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| vigra/eigensystem.hxx |
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    00001 /************************************************************************/
    00002 /*                                                                      */
    00003 /*                  Copyright 2004 by Ullrich Koethe                    */
    00004 /*       Cognitive Systems Group, University of Hamburg, Germany        */
    00005 /*                                                                      */
    00006 /*    This file is part of the VIGRA computer vision library.           */
    00007 /*    ( Version 1.5.0, Dec 07 2006 )                                    */
    00008 /*    The VIGRA Website is                                              */
    00009 /*        http://kogs-www.informatik.uni-hamburg.de/~koethe/vigra/      */
    00010 /*    Please direct questions, bug reports, and contributions to        */
    00011 /*        koethe@informatik.uni-hamburg.de          or                  */
    00012 /*        vigra@kogs1.informatik.uni-hamburg.de                         */
    00013 /*                                                                      */
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    00021 /*    conditions:                                                       */
    00022 /*                                                                      */
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    00025 /*    Software.                                                         */
    00026 /*                                                                      */
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    00035 /*                                                                      */
    00036 /************************************************************************/
    00037
    00038
    00039 #ifndef VIGRA_EIGENSYSTEM_HXX
    00040 #define VIGRA_EIGENSYSTEM_HXX
    00041
    00042 #include <algorithm>
    00043 #include <complex>
    00044 #include "matrix.hxx"
    00045 #include "array_vector.hxx"
    00046 #include "polynomial.hxx"
    00047
    00048 namespace vigra
    00049 {
    00050
    00051 namespace linalg
    00052 {
    00053
    00054 namespace detail
    00055 {
    00056
    00057 // code adapted from JAMA
    00058 // a and b will be overwritten
    00059 template <class T, class C1, class C2>
    00060 void
    00061 housholderTridiagonalization(MultiArrayView<2, T, C1> &a, MultiArrayView<2, T, C2> &b)
    00062 {
    00063     const unsigned int n = rowCount(a);
    00064     vigra_precondition(n == columnCount(a),
    00065         "housholderTridiagonalization(): matrix must be square.");
    00066     vigra_precondition(n == rowCount(b) && 2 <= columnCount(b),
    00067         "housholderTridiagonalization(): matrix size mismatch.");
    00068
    00069     MultiArrayView<1, T, C2> d = b.bindOuter(0);
    00070     MultiArrayView<1, T, C2> e = b.bindOuter(1);
    00071
    00072     for(unsigned int j = 0; j < n; ++j)
    00073     {
    00074         d(j) = a(n-1, j);
    00075     }
    00076
    00077     // Householder reduction to tridiagonalMatrix form.
    00078
    00079     for(int i = n-1; i > 0; --i)
    00080     {
    00081         // Scale to avoid under/overflow.
    00082
    00083         T scale = 0.0;
    00084         T h = 0.0;
    00085         for(int k = 0; k < i; ++k)
    00086         {
    00087             scale = scale + abs(d(k));
    00088         }
    00089         if(scale == 0.0)
    00090         {
    00091             e(i) = d(i-1);
    00092             for(int j = 0; j < i; ++j)
    00093             {
    00094                 d(j) = a(i-1, j);
    00095                 a(i, j) = 0.0;
    00096                 a(j, i) = 0.0;
    00097             }
    00098         }
    00099         else
    00100         {
    00101             // Generate Householder vector.
    00102
    00103             for(int k = 0; k < i; ++k)
    00104             {
    00105                 d(k) /= scale;
    00106                 h += sq(d(k));
    00107             }
    00108             T f = d(i-1);
    00109             T g = VIGRA_CSTD::sqrt(h);
    00110             if(f > 0) {
    00111                 g = -g;
    00112             }
    00113             e(i) = scale * g;
    00114             h -= f * g;
    00115             d(i-1) = f - g;
    00116             for(int j = 0; j < i; ++j)
    00117             {
    00118                e(j) = 0.0;
    00119             }
    00120
    00121             // Apply similarity transformation to remaining columns.
    00122
    00123             for(int j = 0; j < i; ++j)
    00124             {
    00125                f = d(j);
    00126                a(j, i) = f;
    00127                g = e(j) + a(j, j) * f;
    00128                for(int k = j+1; k <= i-1; ++k)
    00129                {
    00130                    g += a(k, j) * d(k);
    00131                    e(k) += a(k, j) * f;
    00132                }
    00133                e(j) = g;
    00134             }
    00135             f = 0.0;
    00136             for(int j = 0; j < i; ++j)
    00137             {
    00138                 e(j) /= h;
    00139                 f += e(j) * d(j);
    00140             }
    00141             T hh = f / (h + h);
    00142             for(int j = 0; j < i; ++j)
    00143             {
    00144                 e(j) -= hh * d(j);
    00145             }
    00146             for(int j = 0; j < i; ++j)
    00147             {
    00148                 f = d(j);
    00149                 g = e(j);
    00150                 for(int k = j; k <= i-1; ++k)
    00151                 {
    00152                     a(k, j) -= (f * e(k) + g * d(k));
    00153                 }
    00154                 d(j) = a(i-1, j);
    00155                 a(i, j) = 0.0;
    00156             }
    00157         }
    00158         d(i) = h;
    00159     }
    00160
    00161     // Accumulate transformations.
