mirror of https://github.com/k3s-io/k3s
423 lines
11 KiB
Go
423 lines
11 KiB
Go
// Copyright ©2013 The Gonum Authors. All rights reserved.
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// Use of this source code is governed by a BSD-style
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// license that can be found in the LICENSE file.
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package mat
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import (
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"math"
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"gonum.org/v1/gonum/blas"
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"gonum.org/v1/gonum/blas/blas64"
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"gonum.org/v1/gonum/floats"
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"gonum.org/v1/gonum/lapack"
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"gonum.org/v1/gonum/lapack/lapack64"
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)
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const (
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badSliceLength = "mat: improper slice length"
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badLU = "mat: invalid LU factorization"
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)
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// LU is a type for creating and using the LU factorization of a matrix.
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type LU struct {
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lu *Dense
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pivot []int
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cond float64
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}
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// updateCond updates the stored condition number of the matrix. anorm is the
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// norm of the original matrix. If anorm is negative it will be estimated.
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func (lu *LU) updateCond(anorm float64, norm lapack.MatrixNorm) {
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n := lu.lu.mat.Cols
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work := getFloats(4*n, false)
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defer putFloats(work)
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iwork := getInts(n, false)
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defer putInts(iwork)
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if anorm < 0 {
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// This is an approximation. By the definition of a norm,
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// |AB| <= |A| |B|.
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// Since A = L*U, we get for the condition number κ that
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// κ(A) := |A| |A^-1| = |L*U| |A^-1| <= |L| |U| |A^-1|,
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// so this will overestimate the condition number somewhat.
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// The norm of the original factorized matrix cannot be stored
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// because of update possibilities.
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u := lu.lu.asTriDense(n, blas.NonUnit, blas.Upper)
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l := lu.lu.asTriDense(n, blas.Unit, blas.Lower)
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unorm := lapack64.Lantr(norm, u.mat, work)
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lnorm := lapack64.Lantr(norm, l.mat, work)
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anorm = unorm * lnorm
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}
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v := lapack64.Gecon(norm, lu.lu.mat, anorm, work, iwork)
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lu.cond = 1 / v
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}
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// Factorize computes the LU factorization of the square matrix a and stores the
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// result. The LU decomposition will complete regardless of the singularity of a.
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//
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// The LU factorization is computed with pivoting, and so really the decomposition
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// is a PLU decomposition where P is a permutation matrix. The individual matrix
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// factors can be extracted from the factorization using the Permutation method
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// on Dense, and the LU LTo and UTo methods.
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func (lu *LU) Factorize(a Matrix) {
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lu.factorize(a, CondNorm)
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}
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func (lu *LU) factorize(a Matrix, norm lapack.MatrixNorm) {
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r, c := a.Dims()
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if r != c {
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panic(ErrSquare)
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}
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if lu.lu == nil {
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lu.lu = NewDense(r, r, nil)
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} else {
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lu.lu.Reset()
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lu.lu.reuseAs(r, r)
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}
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lu.lu.Copy(a)
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if cap(lu.pivot) < r {
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lu.pivot = make([]int, r)
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}
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lu.pivot = lu.pivot[:r]
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work := getFloats(r, false)
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anorm := lapack64.Lange(norm, lu.lu.mat, work)
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putFloats(work)
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lapack64.Getrf(lu.lu.mat, lu.pivot)
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lu.updateCond(anorm, norm)
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}
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// isValid returns whether the receiver contains a factorization.
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func (lu *LU) isValid() bool {
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return lu.lu != nil && !lu.lu.IsZero()
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}
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// Cond returns the condition number for the factorized matrix.
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// Cond will panic if the receiver does not contain a factorization.
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func (lu *LU) Cond() float64 {
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if !lu.isValid() {
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panic(badLU)
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}
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return lu.cond
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}
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// Reset resets the factorization so that it can be reused as the receiver of a
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// dimensionally restricted operation.
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func (lu *LU) Reset() {
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if lu.lu != nil {
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lu.lu.Reset()
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}
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lu.pivot = lu.pivot[:0]
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}
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func (lu *LU) isZero() bool {
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return len(lu.pivot) == 0
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}
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// Det returns the determinant of the matrix that has been factorized. In many
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// expressions, using LogDet will be more numerically stable.
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// Det will panic if the receiver does not contain a factorization.
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func (lu *LU) Det() float64 {
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det, sign := lu.LogDet()
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return math.Exp(det) * sign
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}
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// LogDet returns the log of the determinant and the sign of the determinant
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// for the matrix that has been factorized. Numerical stability in product and
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// division expressions is generally improved by working in log space.
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// LogDet will panic if the receiver does not contain a factorization.
