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// Code generated by "go generate gonum.org/v1/gonum/blas/gonum”; DO NOT EDIT.
// Copyright ©2014 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 (
"gonum.org/v1/gonum/blas"
"gonum.org/v1/gonum/internal/asm/f32"
)
var _ blas.Float32Level3 = Implementation{}
// Strsm solves one of the matrix equations
// A * X = alpha * B if tA == blas.NoTrans and side == blas.Left
// Aᵀ * X = alpha * B if tA == blas.Trans or blas.ConjTrans, and side == blas.Left
// X * A = alpha * B if tA == blas.NoTrans and side == blas.Right
// X * Aᵀ = alpha * B if tA == blas.Trans or blas.ConjTrans, and side == blas.Right
// where A is an n×n or m×m triangular matrix, X and B are m×n matrices, and alpha is a
// scalar.
//
// At entry to the function, X contains the values of B, and the result is
// stored in-place into X.
//
// No check is made that A is invertible.
//
// Float32 implementations are autogenerated and not directly tested.
func (Implementation) Strsm(s blas.Side, ul blas.Uplo, tA blas.Transpose, d blas.Diag, m, n int, alpha float32, a []float32, lda int, b []float32, ldb int) {
if s != blas.Left && s != blas.Right {
panic(badSide)
}
if ul != blas.Lower && ul != blas.Upper {
panic(badUplo)
}
if tA != blas.NoTrans && tA != blas.Trans && tA != blas.ConjTrans {
panic(badTranspose)
}
if d != blas.NonUnit && d != blas.Unit {
panic(badDiag)
}
if m < 0 {
panic(mLT0)
}
if n < 0 {
panic(nLT0)
}
k := n
if s == blas.Left {
k = m
}
if lda < max(1, k) {
panic(badLdA)
}
if ldb < max(1, n) {
panic(badLdB)
}
// Quick return if possible.
if m == 0 || n == 0 {
return
}
// For zero matrix size the following slice length checks are trivially satisfied.
if len(a) < lda*(k-1)+k {
panic(shortA)
}
if len(b) < ldb*(m-1)+n {
panic(shortB)
}
if alpha == 0 {
for i := 0; i < m; i++ {
btmp := b[i*ldb : i*ldb+n]
for j := range btmp {
btmp[j] = 0
}
}
return
}
nonUnit := d == blas.NonUnit
if s == blas.Left {
if tA == blas.NoTrans {
if ul == blas.Upper {
for i := m - 1; i >= 0; i-- {
btmp := b[i*ldb : i*ldb+n]
if alpha != 1 {
f32.ScalUnitary(alpha, btmp)
}
for ka, va := range a[i*lda+i+1 : i*lda+m] {
if va != 0 {
k := ka + i + 1
f32.AxpyUnitary(-va, b[k*ldb:k*ldb+n], btmp)
}
}
if nonUnit {
tmp := 1 / a[i*lda+i]
f32.ScalUnitary(tmp, btmp)
}
}
return
}
for i := 0; i < m; i++ {
btmp := b[i*ldb : i*ldb+n]
if alpha != 1 {
f32.ScalUnitary(alpha, btmp)
}
for k, va := range a[i*lda : i*lda+i] {
if va != 0 {
f32.AxpyUnitary(-va, b[k*ldb:k*ldb+n], btmp)
}
}
if nonUnit {
tmp := 1 / a[i*lda+i]
f32.ScalUnitary(tmp, btmp)
}
}
return
}
// Cases where a is transposed
if ul == blas.Upper {
for k := 0; k < m; k++ {
btmpk := b[k*ldb : k*ldb+n]
if nonUnit {
tmp := 1 / a[k*lda+k]
f32.ScalUnitary(tmp, btmpk)
}
for ia, va := range a[k*lda+k+1 : k*lda+m] {
if va != 0 {
i := ia + k + 1
f32.AxpyUnitary(-va, btmpk, b[i*ldb:i*ldb+n])
}
}
if alpha != 1 {
f32.ScalUnitary(alpha, btmpk)
}
}
return
}
for k := m - 1; k >= 0; k-- {
btmpk := b[k*ldb : k*ldb+n]
if nonUnit {
tmp := 1 / a[k*lda+k]
f32.ScalUnitary(tmp, btmpk)
}
for i, va := range a[k*lda : k*lda+k] {
if va != 0 {
f32.AxpyUnitary(-va, btmpk, b[i*ldb:i*ldb+n])
}
}
if alpha != 1 {
f32.ScalUnitary(alpha, btmpk)
}
}
return
}
// Cases where a is to the right of X.