    00162
    00163     for(unsigned int i = 0; i < n-1; ++i)
    00164     {
    00165         a(n-1, i) = a(i, i);
    00166         a(i, i) = 1.0;
    00167         T h = d(i+1);
    00168         if(h != 0.0)
    00169         {
    00170             for(unsigned int k = 0; k <= i; ++k)
    00171             {
    00172                 d(k) = a(k, i+1) / h;
    00173             }
    00174             for(unsigned int j = 0; j <= i; ++j)
    00175             {
    00176                 T g = 0.0;
    00177                 for(unsigned int k = 0; k <= i; ++k)
    00178                 {
    00179                     g += a(k, i+1) * a(k, j);
    00180                 }
    00181                 for(unsigned int k = 0; k <= i; ++k)
    00182                 {
    00183                     a(k, j) -= g * d(k);
    00184                 }
    00185             }
    00186         }
    00187         for(unsigned int k = 0; k <= i; ++k)
    00188         {
    00189             a(k, i+1) = 0.0;
    00190         }
    00191     }
    00192     for(unsigned int j = 0; j < n; ++j)
    00193     {
    00194         d(j) = a(n-1, j);
    00195         a(n-1, j) = 0.0;
    00196     }
    00197     a(n-1, n-1) = 1.0;
    00198     e(0) = 0.0;
    00199 }
    00200
    00201 // code adapted from JAMA
    00202 // de and z will be overwritten
    00203 template <class T, class C1, class C2>
    00204 bool
    00205 tridiagonalMatrixEigensystem(MultiArrayView<2, T, C1> &de, MultiArrayView<2, T, C2> &z)
    00206 {
    00207     const unsigned int n = rowCount(z);
    00208     vigra_precondition(n == columnCount(z),
    00209         "tridiagonalMatrixEigensystem(): matrix must be square.");
    00210     vigra_precondition(n == rowCount(de) && 2 <= columnCount(de),
    00211         "tridiagonalMatrixEigensystem(): matrix size mismatch.");
    00212
    00213     MultiArrayView<1, T, C2> d = de.bindOuter(0);
    00214     MultiArrayView<1, T, C2> e = de.bindOuter(1);
    00215
    00216     for(unsigned int i = 1; i < n; i++) {
    00217        e(i-1) = e(i);
    00218     }
    00219     e(n-1) = 0.0;
    00220
    00221     T f = 0.0;
    00222     T tst1 = 0.0;
    00223     T eps = VIGRA_CSTD::pow(2.0,-52.0);
    00224     for(unsigned int l = 0; l < n; ++l)
    00225     {
    00226         // Find small subdiagonalMatrix element
    00227
    00228         tst1 = std::max(tst1, abs(d(l)) + abs(e(l)));
    00229         unsigned int m = l;
    00230
    00231         // Original while-loop from Java code
    00232         while(m < n)
    00233         {
    00234             if(abs(e(m)) <= eps*tst1)
    00235                 break;
    00236             ++m;
    00237         }
    00238
    00239         // If m == l, d(l) is an eigenvalue,
    00240         // otherwise, iterate.
    00241
    00242         if(m > l)
    00243         {
    00244             int iter = 0;
    00245             do
    00246             {
    00247                 if(++iter > 50)
    00248                    return false; // too many iterations
    00249
    00250                 // Compute implicit shift
    00251
    00252                 T g = d(l);
    00253                 T p = (d(l+1) - g) / (2.0 * e(l));
    00254                 T r = hypot(p,1.0);
    00255                 if(p < 0)
    00256                 {
    00257                     r = -r;
    00258                 }
    00259                 d(l) = e(l) / (p + r);
    00260                 d(l+1) = e(l) * (p + r);
    00261                 T dl1 = d(l+1);
    00262                 T h = g - d(l);
    00263                 for(unsigned int i = l+2; i < n; ++i)
    00264                 {
    00265                    d(i) -= h;
    00266                 }
    00267                 f = f + h;
    00268
    00269                 // Implicit QL transformation.
    00270
    00271                 p = d(m);
    00272                 T c = 1.0;
    00273                 T c2 = c;
    00274                 T c3 = c;
    00275                 T el1 = e(l+1);
    00276                 T s = 0.0;
    00277                 T s2 = 0.0;
    00278                 for(int i = m-1; i >= (int)l; --i)
    00279                 {
    00280                     c3 = c2;
    00281                     c2 = c;
    00282                     s2 = s;
    00283                     g = c * e(i);
    00284                     h = c * p;
    00285                     r = hypot(p,e(i));
    00286                     e(i+1) = s * r;
    00287                     s = e(i) / r;
    00288                     c = p / r;
    00289                     p = c * d(i) - s * g;
    00290                     d(i+1) = h + s * (c * g + s * d(i));
    00291
    00292                     // Accumulate transformation.
    00293
    00294                     for(unsigned int k = 0; k < n; ++k)
    00295                     {
    00296                          h = z(k, i+1);
    00297                          z(k, i+1) = s * z(k, i) + c * h;
    00298                         z(k, i) = c * z(k, i) - s * h;
    00299                     }
    00300                 }
    00301                 p = -s * s2 * c3 * el1 * e(l) / dl1;
    00302                 e(l) = s * p;
    00303                 d(l) = c * p;
    00304
    00305                 // Check for convergence.
    00306
    00307             } while(abs(e(l)) > eps*tst1);
    00308         }
    00309         d(l) = d(l) + f;
    00310         e(l) = 0.0;
    00311     }
    00312
    00313     // Sort eigenvalues and corresponding vectors.
