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greatly improve dlanhs impl, docs and tests

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This commit is contained in:
soypat
2023-09-06 18:34:53 -03:00
committed by Vladimír Chalupecký
parent 92392f3782
commit b79b9f7740
2 changed files with 12 additions and 6 deletions
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10
lapack/gonum/dlanhs.go
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@@ -15,15 +15,19 @@ import (
// the infinity norm, or the element of largest absolute value of a // the infinity norm, or the element of largest absolute value of a
// Hessenberg matrix A. // Hessenberg matrix A.
// //
// On using norm=lapack.MaxRowSum, the vector work must have length n. // On using norm=lapack.MaxColumnSum, the vector work must have length n.
func (impl Implementation) Dlanhs(norm lapack.MatrixNorm, n int, a []float64, lda int, work []float64) float64 { func (impl Implementation) Dlanhs(norm lapack.MatrixNorm, n int, a []float64, lda int, work []float64) float64 {
switch { switch {
case n < 0: case n < 0:
panic(nLT0) panic(nLT0)
case lda < max(1, n): case lda < max(1, n):
panic(badLdA) panic(badLdA)
case norm == lapack.MaxRowSum && len(work) < n: case len(a) < (n-1)*lda+n:
panic(shortA)
case norm == lapack.MaxColumnSum && len(work) < n:
panic(badLWork) panic(badLWork)
case norm != lapack.MaxRowSum && norm != lapack.MaxAbs && norm != lapack.MaxColumnSum && norm != lapack.Frobenius:
panic(badNorm)
} }
if n == 0 { if n == 0 {
return 0 // Early return. return 0 // Early return.
@@ -32,8 +36,6 @@ func (impl Implementation) Dlanhs(norm lapack.MatrixNorm, n int, a []float64, ld
bi := blas64.Implementation() bi := blas64.Implementation()
var value float64 var value float64
switch norm { switch norm {
default:
panic(badNorm)
case lapack.MaxAbs: case lapack.MaxAbs:
for i := 0; i < n; i++ { for i := 0; i < n; i++ {
minj := max(0, i-1) minj := max(0, i-1)

8
lapack/testlapack/dlanhs.go
View File

@@ -19,13 +19,17 @@ type Dlanhser interface {
func DlanhsTest(t *testing.T, impl Dlanhser) { func DlanhsTest(t *testing.T, impl Dlanhser) {
const tol = 1e-15 const tol = 1e-15
work := make([]float64, 9) allwork := make([]float64, 9)
rnd := rand.New(rand.NewSource(1)) rnd := rand.New(rand.NewSource(1))
for _, n := range []int{1, 2, 4, 9} { for _, n := range []int{1, 2, 4, 9} {
for _, lda := range []int{n, n + 5} { for _, lda := range []int{n, n + 5} {
a := randomHessenberg(n, lda, rnd) a := randomHessenberg(n, lda, rnd)
for _, norm := range []lapack.MatrixNorm{lapack.MaxAbs, lapack.MaxRowSum, lapack.MaxColumnSum, lapack.Frobenius} { for _, norm := range []lapack.MatrixNorm{lapack.MaxAbs, lapack.MaxRowSum, lapack.MaxColumnSum, lapack.Frobenius} {
for i := range work[:n] { var work []float64
if norm == lapack.MaxColumnSum {
work = allwork[:n]
}
for i := range work {
work[i] = math.NaN() work[i] = math.NaN()
} }
want := dlange(norm, a.Rows, a.Cols, a.Data, a.Stride) want := dlange(norm, a.Rows, a.Cols, a.Data, a.Stride)