //update real pos |
| 677 | realCol[realI] = J; |
| 678 | realCol[j] = I; |
| 679 | realInd[J - Zero] = realI; |
| 680 | realInd[I - Zero] = j; |
| 681 | } |
| 682 | for (Index i = firstCol + shift + 1; i<lastCol + shift;i++){ |
| 683 | if ((m_computed(i + 1, i + 1) - m_computed(i, i)) < EPS){ |
| 684 | deflation44(firstCol , |
| 685 | firstCol + shift, |
| 686 | firstRowW, |
| 687 | firstColW, |
| 688 | i - Zero, |
| 689 | i + 1 - Zero, |
| 690 | length); |
| 691 | } |
| 692 | } |
| 693 | delete [] permutation; |
| 694 | delete [] realInd; |
| 695 | delete [] realCol; |
| 696 | |
| 697 | }//end deflation |
| 698 | |
| 699 | |
| 700 | namespace internal{ |
| 701 | |
| 702 | template<typename _MatrixType, typename Rhs> |
| 703 | struct solve_retval<BDCSVD<_MatrixType>, Rhs> |
| 704 | : solve_retval_base<BDCSVD<_MatrixType>, Rhs> |
| 705 | { |
| 706 | typedef BDCSVD<_MatrixType> BDCSVDType; |
| 707 | EIGEN_MAKE_SOLVE_HELPERS(BDCSVDType, Rhs) |
| 708 | |
| 709 | template<typename Dest> void evalTo(Dest& dst) const |
| 710 | { |
| 711 | eigen_assert(rhs().rows() == dec().rows()); |
| 712 | // A = U S V^* |
| 713 | // So A^{ - 1} = V S^{ - 1} U^* |
| 714 | Index diagSize = (std::min)(dec().rows(), dec().cols()); |
| 715 | typename BDCSVDType::SingularValuesType invertedSingVals(diagSize); |
| 716 | Index nonzeroSingVals = dec().nonzeroSingularValues(); |
| 717 | invertedSingVals.head(nonzeroSingVals) = dec().singularValues().head(nonzeroSingVals).array().inverse(); |
| 718 | invertedSingVals.tail(diagSize - nonzeroSingVals).setZero(); |
| 719 | |
| 720 | dst = dec().matrixV().leftCols(diagSize) |
| 721 | * invertedSingVals.asDiagonal() |
| 722 | * dec().matrixU().leftCols(diagSize).adjoint() |
| 723 | * rhs(); |
| 724 | return; |
| 725 | } |
| 726 | }; |
| 727 | |
| 728 | } //end namespace internal |
| 729 | |
| 730 | /** \svd_module |
| 731 | * |
| 732 | * \return the singular value decomposition of \c *this computed by |
| 733 | * BDC Algorithm |
| 734 | * |
| 735 | * \sa class BDCSVD |
| 736 | */ |
| 737 | /* |
| 738 | template<typename Derived> |
| 739 | BDCSVD<typename MatrixBase<Derived>::PlainObject> |
| 740 | MatrixBase<Derived>::bdcSvd(unsigned int computationOptions) const |
| 741 | { |
| 742 | return BDCSVD<PlainObject>(*this, computationOptions); |
| 743 | } |
| 744 | */ |
| 745 | |
| 746 | } // end namespace Eigen |
| 747 | |
| 748 | #endif |