    00314
    00315     for(unsigned int i = 0; i < n-1; ++i)
    00316     {
    00317         int k = i;
    00318         T p = abs(d(i));
    00319         for(unsigned int j = i+1; j < n; ++j)
    00320         {
    00321             T p1 = abs(d(j));
    00322             if(p < p1)
    00323             {
    00324                 k = j;
    00325                 p = p1;
    00326             }
    00327         }
    00328         if(k != i)
    00329         {
    00330             std::swap(d(k), d(i));
    00331             for(unsigned int j = 0; j < n; ++j)
    00332             {
    00333                 std::swap(z(j, i), z(j, k));
    00334             }
    00335         }
    00336     }
    00337     return true;
    00338 }
    00339
    00340 // Nonsymmetric reduction to Hessenberg form.
    00341
    00342 template <class T, class C1, class C2>
    00343 void nonsymmetricHessenbergReduction(MultiArrayView<2, T, C1> & H, MultiArrayView<2, T, C2> & V)
    00344 {
    00345     //  This is derived from the Algol procedures orthes and ortran,
    00346     //  by Martin and Wilkinson, Handbook for Auto. Comp.,
    00347     //  Vol.ii-Linear Algebra, and the corresponding
    00348     //  Fortran subroutines in EISPACK.
    00349
    00350     int n = rowCount(H);
    00351     int low = 0;
    00352     int high = n-1;
    00353     ArrayVector<T> ort(n);
    00354
    00355     for(int m = low+1; m <= high-1; ++m)
    00356     {
    00357         // Scale column.
    00358
    00359         T scale = 0.0;
    00360         for(int i = m; i <= high; ++i)
    00361         {
    00362             scale = scale + abs(H(i, m-1));
    00363         }
    00364         if(scale != 0.0)
    00365         {
    00366
    00367             // Compute Householder transformation.
    00368
    00369             T h = 0.0;
    00370             for(int i = high; i >= m; --i)
    00371             {
    00372                 ort[i] = H(i, m-1)/scale;
    00373                 h += sq(ort[i]);
    00374             }
    00375             T g = VIGRA_CSTD::sqrt(h);
    00376             if(ort[m] > 0)
    00377             {
    00378                 g = -g;
    00379             }
    00380             h = h - ort[m] * g;
    00381             ort[m] = ort[m] - g;
    00382
    00383             // Apply Householder similarity transformation
    00384             // H = (I-u*u'/h)*H*(I-u*u')/h)
    00385
    00386             for(int j = m; j < n; ++j)
    00387             {
    00388                 T f = 0.0;
    00389                 for(int i = high; i >= m; --i)
    00390                 {
    00391                     f += ort[i]*H(i, j);
    00392                 }
    00393                 f = f/h;
    00394                 for(int i = m; i <= high; ++i)
    00395                 {
    00396                     H(i, j) -= f*ort[i];
    00397                 }
    00398             }
    00399
    00400             for(int i = 0; i <= high; ++i)
    00401             {
    00402                 T f = 0.0;
    00403                 for(int j = high; j >= m; --j)
    00404                 {
    00405                     f += ort[j]*H(i, j);
    00406                 }
    00407                 f = f/h;
    00408                 for(int j = m; j <= high; ++j)
    00409                 {
    00410                     H(i, j) -= f*ort[j];
    00411                 }
    00412             }
    00413             ort[m] = scale*ort[m];
    00414             H(m, m-1) = scale*g;
    00415         }
    00416     }
    00417
    00418     // Accumulate transformations (Algol's ortran).
    00419
    00420     for(int i = 0; i < n; ++i)
    00421     {
    00422         for(int j = 0; j < n; ++j)
    00423         {
    00424             V(i, j) = (i == j ? 1.0 : 0.0);
    00425         }
    00426     }
    00427
    00428     for(int m = high-1; m >= low+1; --m)
    00429     {
    00430         if(H(m, m-1) != 0.0)
    00431         {
    00432             for(int i = m+1; i <= high; ++i)
    00433             {
    00434                 ort[i] = H(i, m-1);
    00435             }
    00436             for(int j = m; j <= high; ++j)
    00437             {
    00438                 T g = 0.0;
    00439                 for(int i = m; i <= high; ++i)
    00440                 {
    00441                     g += ort[i] * V(i, j);
    00442                 }
    00443                 // Double division avoids possible underflow
    00444                 g = (g / ort[m]) / H(m, m-1);
    00445                 for(int i = m; i <= high; ++i)
    00446                 {
    00447                     V(i, j) += g * ort[i];
    00448                 }
    00449             }
    00450         }
    00451     }
    00452 }
    00453
    00454
    00455 // Complex scalar division.
