/
dtrevc3.go
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/
dtrevc3.go
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// Copyright ©2016 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 testlapack
import (
"fmt"
"math"
"testing"
"golang.org/x/exp/rand"
"gonum.org/v1/gonum/blas"
"gonum.org/v1/gonum/blas/blas64"
"gonum.org/v1/gonum/lapack"
)
type Dtrevc3er interface {
Dtrevc3(side lapack.EVSide, howmny lapack.EVHowMany, selected []bool, n int, t []float64, ldt int, vl []float64, ldvl int, vr []float64, ldvr int, mm int, work []float64, lwork int) int
}
func Dtrevc3Test(t *testing.T, impl Dtrevc3er) {
rnd := rand.New(rand.NewSource(1))
for _, side := range []lapack.EVSide{lapack.EVRight, lapack.EVLeft, lapack.EVBoth} {
var name string
switch side {
case lapack.EVRight:
name = "Rigth"
case lapack.EVLeft:
name = "Left"
case lapack.EVBoth:
name = "Both"
}
t.Run(name, func(t *testing.T) {
runDtrevc3Test(t, impl, rnd, side)
})
}
}
func runDtrevc3Test(t *testing.T, impl Dtrevc3er, rnd *rand.Rand, side lapack.EVSide) {
for _, n := range []int{0, 1, 2, 3, 4, 5, 6, 7, 10, 34} {
for _, extra := range []int{0, 11} {
for _, optwork := range []bool{true, false} {
for cas := 0; cas < 10; cas++ {
dtrevc3Test(t, impl, side, n, extra, optwork, rnd)
}
}
}
}
}
// dtrevc3Test tests Dtrevc3 by generating a random matrix T in Schur canonical
// form and performing the following checks:
// 1. Compute all eigenvectors of T and check that they are indeed correctly
// normalized eigenvectors
// 2. Compute selected eigenvectors and check that they are exactly equal to
// eigenvectors from check 1.
// 3. Compute all eigenvectors multiplied into a matrix Q and check that the
// result is equal to eigenvectors from step 1 multiplied by Q and scaled
// appropriately.
func dtrevc3Test(t *testing.T, impl Dtrevc3er, side lapack.EVSide, n, extra int, optwork bool, rnd *rand.Rand) {
const tol = 1e-15
name := fmt.Sprintf("n=%d,extra=%d,optwk=%v", n, extra, optwork)
right := side != lapack.EVLeft
left := side != lapack.EVRight
// Generate a random matrix in Schur canonical form possibly with tiny or zero eigenvalues.
// Zero elements of wi signify a real eigenvalue.
tmat, wr, wi := randomSchurCanonical(n, n+extra, true, rnd)
tmatCopy := cloneGeneral(tmat)
// 1. Compute all eigenvectors of T and check that they are indeed correctly
// normalized eigenvectors
howmny := lapack.EVAll
var vr, vl blas64.General
if right {
// Fill VR and VL with NaN because they should be completely overwritten in Dtrevc3.
vr = nanGeneral(n, n, n+extra)
}
if left {
vl = nanGeneral(n, n, n+extra)
}
var work []float64
if optwork {
work = []float64{0}
impl.Dtrevc3(side, howmny, nil, n, tmat.Data, tmat.Stride,
vl.Data, max(1, vl.Stride), vr.Data, max(1, vr.Stride), n, work, -1)
work = make([]float64, int(work[0]))
} else {
work = make([]float64, max(1, 3*n))
}
mGot := impl.Dtrevc3(side, howmny, nil, n, tmat.Data, tmat.Stride,
vl.Data, max(1, vl.Stride), vr.Data, max(1, vr.Stride), n, work, len(work))
if !generalOutsideAllNaN(tmat) {
t.Errorf("%v: out-of-range write to T", name)
}
if !equalGeneral(tmat, tmatCopy) {
t.Errorf("%v: unexpected modification of T", name)
}
if !generalOutsideAllNaN(vr) {
t.Errorf("%v: out-of-range write to VR", name)
}
if !generalOutsideAllNaN(vl) {
t.Errorf("%v: out-of-range write to VL", name)
}
mWant := n
if mGot != mWant {
t.Errorf("%v: unexpected value of m=%d, want %d", name, mGot, mWant)
}
if right {
resid := residualRightEV(tmat, vr, wr, wi)
if resid > tol {
t.Errorf("%v: unexpected right eigenvectors; residual=%v, want<=%v", name, resid, tol)
}
resid = residualEVNormalization(vr, wi)
if resid > tol {
t.Errorf("%v: unexpected normalization of right eigenvectors; residual=%v, want<=%v", name, resid, tol)
}
}
if left {
resid := residualLeftEV(tmat, vl, wr, wi)
if resid > tol {
t.Errorf("%v: unexpected left eigenvectors; residual=%v, want<=%v", name, resid, tol)
}
resid = residualEVNormalization(vl, wi)
if resid > tol {
t.Errorf("%v: unexpected normalization of left eigenvectors; residual=%v, want<=%v", name, resid, tol)
}
}
// 2. Compute selected eigenvectors and check that they are exactly equal to
// eigenvectors from check 1.
