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// Copyright ©2015 The gonum Authors. All rights reserved.
// Use of this source code is governed by a BSD-style
// license that can be found in the LICENSE file.
package gonum
import (
"math"
"gonum.org/v1/gonum/lapack"
)
// Dlange computes the matrix norm of the general m×n matrix a. The input norm
// specifies the norm computed.
// lapack.MaxAbs: the maximum absolute value of an element.
// lapack.MaxColumnSum: the maximum column sum of the absolute values of the entries.
// lapack.MaxRowSum: the maximum row sum of the absolute values of the entries.
// lapack.NormFrob: the square root of the sum of the squares of the entries.
// If norm == lapack.MaxColumnSum, work must be of length n, and this function will panic otherwise.
// There are no restrictions on work for the other matrix norms.
func (impl Implementation) Dlange(norm lapack.MatrixNorm, m, n int, a []float64, lda int, work []float64) float64 {
// TODO(btracey): These should probably be refactored to use BLAS calls.
checkMatrix(m, n, a, lda)
switch norm {
case lapack.MaxRowSum, lapack.MaxColumnSum, lapack.NormFrob, lapack.MaxAbs:
default:
panic(badNorm)
}
if norm == lapack.MaxColumnSum && len(work) < n {
panic(badWork)
}
if m == 0 && n == 0 {
return 0
}
if norm == lapack.MaxAbs {
var value float64
for i := 0; i < m; i++ {
for j := 0; j < n; j++ {
value = math.Max(value, math.Abs(a[i*lda+j]))
}
}
return value
}
if norm == lapack.MaxColumnSum {
if len(work) < n {
panic(badWork)
}
for i := 0; i < n; i++ {
work[i] = 0
}
for i := 0; i < m; i++ {
for j := 0; j < n; j++ {
work[j] += math.Abs(a[i*lda+j])
}
}
var value float64
for i := 0; i < n; i++ {
value = math.Max(value, work[i])
}
return value
}
if norm == lapack.MaxRowSum {
var value float64
for i := 0; i < m; i++ {
var sum float64
for j := 0; j < n; j++ {
sum += math.Abs(a[i*lda+j])
}
value = math.Max(value, sum)
}
return value
}
if norm == lapack.NormFrob {
var value float64
scale := 0.0
sum := 1.0
for i := 0; i < m; i++ {
scale, sum = impl.Dlassq(n, a[i*lda:], 1, scale, sum)
}
value = scale * math.Sqrt(sum)
return value
}
panic("lapack: bad matrix norm")
}