/usr/include/libwildmagic/Wm5SymmetricEigensolverGTE.h is in libwildmagic-dev 5.13-1+b2.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 | // Geometric Tools, LLC
// Copyright (c) 1998-2014
// Distributed under the Boost Software License, Version 1.0.
// http://www.boost.org/LICENSE_1_0.txt
// http://www.geometrictools.com/License/Boost/LICENSE_1_0.txt
//
// File Version: 5.12.1 (2014/07/09)
// NOTE: This code was written for the upcoming Geometric Tools Engine but
// has been back-ported to Wild Magic 5 because it has better quality than
// its previous version.
#ifndef WM5SYMMETRICEIGENSOLVER_H
#define WM5SYMMETRICEIGENSOLVER_H
#include "Wm5MathematicsLIB.h"
// The SymmetricEigensolver class is an implementation of Algorithm 8.2.3
// (Symmetric QR Algorithm) described in "Matrix Computations, 2nd edition"
// by G. H. Golub and C. F. Van Loan, The Johns Hopkins University Press,
// Baltimore MD, Fourth Printing 1993. Algorithm 8.2.1 (Householder
// Tridiagonalization) is used to reduce matrix A to tridiagonal T.
// Algorithm 8.2.2 (Implicit Symmetric QR Step with Wilkinson Shift) is
// used for the iterative reduction from tridiagonal to diagonal. If A is
// the original matrix, D is the diagonal matrix of eigenvalues, and Q is
// the orthogonal matrix of eigenvectors, then theoretically Q^T*A*Q = D.
// Numerically, we have errors E = Q^T*A*Q - D. Algorithm 8.2.3 mentions
// that one expects |E| is approximately u*|A|, where |M| denotes the
// Frobenius norm of M and where u is the unit roundoff for the
// floating-point arithmetic: 2^{-23} for 'float', which is FLT_EPSILON
// = 1.192092896e-7f, and 2^{-52} for'double', which is DBL_EPSILON
// = 2.2204460492503131e-16.
//
// The condition |a(i,i+1)| <= epsilon*(|a(i,i) + a(i+1,i+1)|) used to
// determine when the reduction decouples to smaller problems is implemented
// as: sum = |a(i,i)| + |a(i+1,i+1)|; sum + |a(i,i+1)| == sum. The idea is
// that the superdiagonal term is small relative to its diagonal neighbors,
// and so it is effectively zero. The unit tests have shown that this
// interpretation of decoupling is effective.
//
// The authors suggest that once you have the tridiagonal matrix, a practical
// implementation will store the diagonal and superdiagonal entries in linear
// arrays, ignoring the theoretically zero values not in the 3-band. This is
// good for cache coherence. The authors also suggest storing the Householder
// vectors in the lower-triangular portion of the matrix to save memory. The
// implementation uses both suggestions.
//
// For matrices with randomly generated values in [0,1], the unit tests
// produce the following information for N-by-N matrices.