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func (lu *LU) LogDet() (det float64, sign float64) {
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if !lu.isValid() {
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panic(badLU)
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}
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_, n := lu.lu.Dims()
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logDiag := getFloats(n, false)
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defer putFloats(logDiag)
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sign = 1.0
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for i := 0; i < n; i++ {
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v := lu.lu.at(i, i)
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if v < 0 {
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sign *= -1
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}
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if lu.pivot[i] != i {
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sign *= -1
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}
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logDiag[i] = math.Log(math.Abs(v))
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}
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return floats.Sum(logDiag), sign
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}
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// Pivot returns pivot indices that enable the construction of the permutation
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// matrix P (see Dense.Permutation). If swaps == nil, then new memory will be
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// allocated, otherwise the length of the input must be equal to the size of the
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// factorized matrix.
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// Pivot will panic if the receiver does not contain a factorization.
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func (lu *LU) Pivot(swaps []int) []int {
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if !lu.isValid() {
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panic(badLU)
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}
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_, n := lu.lu.Dims()
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if swaps == nil {
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swaps = make([]int, n)
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}
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if len(swaps) != n {
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panic(badSliceLength)
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}
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// Perform the inverse of the row swaps in order to find the final
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// row swap position.
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for i := range swaps {
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swaps[i] = i
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}
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for i := n - 1; i >= 0; i-- {
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v := lu.pivot[i]
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swaps[i], swaps[v] = swaps[v], swaps[i]
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}
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return swaps
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}
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// RankOne updates an LU factorization as if a rank-one update had been applied to
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// the original matrix A, storing the result into the receiver. That is, if in
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// the original LU decomposition P * L * U = A, in the updated decomposition
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// P * L * U = A + alpha * x * y^T.
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// RankOne will panic if orig does not contain a factorization.
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func (lu *LU) RankOne(orig *LU, alpha float64, x, y Vector) {
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if !orig.isValid() {
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panic(badLU)
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}
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// RankOne uses algorithm a1 on page 28 of "Multiple-Rank Updates to Matrix
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// Factorizations for Nonlinear Analysis and Circuit Design" by Linzhong Deng.
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// http://web.stanford.edu/group/SOL/dissertations/Linzhong-Deng-thesis.pdf
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_, n := orig.lu.Dims()
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if r, c := x.Dims(); r != n || c != 1 {
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panic(ErrShape)
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}
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if r, c := y.Dims(); r != n || c != 1 {
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panic(ErrShape)
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}
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if orig != lu {
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if lu.isZero() {
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if cap(lu.pivot) < n {
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lu.pivot = make([]int, n)
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}
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lu.pivot = lu.pivot[:n]
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if lu.lu == nil {
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lu.lu = NewDense(n, n, nil)
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} else {
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lu.lu.reuseAs(n, n)
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}
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} else if len(lu.pivot) != n {
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panic(ErrShape)
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}
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copy(lu.pivot, orig.pivot)
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lu.lu.Copy(orig.lu)
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}
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xs := getFloats(n, false)
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defer putFloats(xs)
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ys := getFloats(n, false)
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defer putFloats(ys)
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for i := 0; i < n; i++ {
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xs[i] = x.AtVec(i)
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ys[i] = y.AtVec(i)
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}
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// Adjust for the pivoting in the LU factorization
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for i, v := range lu.pivot {
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xs[i], xs[v] = xs[v], xs[i]
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}
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lum := lu.lu.mat
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omega := alpha
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for j := 0; j < n; j++ {
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ujj := lum.Data[j*lum.Stride+j]
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ys[j] /= ujj
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theta := 1 + xs[j]*ys[j]*omega
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beta := omega * ys[j] / theta
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gamma := omega * xs[j]
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omega -= beta * gamma
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lum.Data[j*lum.Stride+j] *= theta
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for i := j + 1; i < n; i++ {
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xs[i] -= lum.Data[i*lum.Stride+j] * xs[j]
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tmp := ys[i]
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ys[i] -= lum.Data[j*lum.Stride+i] * ys[j]
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lum.Data[i*lum.Stride+j] += beta * xs[i]
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lum.Data[j*lum.Stride+i] += gamma * tmp
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}
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}
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lu.updateCond(-1, CondNorm)
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}
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// LTo extracts the lower triangular matrix from an LU factorization.
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// If dst is nil, a new matrix is allocated. The resulting L matrix is returned.
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// LTo will panic if the receiver does not contain a factorization.
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func (lu *LU) LTo(dst *TriDense) *TriDense {
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if !lu.isValid() {
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panic(badLU)
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}
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_, n := lu.lu.Dims()
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if dst == nil {
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dst = NewTriDense(n, Lower, nil)
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} else {
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dst.reuseAs(n, Lower)
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}
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// Extract the lower triangular elements.
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for i := 0; i < n; i++ {
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for j := 0; j < i; j++ {
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dst.mat.Data[i*dst.mat.Stride+j] = lu.lu.mat.Data[i*lu.lu.mat.Stride+j]
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}
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}
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// Set ones on the diagonal.
