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| vigra/linear_solve.hxx |
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    00001 /************************************************************************/
    00002 /*                                                                      */
    00003 /*        Copyright 2004 by Gunnar Kedenburg and Ullrich Koethe         */
    00004 /*       Cognitive Systems Group, University of Hamburg, Germany        */
    00005 /*                                                                      */
    00006 /*    This file is part of the VIGRA computer vision library.           */
    00007 /*    ( Version 1.4.0, Dec 21 2005 )                                    */
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    00011 /*        koethe@informatik.uni-hamburg.de          or                  */
    00012 /*        vigra@kogs1.informatik.uni-hamburg.de                         */
    00013 /*                                                                      */
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    00022 /*                                                                      */
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    00025 /*    Software.                                                         */
    00026 /*                                                                      */
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    00037
    00038
    00039 #ifndef VIGRA_LINEAR_SOLVE_HXX
    00040 #define VIGRA_LINEAR_SOLVE_HXX
    00041
    00042 #include "vigra/matrix.hxx"
    00043
    00044
    00045 namespace vigra
    00046 {
    00047
    00048 namespace linalg
    00049 {
    00050
    00051 /** \addtogroup LinearAlgebraFunctions Matrix functions
    00052  */
    00053 //@{
    00054     /** invert square matrix \a v.
    00055         The result is written into \a r which must have the same shape.
    00056         The inverse is calculated by means of QR decomposition. If \a v
    00057         is not invertible, <tt>vigra::PreconditionViolation</tt> exception is thrown.
    00058
    00059     <b>\#include</b> "<a href="linear__solve_8hxx-source.html">vigra/linear__solve.hxx</a>" or<br>
    00060     <b>\#include</b> "<a href="linear__algebra_8hxx-source.html">vigra/linear_algebra.hxx</a>"<br>
    00061         Namespaces: vigra and vigra::linalg
    00062      */
    00063 template <class T, class C1, class C2>
    00064 void inverse(const MultiArrayView<2, T, C1> &v, MultiArrayView<2, T, C2> &r)
    00065 {
    00066     const unsigned int n = rowCount(r);
    00067     vigra_precondition(n == columnCount(v) && n == rowCount(v) && n == columnCount(r),
    00068        "inverse(): matrices must be square.");
    00069     vigra_precondition(linearSolve(v, identityMatrix<T>(n), r),
    00070         "inverse(): matrix is not invertible.");
    00071 }
    00072
    00073     /** create the inverse of square matrix \a v.
    00074         The result is returned as a temporary matrix.
    00075         The inverse is calculated by means of QR decomposition. If \a v
    00076         is not invertible, <tt>vigra::PreconditionViolation</tt> exception is thrown.
    00077         Usage:
    00078
    00079         \code
    00080         vigra::Matrix<double> v(n, n);
    00081         v = ...;
    00082
    00083         vigra::Matrix<double> m = inverse(v);
    00084         \endcode
    00085
    00086     <b>\#include</b> "<a href="linear__solve_8hxx-source.html">vigra/linear__solve.hxx</a>" or<br>
    00087     <b>\#include</b> "<a href="linear__algebra_8hxx-source.html">vigra/linear_algebra.hxx</a>"<br>
    00088         Namespaces: vigra and vigra::linalg
    00089      */
    00090 template <class T, class C>
    00091 TemporaryMatrix<T> inverse(const MultiArrayView<2, T, C> &v)
    00092 {
    00093     const unsigned int n = rowCount(v);
    00094     vigra_precondition(n == columnCount(v),
    00095        "inverse(): matrix must be square.");
    00096     TemporaryMatrix<T> ret = identityMatrix<T>(n);
    00097     vigra_precondition(linearSolve(v, ret, ret),
    00098         "inverse(): matrix is not invertible.");
    00099     return ret;
    00100 }
    00101
    00102 template <class T>
    00103 TemporaryMatrix<T> inverse(const TemporaryMatrix<T> &v)
    00104 {
    00105     const unsigned int n = v.rowCount();
    00106     vigra_precondition(n == v.columnCount(),
    00107        "inverse(): matrix must be square.");
    00108     vigra_precondition(linearSolve(v, identityMatrix<T>(n), v),
    00109         "inverse(): matrix is not invertible.");
    00110     return v;
    00111 }
    00112
    00113     /** QR decomposition.