    00456
    00457 template <class T>
    00458 void cdiv(T xr, T xi, T yr, T yi, T & cdivr, T & cdivi)
    00459 {
    00460     T r,d;
    00461     if(abs(yr) > abs(yi))
    00462     {
    00463         r = yi/yr;
    00464         d = yr + r*yi;
    00465         cdivr = (xr + r*xi)/d;
    00466         cdivi = (xi - r*xr)/d;
    00467     }
    00468     else
    00469     {
    00470         r = yr/yi;
    00471         d = yi + r*yr;
    00472         cdivr = (r*xr + xi)/d;
    00473         cdivi = (r*xi - xr)/d;
    00474     }
    00475 }
    00476
    00477 template <class T, class C>
    00478 int hessenbergQrDecompositionHelper(MultiArrayView<2, T, C> & H, int n, int l, double eps,
    00479              T & p, T & q, T & r, T & s, T & w, T & x, T & y, T & z)
    00480 {
    00481     int m = n-2;
    00482     while(m >= l)
    00483     {
    00484         z = H(m, m);
    00485         r = x - z;
    00486         s = y - z;
    00487         p = (r * s - w) / H(m+1, m) + H(m, m+1);
    00488         q = H(m+1, m+1) - z - r - s;
    00489         r = H(m+2, m+1);
    00490         s = abs(p) + abs(q) + abs(r);
    00491         p = p / s;
    00492         q = q / s;
    00493         r = r / s;
    00494         if(m == l)
    00495         {
    00496             break;
    00497         }
    00498         if(abs(H(m, m-1)) * (abs(q) + abs(r)) <
    00499             eps * (abs(p) * (abs(H(m-1, m-1)) + abs(z) +
    00500             abs(H(m+1, m+1)))))
    00501         {
    00502                 break;
    00503         }
    00504         --m;
    00505     }
    00506     return m;
    00507 }
    00508
    00509
    00510
    00511 // Nonsymmetric reduction from Hessenberg to real Schur form.
    00512
    00513 template <class T, class C1, class C2, class C3>
    00514 bool hessenbergQrDecomposition(MultiArrayView<2, T, C1> & H, MultiArrayView<2, T, C2> & V,
    00515                                      MultiArrayView<2, T, C3> & de)
    00516 {
    00517
    00518     //  This is derived from the Algol procedure hqr2,
    00519     //  by Martin and Wilkinson, Handbook for Auto. Comp.,
    00520     //  Vol.ii-Linear Algebra, and the corresponding
    00521     //  Fortran subroutine in EISPACK.
    00522
    00523     // Initialize
    00524     MultiArrayView<1, T, C3> d = de.bindOuter(0);
    00525     MultiArrayView<1, T, C3> e = de.bindOuter(1);
    00526
    00527     int nn = rowCount(H);
    00528     int n = nn-1;
    00529     int low = 0;
    00530     int high = nn-1;
    00531     T eps = VIGRA_CSTD::pow(2.0, sizeof(T) == sizeof(float)
    00532                                      ? -23.0
    00533                                      : -52.0);
    00534     T exshift = 0.0;
    00535     T p=0,q=0,r=0,s=0,z=0,t,w,x,y;
    00536     T norm = vigra::norm(H);
    00537
    00538     // Outer loop over eigenvalue index
    00539     int iter = 0;
    00540     while(n >= low)
    00541     {
    00542
    00543         // Look for single small sub-diagonal element
    00544         int l = n;
    00545         while (l > low)
    00546         {
    00547             s = abs(H(l-1, l-1)) + abs(H(l, l));
    00548             if(s == 0.0)
    00549             {
    00550                 s = norm;
    00551             }
    00552             if(abs(H(l, l-1)) < eps * s)
    00553             {
    00554                 break;
    00555             }
    00556             --l;
    00557         }
    00558
    00559         // Check for convergence
    00560         // One root found
    00561         if(l == n)
    00562         {
    00563             H(n, n) = H(n, n) + exshift;
    00564             d(n) = H(n, n);
    00565             e(n) = 0.0;
    00566             --n;
    00567             iter = 0;
    00568
    00569         // Two roots found
    00570
    00571         }
    00572         else if(l == n-1)
    00573         {
    00574             w = H(n, n-1) * H(n-1, n);
    00575             p = (H(n-1, n-1) - H(n, n)) / 2.0;
    00576             q = p * p + w;
    00577             z = VIGRA_CSTD::sqrt(abs(q));
    00578             H(n, n) = H(n, n) + exshift;
    00579             H(n-1, n-1) = H(n-1, n-1) + exshift;
    00580             x = H(n, n);
    00581
    00582             // Real pair
    00583
    00584             if(q >= 0)
    00585             {
    00586                 if(p >= 0)
    00587                 {
    00588                     z = p + z;
    00589                 }
    00590                 else
    00591                 {
    00592                     z = p - z;
    00593                 }
    00594                 d(n-1) = x + z;
    00595                 d(n) = d(n-1);
    00596                 if(z != 0.0)
    00597                 {
    00598                     d(n) = x - w / z;
    00599                 }
    00600                 e(n-1) = 0.0;
    00601                 e(n) = 0.0;
    00602                 x = H(n, n-1);
    00603                 s = abs(x) + abs(z);
    00604                 p = x / s;
    00605                 q = z / s;
    00606                 r = VIGRA_CSTD::sqrt(p * p+q * q);
    00607                 p = p / r;
    00608                 q = q / r;
    00609
    00610                 // Row modification
    00611
    00612                 for(int j = n-1; j < nn; ++j)
    00613                 {
    00614                     z = H(n-1, j);
    00615                     H(n-1, j) = q * z + p * H(n, j);
    00616                     H(n, j) = q * H(n, j) - p * z;
    00617                 }
    00618
    00619                 // Column modification
    00620
    00621                 for(int i = 0; i <= n; ++i)
    00622                 {
    00623                     z = H(i, n-1);
    00624                     H(i, n-1) = q * z + p * H(i, n);
    00625                     H(i, n) = q * H(i, n) - p * z;
    00626                 }
    00627
    00628                 // Accumulate transformations
    00629
    00630                 for(int i = low; i <= high; ++i)
    00631                 {
    00632                     z = V(i, n-1);
    00633                     V(i, n-1) = q * z + p * V(i, n);
    00634                     V(i, n) = q * V(i, n) - p * z;
    00635                 }
    00636
    00637             // Complex pair
    00638
    00639             }
    00640             else
    00641             {
    00642                 d(n-1) = x + p;
    00643                 d(n) = x + p;
    00644                 e(n-1) = z;
    00645                 e(n) = -z;
    00646             }
    00647             n = n - 2;
    00648             iter = 0;
    00649