//
// N |A| |E| |E|/|A| iterations
// -------------------------------------------
// 2 1.2332 5.5511e-17 4.5011e-17 1
// 3 2.0024 1.1818e-15 5.9020e-16 5
// 4 2.8708 9.9287e-16 3.4585e-16 7
// 5 2.9040 2.5958e-15 8.9388e-16 9
// 6 4.0427 2.2434e-15 5.5493e-16 12
// 7 5.0276 3.2456e-15 6.4555e-16 15
// 8 5.4468 6.5626e-15 1.2048e-15 15
// 9 6.1510 4.0317e-15 6.5545e-16 17
// 10 6.7523 4.9334e-15 7.3062e-16 21
// 11 7.1322 7.1347e-15 1.0003e-15 22
// 12 7.8324 5.6106e-15 7.1633e-16 24
// 13 8.1073 5.1366e-15 6.3357e-16 25
// 14 8.6257 8.3496e-15 9.6798e-16 29
// 15 9.2603 6.9526e-15 7.5080e-16 31
// 16 9.9853 6.5807e-15 6.5904e-16 32
// 17 10.5388 8.0931e-15 7.6793e-16 35
// 18 11.2377 1.1149e-14 9.9218e-16 38
// 19 11.7105 1.0711e-14 9.1470e-16 42
// 20 12.2227 1.7723e-14 1.4500e-15 42
// 21 12.7411 1.2515e-14 9.8231e-16 47
// 22 13.4462 1.2645e-14 9.4046e-16 50
// 23 13.9541 1.2899e-14 9.2444e-16 47
// 24 14.3298 1.6337e-14 1.1400e-15 53
// 25 14.8050 1.4760e-14 9.9701e-16 54
// 26 15.3914 1.7076e-14 1.1094e-15 57
// 27 15.8430 1.9714e-14 1.2443e-15 60
// 28 16.4771 1.7148e-14 1.0407e-15 60
// 29 16.9909 1.7309e-14 1.0187e-15 60
// 30 17.4456 2.1453e-14 1.2297e-15 64
// 31 17.9909 2.2069e-14 1.2267e-15 68
//
// The eigenvalues and |E|/|A| values were compared to those generated by
// Mathematica Version 9.0; Wolfram Research, Inc., Champaign IL, 2012.
// The results were all comparable with eigenvalues agreeing to a large
// number of decimal places.
//
// Timing on an Intel (R) Core (TM) i7-3930K CPU @ 3.20 GHZ (in seconds):
//
// N |E|/|A| iters tridiag QR evecs evec[N] comperr
// --------------------------------------------------------------
// 512 4.4149e-15 1017 0.180 0.005 1.151 0.848 2.166
// 1024 6.1691e-15 1990 1.775 0.031 11.894 12.759 21.179
// 2048 8.5108e-15 3849 16.592 0.107 119.744 116.56 212.227
//
// where iters is the number of QR steps taken, tridiag is the computation
// of the Householder reflection vectors, evecs is the composition of
// Householder reflections and Givens rotations to obtain the matrix of
// eigenvectors, evec[N] is N calls to get the eigenvectors separately, and
// comperr is the computation E = Q^T*A*Q - D. The construction of the full
// eigenvector matrix is, of course, quite expensive. If you need only a
// small number of eigenvectors, use function GetEigenvector(int,Real*).
namespace Wm5
{
template <typename Real>
class WM5_MATHEMATICS_ITEM SymmetricEigensolverGTE
{
public:
// The solver processes NxN symmetric matrices, where N > 1 ('size' is N)
// and the matrix is stored in row-major order. The maximum number of
// iterations ('maxIterations') must be specified for the reduction of a
// tridiagonal matrix to a diagonal matrix. The goal is to compute
// NxN orthogonal Q and NxN diagonal D for which Q^T*A*Q = D.
SymmetricEigensolverGTE(int size, unsigned maxIterations);
// A copy of the NxN symmetric input is made internally. The order of
// the eigenvalues is specified by sortType: -1 (decreasing), 0 (no
// sorting), or +1 (increasing). When sorted, the eigenvectors are
// ordered accordingly. The return value is the number of iterations
// consumed when convergence occurred, 0xFFFFFFFF when convergence did
// not occur, or 0 when N <= 1 was passed to the constructor.
unsigned int Solve(Real const* input, int sortType);
// Get the eigenvalues of the matrix passed to Solve(...). The input
// 'eigenvalues' must have N elements.
void GetEigenvalues(Real* eigenvalues) const;
// Accumulate the Householder reflections and Givens rotations to produce
// the orthogonal matrix Q for which Q^T*A*Q = D. The input
// 'eigenvectors' must be NxN and stored in row-major order.
void GetEigenvectors(Real* eigenvectors) const;
// With no sorting, when N is odd the matrix returned by GetEigenvectors
// is a reflection and when N is even it is a rotation. With sorting
// enabled, the type of matrix returned depends on the permutation of
// columns. If the permutation has C cycles, the minimum number of column
// transpositions is T = N-C. Thus, when C is odd the matrix is a
// reflection and when C is even the matrix is a rotation.
bool IsRotation() const;
// Compute a single eigenvector, which amounts to computing column c
// of matrix Q. The reflections and rotations are applied incrementally.