if tA == blas.NoTrans {
if ul == blas.Upper {
for i := 0; i < m; i++ {
btmp := b[i*ldb : i*ldb+n]
if alpha != 1 {
f32.ScalUnitary(alpha, btmp)
}
for k, vb := range btmp {
if vb == 0 {
continue
}
if nonUnit {
btmp[k] /= a[k*lda+k]
}
f32.AxpyUnitary(-btmp[k], a[k*lda+k+1:k*lda+n], btmp[k+1:n])
}
}
return
}
for i := 0; i < m; i++ {
btmp := b[i*ldb : i*ldb+n]
if alpha != 1 {
f32.ScalUnitary(alpha, btmp)
}
for k := n - 1; k >= 0; k-- {
if btmp[k] == 0 {
continue
}
if nonUnit {
btmp[k] /= a[k*lda+k]
}
f32.AxpyUnitary(-btmp[k], a[k*lda:k*lda+k], btmp[:k])
}
}
return
}
// Cases where a is transposed.
if ul == blas.Upper {
for i := 0; i < m; i++ {
btmp := b[i*ldb : i*ldb+n]
for j := n - 1; j >= 0; j-- {
tmp := alpha*btmp[j] - f32.DotUnitary(a[j*lda+j+1:j*lda+n], btmp[j+1:])
if nonUnit {
tmp /= a[j*lda+j]
}
btmp[j] = tmp
}
}
return
}
for i := 0; i < m; i++ {
btmp := b[i*ldb : i*ldb+n]
for j := 0; j < n; j++ {
tmp := alpha*btmp[j] - f32.DotUnitary(a[j*lda:j*lda+j], btmp[:j])
if nonUnit {
tmp /= a[j*lda+j]
}
btmp[j] = tmp
}
}
}
// Ssymm performs one of the matrix-matrix operations
// C = alpha * A * B + beta * C if side == blas.Left
// C = alpha * B * A + beta * C if side == blas.Right
// where A is an n×n or m×m symmetric matrix, B and C are m×n matrices, and alpha
// is a scalar.
//
// Float32 implementations are autogenerated and not directly tested.
func (Implementation) Ssymm(s blas.Side, ul blas.Uplo, m, n int, alpha float32, a []float32, lda int, b []float32, ldb int, beta float32, c []float32, ldc int) {
if s != blas.Right && s != blas.Left {
panic(badSide)
}
if ul != blas.Lower && ul != blas.Upper {
panic(badUplo)
}
if m < 0 {
panic(mLT0)
}
if n < 0 {
panic(nLT0)
}
k := n
if s == blas.Left {
k = m
}
if lda < max(1, k) {
panic(badLdA)
}
if ldb < max(1, n) {
panic(badLdB)
}
if ldc < max(1, n) {
panic(badLdC)
}
// Quick return if possible.
if m == 0 || n == 0 {
return
}
// For zero matrix size the following slice length checks are trivially satisfied.
if len(a) < lda*(k-1)+k {
panic(shortA)
}
if len(b) < ldb*(m-1)+n {
panic(shortB)
}
if len(c) < ldc*(m-1)+n {
panic(shortC)
}
// Quick return if possible.
if alpha == 0 && beta == 1 {
return
}
if beta == 0 {
for i := 0; i < m; i++ {
ctmp := c[i*ldc : i*ldc+n]
for j := range ctmp {
ctmp[j] = 0
}
}
}
if alpha == 0 {
if beta != 0 {
for i := 0; i < m; i++ {
ctmp := c[i*ldc : i*ldc+n]
for j := 0; j < n; j++ {
ctmp[j] *= beta
}
}
}
return
}
isUpper := ul == blas.Upper
if s == blas.Left {
for i := 0; i < m; i++ {
atmp := alpha * a[i*lda+i]
btmp := b[i*ldb : i*ldb+n]
ctmp := c[i*ldc : i*ldc+n]
for j, v := range btmp {
ctmp[j] *= beta
ctmp[j] += atmp * v
}
for k := 0; k < i; k++ {
var atmp float32
if isUpper {
atmp = a[k*lda+i]
} else {
atmp = a[i*lda+k]
}
atmp *= alpha
f32.AxpyUnitary(atmp, b[k*ldb:k*ldb+n], ctmp)
}
for k := i + 1; k < m; k++ {
var atmp float32
if isUpper {
atmp = a[i*lda+k]
} else {
atmp = a[k*lda+i]
}
atmp *= alpha
f32.AxpyUnitary(atmp, b[k*ldb:k*ldb+n], ctmp)
}
}
return
}
if isUpper {
for i := 0; i < m; i++ {