    00650         // No convergence yet
    00651
    00652         }
    00653         else
    00654         {
    00655
    00656             // Form shift
    00657
    00658             x = H(n, n);
    00659             y = 0.0;
    00660             w = 0.0;
    00661             if(l < n)
    00662             {
    00663                 y = H(n-1, n-1);
    00664                 w = H(n, n-1) * H(n-1, n);
    00665             }
    00666
    00667             // Wilkinson's original ad hoc shift
    00668
    00669             if(iter == 10)
    00670             {
    00671                 exshift += x;
    00672                 for(int i = low; i <= n; ++i)
    00673                 {
    00674                     H(i, i) -= x;
    00675                 }
    00676                 s = abs(H(n, n-1)) + abs(H(n-1, n-2));
    00677                 x = y = 0.75 * s;
    00678                 w = -0.4375 * s * s;
    00679             }
    00680
    00681             // MATLAB's new ad hoc shift
    00682
    00683             if(iter == 30)
    00684             {
    00685                  s = (y - x) / 2.0;
    00686                  s = s * s + w;
    00687                  if(s > 0)
    00688                  {
    00689                       s = VIGRA_CSTD::sqrt(s);
    00690                       if(y < x)
    00691                       {
    00692                           s = -s;
    00693                       }
    00694                       s = x - w / ((y - x) / 2.0 + s);
    00695                       for(int i = low; i <= n; ++i)
    00696                       {
    00697                           H(i, i) -= s;
    00698                       }
    00699                       exshift += s;
    00700                       x = y = w = 0.964;
    00701                  }
    00702             }
    00703
    00704             iter = iter + 1;
    00705             if(iter > 60)
    00706                 return false;
    00707
    00708             // Look for two consecutive small sub-diagonal elements
    00709             int m = hessenbergQrDecompositionHelper(H, n, l, eps, p, q, r, s, w, x, y, z);
    00710             for(int i = m+2; i <= n; ++i)
    00711             {
    00712                 H(i, i-2) = 0.0;
    00713                 if(i > m+2)
    00714                 {
    00715                     H(i, i-3) = 0.0;
    00716                 }
    00717             }
    00718
    00719             // Double QR step involving rows l:n and columns m:n
    00720
    00721             for(int k = m; k <= n-1; ++k)
    00722             {
    00723                 int notlast = (k != n-1);
    00724                 if(k != m) {
    00725                     p = H(k, k-1);
    00726                     q = H(k+1, k-1);
    00727                     r = (notlast ? H(k+2, k-1) : 0.0);
    00728                     x = abs(p) + abs(q) + abs(r);
    00729                     if(x != 0.0)
    00730                     {
    00731                         p = p / x;
    00732                         q = q / x;
    00733                         r = r / x;
    00734                     }
    00735                 }
    00736                 if(x == 0.0)
    00737                 {
    00738                     break;
    00739                 }
    00740                 s = VIGRA_CSTD::sqrt(p * p + q * q + r * r);
    00741                 if(p < 0)
    00742                 {
    00743                     s = -s;
    00744                 }
    00745                 if(s != 0)
    00746                 {
    00747                     if(k != m)
    00748                     {
    00749                         H(k, k-1) = -s * x;
    00750                     }
    00751                     else if(l != m)
    00752                     {
    00753                         H(k, k-1) = -H(k, k-1);
    00754                     }
    00755                     p = p + s;
    00756                     x = p / s;
    00757                     y = q / s;
    00758                     z = r / s;
    00759                     q = q / p;
    00760                     r = r / p;
    00761
    00762                     // Row modification
    00763
    00764                     for(int j = k; j < nn; ++j)
    00765                     {
    00766                         p = H(k, j) + q * H(k+1, j);
    00767                         if(notlast)
    00768                         {
    00769                             p = p + r * H(k+2, j);
    00770                             H(k+2, j) = H(k+2, j) - p * z;
    00771                         }
    00772                         H(k, j) = H(k, j) - p * x;
    00773                         H(k+1, j) = H(k+1, j) - p * y;
    00774                     }
    00775
    00776                     // Column modification
    00777
    00778                     for(int i = 0; i <= std::min(n,k+3); ++i)
    00779                     {
    00780                         p = x * H(i, k) + y * H(i, k+1);
    00781                         if(notlast)
    00782                         {
    00783                             p = p + z * H(i, k+2);
    00784                             H(i, k+2) = H(i, k+2) - p * r;
    00785                         }
    00786                         H(i, k) = H(i, k) - p;
    00787                         H(i, k+1) = H(i, k+1) - p * q;
    00788                     }
    00789
    00790                     // Accumulate transformations
    00791
    00792                     for(int i = low; i <= high; ++i)
    00793                     {
    00794                         p = x * V(i, k) + y * V(i, k+1);
    00795                         if(notlast)
    00796                         {
    00797                             p = p + z * V(i, k+2);
    00798                             V(i, k+2) = V(i, k+2) - p * r;
    00799                         }
    00800                         V(i, k) = V(i, k) - p;
    00801                         V(i, k+1) = V(i, k+1) - p * q;
    00802                     }
    00803                 }  // (s != 0)
    00804             }  // k loop
    00805         }  // check convergence
    00806     }  // while (n >= low)
    00807
    00808     // Backsubstitute to find vectors of upper triangular form
    00809
    00810     if(norm == 0.0)
    00811     {
    00812         return false;
    00813     }
    00814
    00815     for(n = nn-1; n >= 0; --n)
    00816     {
    00817         p = d(n);
    00818         q = e(n);
    00819
    00820         // Real vector
    00821
    00822         if(q == 0)
    00823         {
    00824             int l = n;
    00825             H(n, n) = 1.0;