    00114
    00115         \a a contains the original matrix, results are returned in \a q and \a r, where
    00116         \a q is a orthogonal matrix, and \a r is an uppr triangular matrix, and
    00117         the following relation holds:
    00118
    00119         \code
    00120         assert(a == q * r);
    00121         \endcode
    00122
    00123         This implementation uses householder transformations. It can be applied in-place,
    00124         i.e. <tt>&a == &r</tt> is allowed.
    00125
    00126     <b>\#include</b> "<a href="linear__solve_8hxx-source.html">vigra/linear__solve.hxx</a>" or<br>
    00127     <b>\#include</b> "<a href="linear__algebra_8hxx-source.html">vigra/linear_algebra.hxx</a>"<br>
    00128         Namespaces: vigra and vigra::linalg
    00129      */
    00130 template <class T, class C1, class C2, class C3>
    00131 void qrDecomposition(MultiArrayView<2, T, C1> const & a,
    00132                      MultiArrayView<2, T, C2> &q, MultiArrayView<2, T, C3> &r)
    00133 {
    00134     typedef typename MultiArrayView<2, T, C2>::difference_type MatrixShape;
    00135     typedef typename MultiArray<1, T>::difference_type VectorShape;
    00136
    00137     // the orthogonal matrix q will have as many rows and columns as
    00138     // the original matrix has columns.
    00139     const unsigned int rows = rowCount(a);
    00140     const unsigned int cols = columnCount(a);
    00141     vigra_precondition(cols == columnCount(r) && cols == rowCount(r) &&
    00142                        cols == columnCount(q) && cols == rowCount(q),
    00143                        "qrDecomposition(): Matrix shape mismatch.");
    00144
    00145     identityMatrix(q);
    00146     r.copy(a);   // does nothing if &r == &a
    00147
    00148     for(unsigned int k = 0; (k < cols) && (k < rows - 1); ++k) {
    00149
    00150         const unsigned int rows_left = rows - k;
    00151         const unsigned int cols_left = cols - k;
    00152
    00153         // create a view on the remaining part of r
    00154         MatrixShape rul(k, k);
    00155         MultiArrayView<2, T, C2> rsub = r.subarray(rul, r.shape());
    00156
    00157         // decompose the first row
    00158         MultiArrayView <1, T, C2 > vec = rsub.bindOuter(0);
    00159
    00160         // defining householder vector
    00161         VectorShape ushape(rows_left);
    00162         MultiArray<1, T> u(ushape);
    00163         for(unsigned int i = 0; i < rows_left; ++i)
    00164             u(i) = vec(i);
    00165         u(0) += norm(vec);
    00166
    00167         const T divisor = squaredNorm(u);
    00168         const T scal = (divisor == 0) ? 0.0 : 2.0 / divisor;
    00169
    00170         // apply householder elimination on rsub
    00171         for(unsigned int i = 0; i < cols_left; ++i) {
    00172
    00173             // compute the inner product of the i'th column of rsub with u
    00174             T sum = dot(u, rsub.bindOuter(i));
    00175
    00176             // add rsub*(uu')/(u'u)
    00177             sum *= scal;
    00178             for(unsigned int j = 0; j < rows_left; ++j)
    00179                 rsub(j, i) -= sum * u(j);
    00180         }
    00181
    00182         MatrixShape qul(0, k);
    00183         MultiArrayView <2, T, C3 > qsub = q.subarray(qul, q.shape());
    00184
    00185         // apply the (self-inverse) householder matrix on q
    00186         for(unsigned int i = 0; i < cols; ++i) {
    00187
    00188             // compute the inner product of the i'th row of q with u
    00189             T sum = dot(qsub.bindInner(i), u);
    00190
    00191             // add q*(uu')/(u'u)
    00192             sum *= scal;
    00193             for(unsigned int j = 0; j < rows_left; ++j)
    00194                 qsub(i, j) -= sum * u(j);
    00195         }
    00196     }
    00197 }
    00198
    00199     /** Solve a linear system with right-triangular defining matrix.
    00200
    00201         The square matrix \a a must be a right-triangular coefficient matrix as can,
    00202         for example, be obtained by means of QR decomposition. The column vectors
    00203         in \a b are the right-hand sides of the equation (so, several equations
    00204         with the same coefficients can be solved in one go). The result is returned
    00205         int \a x, whose columns contain the solutions for the correspoinding
    00206         columns of \a b. The number of columns of \a a must equal the number of rows of
    00207         both \a b and \a x, and the number of columns of \a b and \a x must be
    00208         equal. This implementation can be applied in-place, i.e. <tt>&b == &x</tt> is allowed.
