// updateCond updates the stored condition number of the matrix. Norm is the
// norm of the original matrix. If norm is negative it will be estimated.
func (lu *LU) updateCond(norm float64) {
n := lu.lu.mat.Cols
work := make([]float64, 4*n)
iwork := make([]int, n)
if norm < 0 {
// This is an approximation. By the definition of a norm, ||AB|| <= ||A|| ||B||.
// The condition number is ||A|| || A^-1||, so this will underestimate
// the condition number somewhat.
// The norm of the original factorized matrix cannot be stored because of
// update possibilities, e.g. RankOne.
u := lu.lu.asTriDense(n, blas.NonUnit, blas.Upper)
l := lu.lu.asTriDense(n, blas.Unit, blas.Lower)
unorm := lapack64.Lantr(matrix.CondNorm, u.mat, work)
lnorm := lapack64.Lantr(matrix.CondNorm, l.mat, work)
norm = unorm * lnorm
}
v := lapack64.Gecon(matrix.CondNorm, lu.lu.mat, norm, work, iwork)
lu.cond = 1 / v
}
// updateCond updates the condition number of the Cholesky decomposition. If
// norm > 0, then that norm is used as the norm of the original matrix A, otherwise
// the norm is estimated from the decompositon.
func (c *Cholesky) updateCond(norm float64) {
n := c.chol.mat.N
work := make([]float64, 3*n)
if norm < 0 {
// This is an approximation. By the definition of a norm, ||AB|| <= ||A|| ||B||.
// Here, A = U^T * U.
// The condition number is ||A|| || A^-1||, so this will underestimate
// the condition number somewhat.
// The norm of the original factorized matrix cannot be stored because of
// update possibilites.
unorm := lapack64.Lantr(matrix.CondNorm, c.chol.mat, work)
lnorm := lapack64.Lantr(matrix.CondNormTrans, c.chol.mat, work)
norm = unorm * lnorm
}
sym := c.chol.asSymBlas()
iwork := make([]int, n)
v := lapack64.Pocon(sym, norm, work, iwork)
c.cond = 1 / v
}
// Norm returns the specified (induced) norm of the matrix a. See
// https://en.wikipedia.org/wiki/Matrix_norm for the definition of an induced norm.
//
// Valid norms are:
// 1 - The maximum absolute column sum
// 2 - Frobenius norm, the square root of the sum of the squares of the elements.
// Inf - The maximum absolute row sum.
// Norm will panic with ErrNormOrder if an illegal norm order is specified.
func Norm(a Matrix, norm float64) float64 {
r, c := a.Dims()
aMat, aTrans := untranspose(a)
var work []float64
switch rma := aMat.(type) {
case RawMatrixer:
rm := rma.RawMatrix()
n := normLapack(norm, aTrans)
if n == lapack.MaxColumnSum {
work = make([]float64, rm.Cols)
}
return lapack64.Lange(n, rm, work)
case RawTriangular:
rm := rma.RawTriangular()
n := normLapack(norm, aTrans)
if n == lapack.MaxRowSum || n == lapack.MaxColumnSum {
work = make([]float64, rm.N)
}
return lapack64.Lantr(n, rm, work)
case RawSymmetricer:
rm := rma.RawSymmetric()
n := normLapack(norm, aTrans)
if n == lapack.MaxRowSum || n == lapack.MaxColumnSum {
work = make([]float64, rm.N)
}
return lapack64.Lansy(n, rm, work)
}
switch norm {
default:
panic("unreachable")
case 1:
var max float64
for j := 0; j < c; j++ {
var sum float64
for i := 0; i < r; i++ {
sum += math.Abs(a.At(i, j))
}
if sum > max {
max = sum
}
}
return max
case 2:
var sum float64
for i := 0; i < r; i++ {
for j := 0; j < c; j++ {
v := a.At(i, j)
sum += v * v
}
}
return math.Sqrt(sum)
case math.Inf(1):
var max float64
for i := 0; i < r; i++ {
var sum float64
for j := 0; j < c; j++ {
sum += math.Abs(a.At(i, j))
}
if sum > max {
max = sum
}
}
return max
}
}
// Norm returns the specified (induced) norm of the matrix a. See
// https://en.wikipedia.org/wiki/Matrix_norm for the definition of an induced norm.
//
// Valid norms are:
// 1 - The maximum absolute column sum
// 2 - Frobenius norm, the square root of the sum of the squares of the elements.
// Inf - The maximum absolute row sum.
// Norm will panic with ErrNormOrder if an illegal norm order is specified and
// with matrix.ErrShape if the matrix has zero size.
func Norm(a Matrix, norm float64) float64 {
r, c := a.Dims()
if r == 0 || c == 0 {
panic(matrix.ErrShape)
}
aU, aTrans := untranspose(a)
var work []float64
switch rma := aU.(type) {
case RawMatrixer:
rm := rma.RawMatrix()
n := normLapack(norm, aTrans)
if n == lapack.MaxColumnSum {
work = make([]float64, rm.Cols)
}
return lapack64.Lange(n, rm, work)
case RawTriangular:
rm := rma.RawTriangular()
n := normLapack(norm, aTrans)
if n == lapack.MaxRowSum || n == lapack.MaxColumnSum {
work = make([]float64, rm.N)
}
return lapack64.Lantr(n, rm, work)
case RawSymmetricer:
rm := rma.RawSymmetric()
n := normLapack(norm, aTrans)
if n == lapack.MaxRowSum || n == lapack.MaxColumnSum {
work = make([]float64, rm.N)
}
return lapack64.Lansy(n, rm, work)
case *Vector:
rv := rma.RawVector()
switch norm {
default:
panic("unreachable")
case 1:
if aTrans {
imax := blas64.Iamax(rma.n, rv)
return math.Abs(rma.At(imax, 0))
}
return blas64.Asum(rma.n, rv)
case 2:
return blas64.Nrm2(rma.n, rv)
case math.Inf(1):
if aTrans {
return blas64.Asum(rma.n, rv)
}
imax := blas64.Iamax(rma.n, rv)
return math.Abs(rma.At(imax, 0))
}
}
switch norm {
default:
panic("unreachable")
case 1:
var max float64
for j := 0; j < c; j++ {
var sum float64
for i := 0; i < r; i++ {
sum += math.Abs(a.At(i, j))
}
if sum > max {
max = sum
}
}
return max
case 2:
var sum float64
for i := 0; i < r; i++ {
for j := 0; j < c; j++ {
v := a.At(i, j)
sum += v * v
}
}
return math.Sqrt(sum)
case math.Inf(1):
var max float64
for i := 0; i < r; i++ {
var sum float64
for j := 0; j < c; j++ {
sum += math.Abs(a.At(i, j))
}
if sum > max {
max = sum
}
}
return max
}
}