    00826             for(int i = n-1; i >= 0; --i)
    00827             {
    00828                 w = H(i, i) - p;
    00829                 r = 0.0;
    00830                 for(int j = l; j <= n; ++j)
    00831                 {
    00832                     r = r + H(i, j) * H(j, n);
    00833                 }
    00834                 if(e(i) < 0.0)
    00835                 {
    00836                     z = w;
    00837                     s = r;
    00838                 }
    00839                 else
    00840                 {
    00841                     l = i;
    00842                     if(e(i) == 0.0)
    00843                     {
    00844                         if(w != 0.0)
    00845                         {
    00846                             H(i, n) = -r / w;
    00847                         }
    00848                         else
    00849                         {
    00850                             H(i, n) = -r / (eps * norm);
    00851                         }
    00852
    00853                     // Solve real equations
    00854
    00855                     }
    00856                     else
    00857                     {
    00858                         x = H(i, i+1);
    00859                         y = H(i+1, i);
    00860                         q = (d(i) - p) * (d(i) - p) + e(i) * e(i);
    00861                         t = (x * s - z * r) / q;
    00862                         H(i, n) = t;
    00863                         if(abs(x) > abs(z))
    00864                         {
    00865                             H(i+1, n) = (-r - w * t) / x;
    00866                         }
    00867                         else
    00868                         {
    00869                             H(i+1, n) = (-s - y * t) / z;
    00870                         }
    00871                     }
    00872
    00873                     // Overflow control
    00874
    00875                     t = abs(H(i, n));
    00876                     if((eps * t) * t > 1)
    00877                     {
    00878                         for(int j = i; j <= n; ++j)
    00879                         {
    00880                             H(j, n) = H(j, n) / t;
    00881                         }
    00882                     }
    00883                 }
    00884             }
    00885
    00886         // Complex vector
    00887
    00888         }
    00889         else if(q < 0)
    00890         {
    00891             int l = n-1;
    00892
    00893             // Last vector component imaginary so matrix is triangular
    00894
    00895             if(abs(H(n, n-1)) > abs(H(n-1, n)))
    00896             {
    00897                 H(n-1, n-1) = q / H(n, n-1);
    00898                 H(n-1, n) = -(H(n, n) - p) / H(n, n-1);
    00899             }
    00900             else
    00901             {
    00902                 cdiv(0.0,-H(n-1, n),H(n-1, n-1)-p,q, H(n-1, n-1), H(n-1, n));
    00903             }
    00904             H(n, n-1) = 0.0;
    00905             H(n, n) = 1.0;
    00906             for(int i = n-2; i >= 0; --i)
    00907             {
    00908                 T ra,sa,vr,vi;
    00909                 ra = 0.0;
    00910                 sa = 0.0;
    00911                 for(int j = l; j <= n; ++j)
    00912                 {
    00913                     ra = ra + H(j, n-1) * H(i, j);
    00914                     sa = sa + H(j, n) * H(i, j);
    00915                 }
    00916                 w = H(i, i) - p;
    00917
    00918                 if(e(i) < 0.0)
    00919                 {
    00920                     z = w;
    00921                     r = ra;
    00922                     s = sa;
    00923                 }
    00924                 else
    00925                 {
    00926                     l = i;
    00927                     if(e(i) == 0)
    00928                     {
    00929                         cdiv(-ra,-sa,w,q, H(i, n-1), H(i, n));
    00930                     }
    00931                     else
    00932                     {
    00933                         // Solve complex equations
    00934
    00935                         x = H(i, i+1);
    00936                         y = H(i+1, i);
    00937                         vr = (d(i) - p) * (d(i) - p) + e(i) * e(i) - q * q;
    00938                         vi = (d(i) - p) * 2.0 * q;
    00939                         if((vr == 0.0) && (vi == 0.0))
    00940                         {
    00941                             vr = eps * norm * (abs(w) + abs(q) +
    00942                             abs(x) + abs(y) + abs(z));
    00943                         }
    00944                         cdiv(x*r-z*ra+q*sa,x*s-z*sa-q*ra,vr,vi, H(i, n-1), H(i, n));
    00945                         if(abs(x) > (abs(z) + abs(q)))
    00946                         {
    00947                             H(i+1, n-1) = (-ra - w * H(i, n-1) + q * H(i, n)) / x;
    00948                             H(i+1, n) = (-sa - w * H(i, n) - q * H(i, n-1)) / x;
    00949                         }
    00950                         else
    00951                         {
    00952                             cdiv(-r-y*H(i, n-1),-s-y*H(i, n),z,q, H(i+1, n-1), H(i+1, n));
    00953                         }
    00954                     }
    00955
    00956                     // Overflow control
    00957
    00958                     t = std::max(abs(H(i, n-1)),abs(H(i, n)));
    00959                     if((eps * t) * t > 1)
    00960                     {
    00961                         for(int j = i; j <= n; ++j)
    00962                         {
    00963                             H(j, n-1) = H(j, n-1) / t;
    00964                             H(j, n) = H(j, n) / t;
    00965                         }
    00966                     }
    00967                 }
    00968             }
    00969         }
    00970     }
    00971
    00972     // Back transformation to get eigenvectors of original matrix
    00973
    00974     for(int j = nn-1; j >= low; --j)
    00975     {
    00976         for(int i = low; i <= high; ++i)
    00977         {
    00978             z = 0.0;
    00979             for(int k = low; k <= std::min(j,high); ++k)
    00980             {
    00981                 z = z + V(i, k) * H(k, j);
    00982             }
    00983             V(i, j) = z;
    00984         }
    00985     }
    00986     return true;
    00987 }
    00988
    00989 } // namespace detail
    00990
    00991 /** \addtogroup LinearAlgebraFunctions Matrix functions
    00992  */
    00993 //@{
    00994     /** Compute the eigensystem of a symmetric matrix.