howmny = lapack.EVSelected
// Follow DCHKHS and select last max(1,n/4) real, max(1,n/4) complex
// eigenvectors instead of selecting them randomly.
selected := make([]bool, n)
selectedWant := make([]bool, n)
var nselr, nselc int
for j := n - 1; j > 0; {
if wi[j] == 0 {
if nselr < max(1, n/4) {
nselr++
selected[j] = true
selectedWant[j] = true
}
j--
} else {
if nselc < max(1, n/4) {
nselc++
// Select all columns to check that Dtrevc3 normalizes 'selected' correctly.
selected[j] = true
selected[j-1] = true
selectedWant[j] = false
selectedWant[j-1] = true
}
j -= 2
}
}
mWant = nselr + 2*nselc
var vrSel, vlSel blas64.General
if right {
vrSel = nanGeneral(n, mWant, n+extra)
}
if left {
vlSel = nanGeneral(n, mWant, n+extra)
}
if optwork {
// Reallocate optimal work in case it depends on howmny and selected.
work = []float64{0}
impl.Dtrevc3(side, howmny, selected, n, tmat.Data, tmat.Stride,
vlSel.Data, max(1, vlSel.Stride), vrSel.Data, max(1, vrSel.Stride), mWant, work, -1)
work = make([]float64, int(work[0]))
}
mGot = impl.Dtrevc3(side, howmny, selected, n, tmat.Data, tmat.Stride,
vlSel.Data, max(1, vlSel.Stride), vrSel.Data, max(1, vrSel.Stride), mWant, work, len(work))
if !generalOutsideAllNaN(tmat) {
t.Errorf("%v: out-of-range write to T", name)
}
if !equalGeneral(tmat, tmatCopy) {
t.Errorf("%v: unexpected modification of T", name)
}
if !generalOutsideAllNaN(vrSel) {
t.Errorf("%v: out-of-range write to selected VR", name)
}
if !generalOutsideAllNaN(vlSel) {
t.Errorf("%v: out-of-range write to selected VL", name)
}
if mGot != mWant {
t.Errorf("%v: unexpected value of selected m=%d, want %d", name, mGot, mWant)
}
for i := range selected {
if selected[i] != selectedWant[i] {
t.Errorf("%v: unexpected selected[%v]", name, i)
}
}
// Check that selected columns of vrSel are equal to the corresponding
// columns of vr.
var k int
match := true
if right {
loopVR:
for j := 0; j < n; j++ {
if selected[j] && wi[j] == 0 {
for i := 0; i < n; i++ {
if vrSel.Data[i*vrSel.Stride+k] != vr.Data[i*vr.Stride+j] {
match = false
break loopVR
}
}
k++
} else if selected[j] && wi[j] != 0 {
for i := 0; i < n; i++ {
if vrSel.Data[i*vrSel.Stride+k] != vr.Data[i*vr.Stride+j] ||
vrSel.Data[i*vrSel.Stride+k+1] != vr.Data[i*vr.Stride+j+1] {
match = false
break loopVR
}
}
k += 2
}
}
}
if !match {
t.Errorf("%v: unexpected selected VR", name)
}
// Check that selected columns of vlSel are equal to the corresponding
// columns of vl.
match = true
k = 0
if left {
loopVL:
for j := 0; j < n; j++ {
if selected[j] && wi[j] == 0 {
for i := 0; i < n; i++ {
if vlSel.Data[i*vlSel.Stride+k] != vl.Data[i*vl.Stride+j] {
match = false
break loopVL
}
}
k++
} else if selected[j] && wi[j] != 0 {
for i := 0; i < n; i++ {
if vlSel.Data[i*vlSel.Stride+k] != vl.Data[i*vl.Stride+j] ||
vlSel.Data[i*vlSel.Stride+k+1] != vl.Data[i*vl.Stride+j+1] {
match = false
break loopVL
}
}
k += 2
}
}
}
if !match {
t.Errorf("%v: unexpected selected VL", name)
}
// 3. Compute all eigenvectors multiplied into a matrix Q and check that the
// result is equal to eigenvectors from step 1 multiplied by Q and scaled
// appropriately.
howmny = lapack.EVAllMulQ
var vrMul, qr blas64.General
var vlMul, ql blas64.General
if right {
vrMul = randomGeneral(n, n, n+extra, rnd)
qr = cloneGeneral(vrMul)
}
if left {
vlMul = randomGeneral(n, n, n+extra, rnd)
ql = cloneGeneral(vlMul)
}
if optwork {
// Reallocate optimal work in case it depends on howmny and selected.
