mirror of https://github.com/k3s-io/k3s
85 lines
2.2 KiB
Go
85 lines
2.2 KiB
Go
// Copyright ©2015 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 gonum
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import (
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"math"
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"gonum.org/v1/gonum/lapack"
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)
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// Dlange computes the matrix norm of the general m×n matrix a. The input norm
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// specifies the norm computed.
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// lapack.MaxAbs: the maximum absolute value of an element.
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// lapack.MaxColumnSum: the maximum column sum of the absolute values of the entries.
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// lapack.MaxRowSum: the maximum row sum of the absolute values of the entries.
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// lapack.NormFrob: the square root of the sum of the squares of the entries.
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// If norm == lapack.MaxColumnSum, work must be of length n, and this function will panic otherwise.
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// There are no restrictions on work for the other matrix norms.
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func (impl Implementation) Dlange(norm lapack.MatrixNorm, m, n int, a []float64, lda int, work []float64) float64 {
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// TODO(btracey): These should probably be refactored to use BLAS calls.
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checkMatrix(m, n, a, lda)
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switch norm {
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case lapack.MaxRowSum, lapack.MaxColumnSum, lapack.NormFrob, lapack.MaxAbs:
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default:
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panic(badNorm)
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}
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if norm == lapack.MaxColumnSum && len(work) < n {
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panic(badWork)
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}
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if m == 0 && n == 0 {
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return 0
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}
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if norm == lapack.MaxAbs {
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var value float64
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for i := 0; i < m; i++ {
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for j := 0; j < n; j++ {
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value = math.Max(value, math.Abs(a[i*lda+j]))
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}
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}
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return value
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}
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if norm == lapack.MaxColumnSum {
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if len(work) < n {
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panic(badWork)
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}
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for i := 0; i < n; i++ {
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work[i] = 0
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}
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for i := 0; i < m; i++ {
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for j := 0; j < n; j++ {
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work[j] += math.Abs(a[i*lda+j])
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}
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}
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var value float64
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for i := 0; i < n; i++ {
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value = math.Max(value, work[i])
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}
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return value
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}
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if norm == lapack.MaxRowSum {
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var value float64
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for i := 0; i < m; i++ {
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var sum float64
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for j := 0; j < n; j++ {
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sum += math.Abs(a[i*lda+j])
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}
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value = math.Max(value, sum)
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}
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return value
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}
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if norm == lapack.NormFrob {
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var value float64
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scale := 0.0
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sum := 1.0
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for i := 0; i < m; i++ {
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scale, sum = impl.Dlassq(n, a[i*lda:], 1, scale, sum)
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}
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value = scale * math.Sqrt(sum)
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return value
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}
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panic("lapack: bad matrix norm")
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}
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