    00995
    00996         \a a is a real symmetric matrix, \a ew is a single-column matrix
    00997         holding the eigenvalues, and \a ev is a matrix of the same size as
    00998         \a a whose columns are the corresponding eigenvectors. Eigenvalues
    00999         will be sorted from largest to smallest magnitude.
    01000         The algorithm returns <tt>false</tt> when it doesn't
    01001         converge. It can be applied in-place, i.e. <tt>&a == &ev</tt> is allowed.
    01002         The code of this function was adapted from JAMA.
    01003
    01004     <b>\#include</b> "<a href="eigensystem_8hxx-source.html">vigra/eigensystem.hxx</a>" or<br>
    01005     <b>\#include</b> "<a href="linear__algebra_8hxx-source.html">vigra/linear_algebra.hxx</a>"<br>
    01006         Namespaces: vigra and vigra::linalg
    01007      */
    01008 template <class T, class C1, class C2, class C3>
    01009 bool
    01010 symmetricEigensystem(MultiArrayView<2, T, C1> const & a,
    01011             MultiArrayView<2, T, C2> & ew, MultiArrayView<2, T, C3> & ev)
    01012 {
    01013     vigra_precondition(isSymmetric(a),
    01014         "symmetricEigensystem(): symmetric input matrix required.");
    01015     unsigned int acols = columnCount(a);
    01016     vigra_precondition(1 == columnCount(ew) && acols == rowCount(ew) &&
    01017                        acols == columnCount(ev) && acols == rowCount(ev),
    01018         "symmetricEigensystem(): matrix shape mismatch.");
    01019
    01020     ev.copy(a); // does nothing if &ev == &a
    01021     Matrix<T> de(acols, 2);
    01022     detail::housholderTridiagonalization(ev, de);
    01023     if(!detail::tridiagonalMatrixEigensystem(de, ev))
    01024         return false;
    01025
    01026     ew.copy(columnVector(de, 0));
    01027     return true;
    01028 }
    01029
    01030     /** Compute the eigensystem of a square, but
    01031         not necessarily symmetric matrix.
    01032
    01033         \a a is a real square matrix, \a ew is a single-column matrix
    01034         holding the possibly complex eigenvalues, and \a ev is a matrix of
    01035         the same size as \a a whose columns are the corresponding eigenvectors.
    01036         Eigenvalues will be sorted from largest to smallest magnitude.
    01037         The algorithm returns <tt>false</tt> when it doesn't
    01038         converge. It can be applied in-place, i.e. <tt>&a == &ev</tt> is allowed.
    01039         The code of this function was adapted from JAMA.
    01040
    01041     <b>\#include</b> "<a href="eigensystem_8hxx-source.html">vigra/eigensystem.hxx</a>" or<br>
    01042     <b>\#include</b> "<a href="linear__algebra_8hxx-source.html">vigra/linear_algebra.hxx</a>"<br>
    01043         Namespaces: vigra and vigra::linalg
    01044      */
    01045 template <class T, class C1, class C2, class C3>
    01046 bool
    01047 nonsymmetricEigensystem(MultiArrayView<2, T, C1> const & a,
    01048          MultiArrayView<2, std::complex<T>, C2> & ew, MultiArrayView<2, T, C3> & ev)
    01049 {
    01050     unsigned int acols = columnCount(a);
    01051     vigra_precondition(acols == rowCount(a),
    01052         "nonsymmetricEigensystem(): square input matrix required.");
    01053     vigra_precondition(1 == columnCount(ew) && acols == rowCount(ew) &&
    01054                        acols == columnCount(ev) && acols == rowCount(ev),
    01055         "nonsymmetricEigensystem(): matrix shape mismatch.");
    01056
    01057     Matrix<T> H(a);
    01058     Matrix<T> de(acols, 2);
    01059     detail::nonsymmetricHessenbergReduction(H, ev);
    01060     if(!detail::hessenbergQrDecomposition(H, ev, de))
    01061         return false;
    01062
    01063     for(unsigned int i=0; i < acols; ++i)
    01064     {
    01065         ew(i,0) = std::complex<T>(de(i, 0), de(i, 1));
    01066     }
    01067     return true;
    01068 }
    01069
    01070     /** Compute the roots of a polynomial using the eigenvalue method.