    00209
    00210     <b>\#include</b> "<a href="linear__solve_8hxx-source.html">vigra/linear__solve.hxx</a>" or<br>
    00211     <b>\#include</b> "<a href="linear__algebra_8hxx-source.html">vigra/linear_algebra.hxx</a>"<br>
    00212         Namespaces: vigra and vigra::linalg
    00213      */
    00214 template <class T, class C1, class C2, class C3>
    00215 void reverseElimination(const MultiArrayView<2, T, C1> &r, const MultiArrayView<2, T, C2> &b,
    00216                           MultiArrayView<2, T, C3> & x)
    00217 {
    00218     unsigned int m = columnCount(r);
    00219     unsigned int n = columnCount(b);
    00220     vigra_precondition(m == rowCount(r),
    00221         "reverseElimination(): square coefficient matrix required.");
    00222     vigra_precondition(m == rowCount(b) && m == rowCount(x) && n == columnCount(x),
    00223         "reverseElimination(): matrix shape mismatch.");
    00224
    00225     for(unsigned int k = 0; k < n; ++k)
    00226     {
    00227         x(m-1, k) = b(m-1, k) / r(m-1, m-1);
    00228         if(m >= 2)
    00229         {
    00230             for(int i = m-2; i >= 0; --i)
    00231             {
    00232                 // compute the i'th inner product, excluding the diagonal entry.
    00233                 T sum = NumericTraits<T>::zero();
    00234                 for(unsigned int j = i+1; j < m; ++j)
    00235                     sum += r(i, j) * x(j, k);
    00236                 if(r(i, i) != NumericTraits<T>::zero())
    00237                     x(i, k) = (b(i, k) - sum) / r(i, i);
    00238                 else
    00239                     x(i, k) = NumericTraits<T>::zero();
    00240             }
    00241         }
    00242     }
    00243 }
    00244
    00245     /** Solve a linear system.
    00246
    00247         The square matrix \a a is the coefficient matrix, and the column vectors
    00248         in \a b are the right-hand sides of the equation (so, several equations
    00249         with the same coefficients can be solved in one go). The result is returned
    00250         int \a res, whose columns contain the solutions for the correspoinding
    00251         columns of \a b. The number of columns of \a a must equal the number of rows of
    00252         both \a b and \a res, and the number of columns of \a b and \a res must be
    00253         equal. The algorithm uses QR decomposition of \a a. The algorithm returns
    00254         <tt>false</tt> if \a a doesn't have full rank. This implementation  can be
    00255         applied in-place, i.e. <tt>&b == &res</tt> or <tt>&a == &res</tt> are allowed.
    00256
    00257     <b>\#include</b> "<a href="linear__solve_8hxx-source.html">vigra/linear__solve.hxx</a>" or<br>
    00258     <b>\#include</b> "<a href="linear__algebra_8hxx-source.html">vigra/linear_algebra.hxx</a>"<br>
    00259         Namespaces: vigra and vigra::linalg
    00260      */
    00261 template <class T, class C1, class C2, class C3>
    00262 bool linearSolve(const MultiArrayView<2, T, C1> &a, const MultiArrayView<2, T, C2> &b,
    00263                  MultiArrayView<2, T, C3> & res)
    00264 {
    00265     unsigned int acols = columnCount(a);
    00266     unsigned int bcols = columnCount(b);
    00267     vigra_precondition(acols == rowCount(a),
    00268         "linearSolve(): square coefficient matrix required.");
    00269     vigra_precondition(acols == rowCount(b) && acols == rowCount(res) && bcols == columnCount(res),
    00270         "linearSolve(): matrix shape mismatch.");
    00271
    00272     Matrix<T> q(acols, acols), r(a);
    00273     qrDecomposition(r, q, r);
    00274     if(r(acols-1, acols-1) == NumericTraits<T>::zero())
    00275          return false; // a didn't have full rank.
    00276     q.transpose();
    00277     reverseElimination(r, q * b, res);
    00278     return true;
    00279 }
    00280
    00281 //@}
    00282
    00283 } // namespace linalg
    00284
    00285 using linalg::inverse;
    00286 using linalg::linearSolve;
    00287 using linalg::qrDecomposition;
    00288 using linalg::reverseElimination;
    00289
    00290 } // namespace vigra
    00291
    00292
    00293 #endif // VIGRA_LINEAR_SOLVE_HXX

© Ullrich Köthe (koethe@informatik.uni-hamburg.de) Cognitive Systems Group, University of Hamburg, Germany | html generated using doxygen and Python VIGRA 1.4.0 (21 Dec 2005)