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for i := 0; i < n; i++ {
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dst.mat.Data[i*dst.mat.Stride+i] = 1
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}
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return dst
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}
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// UTo extracts the upper triangular matrix from an LU factorization.
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// If dst is nil, a new matrix is allocated. The resulting U matrix is returned.
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// UTo will panic if the receiver does not contain a factorization.
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func (lu *LU) UTo(dst *TriDense) *TriDense {
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if !lu.isValid() {
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panic(badLU)
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}
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_, n := lu.lu.Dims()
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if dst == nil {
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dst = NewTriDense(n, Upper, nil)
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} else {
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dst.reuseAs(n, Upper)
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}
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// Extract the upper triangular elements.
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for i := 0; i < n; i++ {
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for j := i; j < n; j++ {
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dst.mat.Data[i*dst.mat.Stride+j] = lu.lu.mat.Data[i*lu.lu.mat.Stride+j]
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}
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}
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return dst
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}
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// Permutation constructs an r×r permutation matrix with the given row swaps.
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// A permutation matrix has exactly one element equal to one in each row and column
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// and all other elements equal to zero. swaps[i] specifies the row with which
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// i will be swapped, which is equivalent to the non-zero column of row i.
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func (m *Dense) Permutation(r int, swaps []int) {
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m.reuseAs(r, r)
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for i := 0; i < r; i++ {
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zero(m.mat.Data[i*m.mat.Stride : i*m.mat.Stride+r])
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v := swaps[i]
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if v < 0 || v >= r {
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panic(ErrRowAccess)
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}
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m.mat.Data[i*m.mat.Stride+v] = 1
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}
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}
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// SolveTo solves a system of linear equations using the LU decomposition of a matrix.
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// It computes
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// A * X = B if trans == false
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// A^T * X = B if trans == true
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// In both cases, A is represented in LU factorized form, and the matrix X is
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// stored into dst.
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//
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// If A is singular or near-singular a Condition error is returned. See
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// the documentation for Condition for more information.
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// SolveTo will panic if the receiver does not contain a factorization.
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func (lu *LU) SolveTo(dst *Dense, trans bool, b Matrix) error {
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if !lu.isValid() {
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panic(badLU)
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}
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_, n := lu.lu.Dims()
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br, bc := b.Dims()
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if br != n {
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panic(ErrShape)
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}
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// TODO(btracey): Should test the condition number instead of testing that
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// the determinant is exactly zero.
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if lu.Det() == 0 {
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return Condition(math.Inf(1))
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}
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dst.reuseAs(n, bc)
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bU, _ := untranspose(b)
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var restore func()
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if dst == bU {
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dst, restore = dst.isolatedWorkspace(bU)
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defer restore()
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} else if rm, ok := bU.(RawMatrixer); ok {
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dst.checkOverlap(rm.RawMatrix())
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}
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dst.Copy(b)
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t := blas.NoTrans
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if trans {
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t = blas.Trans
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}
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lapack64.Getrs(t, lu.lu.mat, dst.mat, lu.pivot)
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if lu.cond > ConditionTolerance {
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return Condition(lu.cond)
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}
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return nil
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}
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// SolveVecTo solves a system of linear equations using the LU decomposition of a matrix.
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// It computes
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// A * x = b if trans == false
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// A^T * x = b if trans == true
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// In both cases, A is represented in LU factorized form, and the vector x is
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// stored into dst.
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//
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// If A is singular or near-singular a Condition error is returned. See
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// the documentation for Condition for more information.
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// SolveVecTo will panic if the receiver does not contain a factorization.
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func (lu *LU) SolveVecTo(dst *VecDense, trans bool, b Vector) error {
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if !lu.isValid() {
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panic(badLU)
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}
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_, n := lu.lu.Dims()
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if br, bc := b.Dims(); br != n || bc != 1 {
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panic(ErrShape)
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}
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switch rv := b.(type) {
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default:
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dst.reuseAs(n)
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return lu.SolveTo(dst.asDense(), trans, b)
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case RawVectorer:
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if dst != b {
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dst.checkOverlap(rv.RawVector())
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}
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// TODO(btracey): Should test the condition number instead of testing that
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// the determinant is exactly zero.
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if lu.Det() == 0 {
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return Condition(math.Inf(1))
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}
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dst.reuseAs(n)
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var restore func()
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if dst == b {
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dst, restore = dst.isolatedWorkspace(b)
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defer restore()
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}
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dst.CopyVec(b)
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vMat := blas64.General{
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Rows: n,
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Cols: 1,
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Stride: dst.mat.Inc,
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Data: dst.mat.Data,
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}
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t := blas.NoTrans
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if trans {
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t = blas.Trans
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}
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lapack64.Getrs(t, lu.lu.mat, vMat, lu.pivot)
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if lu.cond > ConditionTolerance {
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return Condition(lu.cond)
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}
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return nil
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}
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}
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