    01071
    01072         \a poly is a real polynomial (compatible to \ref vigra::PolynomialView),
    01073         and \a roots a complex valued vector (compatible to <tt>std::vector</tt>
    01074         with a <tt>value_type</tt> compatible to the type <tt>POLYNOMIAL::Complex</tt>) to which
    01075         the roots are appended. The function calls \ref nonsymmetricEigensystem() with the standard
    01076         companion matrix yielding the roots as eigenvalues. It returns <tt>false</tt> if
    01077         it fails to converge.
    01078
    01079         <b>\#include</b> "<a href="eigensystem_8hxx-source.html">vigra/eigensystem.hxx</a>" or<br>
    01080         <b>\#include</b> "<a href="linear__algebra_8hxx-source.html">vigra/linear_algebra.hxx</a>"<br>
    01081         Namespaces: vigra and vigra::linalg
    01082
    01083         \see polynomialRoots(), vigra::Polynomial
    01084      */
    01085 template <class POLYNOMIAL, class VECTOR>
    01086 bool polynomialRootsEigenvalueMethod(POLYNOMIAL const & poly, VECTOR & roots, bool polishRoots)
    01087 {
    01088     typedef typename POLYNOMIAL::value_type T;
    01089     typedef typename POLYNOMIAL::Real    Real;
    01090     typedef typename POLYNOMIAL::Complex Complex;
    01091     typedef Matrix<T> TMatrix;
    01092     typedef Matrix<Complex> ComplexMatrix;
    01093
    01094     int const degree = poly.order();
    01095     double const eps = poly.epsilon();
    01096
    01097     TMatrix inMatrix(degree, degree);
    01098     for(int i = 0; i < degree; ++i)
    01099         inMatrix(0, i) = -poly[degree - i - 1] / poly[degree];
    01100     for(int i = 0; i < degree - 1; ++i)
    01101         inMatrix(i + 1, i) = NumericTraits<T>::one();
    01102     ComplexMatrix ew(degree, 1);
    01103     TMatrix ev(degree, degree);
    01104     bool success = nonsymmetricEigensystem(inMatrix, ew, ev);
    01105     if(!success)
    01106         return false;
    01107     for(int i = 0; i < degree; ++i)
    01108     {
    01109         if(polishRoots)
    01110             vigra::detail::laguerre1Root(poly, ew(i,0), 1);
    01111         roots.push_back(vigra::detail::deleteBelowEpsilon(ew(i,0), eps));
    01112     }
    01113     std::sort(roots.begin(), roots.end(), vigra::detail::PolynomialRootCompare<Real>(eps));
    01114     return true;
    01115 }
    01116
    01117 template <class POLYNOMIAL, class VECTOR>
    01118 bool polynomialRootsEigenvalueMethod(POLYNOMIAL const & poly, VECTOR & roots)
    01119 {
    01120     return polynomialRootsEigenvalueMethod(poly, roots, true);
    01121 }
    01122
    01123     /** Compute the real roots of a real polynomial using the eigenvalue method.
    01124
    01125         \a poly is a real polynomial (compatible to \ref vigra::PolynomialView),
    01126         and \a roots a real valued vector (compatible to <tt>std::vector</tt>
    01127         with a <tt>value_type</tt> compatible to the type <tt>POLYNOMIAL::Real</tt>) to which
    01128         the roots are appended. The function calls \ref polynomialRootsEigenvalueMethod() and
    01129         throws away all complex roots. It returns <tt>false</tt> if it fails to converge.
    01130
    01131         <b>\#include</b> "<a href="eigensystem_8hxx-source.html">vigra/eigensystem.hxx</a>" or<br>
    01132         <b>\#include</b> "<a href="linear__algebra_8hxx-source.html">vigra/linear_algebra.hxx</a>"<br>
    01133         Namespaces: vigra and vigra::linalg
    01134
    01135         \see polynomialRealRoots(), vigra::Polynomial
    01136      */
    01137 template <class POLYNOMIAL, class VECTOR>
    01138 bool polynomialRealRootsEigenvalueMethod(POLYNOMIAL const & p, VECTOR & roots, bool polishRoots)
    01139 {
    01140     typedef typename NumericTraits<typename VECTOR::value_type>::ComplexPromote Complex;
    01141     ArrayVector<Complex> croots;
    01142     if(!polynomialRootsEigenvalueMethod(p, croots))
    01143         return false;
    01144     for(unsigned int i = 0; i < croots.size(); ++i)
    01145         if(croots[i].imag() == 0.0)
    01146             roots.push_back(croots[i].real());
    01147     return true;
    01148 }
    01149
    01150 template <class POLYNOMIAL, class VECTOR>
    01151 bool polynomialRealRootsEigenvalueMethod(POLYNOMIAL const & p, VECTOR & roots)
    01152 {
    01153     return polynomialRealRootsEigenvalueMethod(p, roots, true);
    01154 }
    01155
    01156
    01157 //@}
    01158
    01159 } // namespace linalg
    01160
    01161 using linalg::symmetricEigensystem;
    01162 using linalg::nonsymmetricEigensystem;
    01163 using linalg::polynomialRootsEigenvalueMethod;
    01164 using linalg::polynomialRealRootsEigenvalueMethod;
    01165
    01166 } // namespace vigra
    01167
    01168 #endif // VIGRA_EIGENSYSTEM_HXX

© Ullrich Köthe (koethe@informatik.uni-hamburg.de) Cognitive Systems Group, University of Hamburg, Germany | html generated using doxygen and Python VIGRA 1.5.0 (7 Dec 2006)