work = []float64{0}
impl.Dtrevc3(side, howmny, nil, n, tmat.Data, tmat.Stride,
vlMul.Data, max(1, vlMul.Stride), vrMul.Data, max(1, vrMul.Stride), n, work, -1)
work = make([]float64, int(work[0]))
}
mGot = impl.Dtrevc3(side, howmny, selected, n, tmat.Data, tmat.Stride,
vlMul.Data, max(1, vlMul.Stride), vrMul.Data, max(1, vrMul.Stride), n, work, len(work))
if !generalOutsideAllNaN(tmat) {
t.Errorf("%v: out-of-range write to T", name)
}
if !equalGeneral(tmat, tmatCopy) {
t.Errorf("%v: unexpected modification of T", name)
}
if !generalOutsideAllNaN(vrMul) {
t.Errorf("%v: out-of-range write to VRMul", name)
}
if !generalOutsideAllNaN(vlMul) {
t.Errorf("%v: out-of-range write to VLMul", name)
}
mWant = n
if mGot != mWant {
t.Errorf("%v: unexpected value of m=%d, want %d", name, mGot, mWant)
}
if right {
// Compute Q * VR explicitly and normalize to match Dtrevc3 output.
qvWant := zeros(n, n, n)
blas64.Gemm(blas.NoTrans, blas.NoTrans, 1, qr, vr, 0, qvWant)
normalizeEV(qvWant, wi)
// Compute the difference between Dtrevc3 output and Q * VR.
r := zeros(n, n, n)
for i := 0; i < n; i++ {
for j := 0; j < n; j++ {
r.Data[i*r.Stride+j] = vrMul.Data[i*vrMul.Stride+j] - qvWant.Data[i*qvWant.Stride+j]
}
}
qvNorm := dlange(lapack.MaxColumnSum, n, n, qvWant.Data, qvWant.Stride)
resid := dlange(lapack.MaxColumnSum, n, n, r.Data, r.Stride) / qvNorm / float64(n)
if resid > tol {
t.Errorf("%v: unexpected VRMul; resid=%v, want <=%v", name, resid, tol)
}
}
if left {
// Compute Q * VL explicitly and normalize to match Dtrevc3 output.
qvWant := zeros(n, n, n)
blas64.Gemm(blas.NoTrans, blas.NoTrans, 1, ql, vl, 0, qvWant)
normalizeEV(qvWant, wi)
// Compute the difference between Dtrevc3 output and Q * VL.
r := zeros(n, n, n)
for i := 0; i < n; i++ {
for j := 0; j < n; j++ {
r.Data[i*r.Stride+j] = vlMul.Data[i*vlMul.Stride+j] - qvWant.Data[i*qvWant.Stride+j]
}
}
qvNorm := dlange(lapack.MaxColumnSum, n, n, qvWant.Data, qvWant.Stride)
resid := dlange(lapack.MaxColumnSum, n, n, r.Data, r.Stride) / qvNorm / float64(n)
if resid > tol {
t.Errorf("%v: unexpected VLMul; resid=%v, want <=%v", name, resid, tol)
}
}
}
// residualEVNormalization returns the maximum normalization error in E:
// max |max-norm(E[:,j]) - 1|
func residualEVNormalization(emat blas64.General, wi []float64) float64 {
n := emat.Rows
if n == 0 {
return 0
}
var (
e = emat.Data
lde = emat.Stride
enrmin = math.Inf(1)
enrmax float64
ipair int
)
for j := 0; j < n; j++ {
if ipair == 0 && j < n-1 && wi[j] != 0 {
ipair = 1
}
var nrm float64
switch ipair {
case 0:
// Real eigenvector
for i := 0; i < n; i++ {
nrm = math.Max(nrm, math.Abs(e[i*lde+j]))
}
enrmin = math.Min(enrmin, nrm)
enrmax = math.Max(enrmax, nrm)
case 1:
// Complex eigenvector
for i := 0; i < n; i++ {
nrm = math.Max(nrm, math.Abs(e[i*lde+j])+math.Abs(e[i*lde+j+1]))
}
enrmin = math.Min(enrmin, nrm)
enrmax = math.Max(enrmax, nrm)
ipair = 2
case 2:
ipair = 0
}
}
return math.Max(math.Abs(enrmin-1), math.Abs(enrmin-1))
}
// normalizeEV normalizes eigenvectors in the columns of E so that the element
// of largest magnitude has magnitude 1.
func normalizeEV(emat blas64.General, wi []float64) {
n := emat.Rows
if n == 0 {
return
}
var (
bi = blas64.Implementation()
e = emat.Data
lde = emat.Stride
ipair int
)
for j := 0; j < n; j++ {
if ipair == 0 && j < n-1 && wi[j] != 0 {
ipair = 1
}
switch ipair {
case 0:
// Real eigenvector
ii := bi.Idamax(n, e[j:], lde)
remax := 1 / math.Abs(e[ii*lde+j])
bi.Dscal(n, remax, e[j:], lde)
case 1:
// Complex eigenvector
var emax float64
for i := 0; i < n; i++ {
emax = math.Max(emax, math.Abs(e[i*lde+j])+math.Abs(e[i*lde+j+1]))
}
bi.Dscal(n, 1/emax, e[j:], lde)
bi.Dscal(n, 1/emax, e[j+1:], lde)
ipair = 2
case 2:
ipair = 0
}
}
}