// This is useful when you want only a small number of the eigenvectors.
void GetEigenvector(int c, Real* eigenvector) const;
private:
// Tridiagonalize using Householder reflections. On input, mMatrix is a
// copy of the input matrix. On output, the upper-triangular part of
// mMatrix including the diagonal stores the tridiagonalization. The
// lower-triangular part contains 2/Dot(v,v) that are used in computing
// eigenvectors and the part below the subdiagonal stores the essential
// parts of the Householder vectors v (the elements of v after the
// leading 1-valued component).
void Tridiagonalize();
// A helper for generating Givens rotation sine and cosine robustly.
void GetSinCos(Real u, Real v, Real& cs, Real& sn);
// The QR step with implicit shift. Generally, the initial T is unreduced
// tridiagonal (all subdiagonal entries are nonzero). If a QR step causes
// a superdiagonal entry to become zero, the matrix decouples into a block
// diagonal matrix with two tridiagonal blocks. These blocks can be
// reduced independently of each other, which allows for parallelization
// of the algorithm. The inputs imin and imax identify the subblock of T
// to be processed. That block has upper-left element T(imin,imin) and
// lower-right element T(imax,imax).
void DoQRImplicitShift(int imin, int imax);
// Sort the eigenvalues and compute the corresponding permutation of the
// indices of the array storing the eigenvalues. The permutation is used
// for reordering the eigenvalues and eigenvectors in the calls to
// GetEigenvalues(...) and GetEigenvectors(...).
void ComputePermutation(int sortType);
// The number N of rows and columns of the matrices to be processed.
int mSize;
// The maximum number of iterations for reducing the tridiagonal mtarix
// to a diagonal matrix.
unsigned int mMaxIterations;
// The internal copy of a matrix passed to the solver. See the comments
// about function Tridiagonalize() about what is stored in the matrix.
std::vector<Real> mMatrix; // NxN elements
// After the initial tridiagonalization by Householder reflections, we no
// longer need the full mMatrix. Copy the diagonal and superdiagonal
// entries to linear arrays in order to be cache friendly.
std::vector<Real> mDiagonal; // N elements
std::vector<Real> mSuperdiagonal; // N-1 elements
// The Givens rotations used to reduce the initial tridiagonal matrix to
// a diagonal matrix. A rotation is the identity with the following
// replacement entries: R(index,index) = cs, R(index,index+1) = sn,
// R(index+1,index) = -sn, and R(index+1,index+1) = cs. If N is the
// matrix size and K is the maximum number of iterations, the maximum
// number of Givens rotations is K*(N-1). The maximum amount of memory
// is allocated to store these.
struct WM5_MATHEMATICS_ITEM GivensRotation
{
GivensRotation();
GivensRotation(int inIndex, Real inCs, Real inSn);
int index;
Real cs, sn;
};
std::vector<GivensRotation> mGivens; // K*(N-1) elements
// When sorting is requested, the permutation associated with the sort is
// stored in mPermutation. When sorting is not requested, mPermutation[0]
// is set to -1. mVisited is used for finding cycles in the permutation.
struct SortItem
{
Real eigenvalue;
int index;
bool operator<(SortItem const& item) const
{
return eigenvalue < item.eigenvalue;
}
bool operator>(SortItem const& item) const
{
return eigenvalue > item.eigenvalue;
}
};
std::vector<int> mPermutation; // N elements
mutable std::vector<int> mVisited; // N elements
mutable int mIsRotation; // 1 = rotation, 0 = reflection, -1 = unknown
// Temporary storage to compute Householder reflections and to support
// sorting of eigenvectors.
mutable std::vector<Real> mPVector; // N elements
mutable std::vector<Real> mVVector; // N elements
mutable std::vector<Real> mWVector; // N elements
};
typedef SymmetricEigensolverGTE<float> SymmetricEigensolverGTEf;
typedef SymmetricEigensolverGTE<double> SymmetricEigensolverGTEd;
}
#endif
|