for j := n - 1; j >= 0; j-- {
tmp := alpha * b[i*ldb+j]
var tmp2 float32
atmp := a[j*lda+j+1 : j*lda+n]
btmp := b[i*ldb+j+1 : i*ldb+n]
ctmp := c[i*ldc+j+1 : i*ldc+n]
for k, v := range atmp {
ctmp[k] += tmp * v
tmp2 += btmp[k] * v
}
c[i*ldc+j] *= beta
c[i*ldc+j] += tmp*a[j*lda+j] + alpha*tmp2
}
}
return
}
for i := 0; i < m; i++ {
for j := 0; j < n; j++ {
tmp := alpha * b[i*ldb+j]
var tmp2 float32
atmp := a[j*lda : j*lda+j]
btmp := b[i*ldb : i*ldb+j]
ctmp := c[i*ldc : i*ldc+j]
for k, v := range atmp {
ctmp[k] += tmp * v
tmp2 += btmp[k] * v
}
c[i*ldc+j] *= beta
c[i*ldc+j] += tmp*a[j*lda+j] + alpha*tmp2
}
}
}
// Ssyrk performs one of the symmetric rank-k operations
// C = alpha * A * Aᵀ + beta * C if tA == blas.NoTrans
// C = alpha * Aᵀ * A + beta * C if tA == blas.Trans or tA == blas.ConjTrans
// where A is an n×k or k×n matrix, C is an n×n symmetric matrix, and alpha and
// beta are scalars.
//
// Float32 implementations are autogenerated and not directly tested.
func (Implementation) Ssyrk(ul blas.Uplo, tA blas.Transpose, n, k int, alpha float32, a []float32, lda int, beta float32, c []float32, ldc int) {
if ul != blas.Lower && ul != blas.Upper {
panic(badUplo)
}
if tA != blas.Trans && tA != blas.NoTrans && tA != blas.ConjTrans {
panic(badTranspose)
}
if n < 0 {
panic(nLT0)
}
if k < 0 {
panic(kLT0)
}
row, col := k, n
if tA == blas.NoTrans {
row, col = n, k
}
if lda < max(1, col) {
panic(badLdA)
}
if ldc < max(1, n) {
panic(badLdC)
}
// Quick return if possible.
if n == 0 {
return
}
// For zero matrix size the following slice length checks are trivially satisfied.
if len(a) < lda*(row-1)+col {
panic(shortA)
}
if len(c) < ldc*(n-1)+n {
panic(shortC)
}
if alpha == 0 {
if beta == 0 {
if ul == blas.Upper {
for i := 0; i < n; i++ {
ctmp := c[i*ldc+i : i*ldc+n]
for j := range ctmp {
ctmp[j] = 0
}
}
return
}
for i := 0; i < n; i++ {
ctmp := c[i*ldc : i*ldc+i+1]
for j := range ctmp {
ctmp[j] = 0
}
}
return
}
if ul == blas.Upper {
for i := 0; i < n; i++ {
ctmp := c[i*ldc+i : i*ldc+n]
for j := range ctmp {
ctmp[j] *= beta
}
}
return
}
for i := 0; i < n; i++ {
ctmp := c[i*ldc : i*ldc+i+1]
for j := range ctmp {
ctmp[j] *= beta
}
}
return
}
if tA == blas.NoTrans {
if ul == blas.Upper {
for i := 0; i < n; i++ {
ctmp := c[i*ldc+i : i*ldc+n]
atmp := a[i*lda : i*lda+k]
if beta == 0 {
for jc := range ctmp {
j := jc + i
ctmp[jc] = alpha * f32.DotUnitary(atmp, a[j*lda:j*lda+k])
}
} else {
for jc, vc := range ctmp {
j := jc + i
ctmp[jc] = vc*beta + alpha*f32.DotUnitary(atmp, a[j*lda:j*lda+k])
}
}
}
return
}
for i := 0; i < n; i++ {
ctmp := c[i*ldc : i*ldc+i+1]
atmp := a[i*lda : i*lda+k]
if beta == 0 {
for j := range ctmp {
ctmp[j] = alpha * f32.DotUnitary(a[j*lda:j*lda+k], atmp)
}
} else {
for j, vc := range ctmp {
ctmp[j] = vc*beta + alpha*f32.DotUnitary(a[j*lda:j*lda+k], atmp)
}
}
}
return
}
// Cases where a is transposed.
if ul == blas.Upper {
for i := 0; i < n; i++ {
ctmp := c[i*ldc+i : i*ldc+n]
if beta == 0 {
for j := range ctmp {
ctmp[j] = 0
}
} else if beta != 1 {
for j := range ctmp {
ctmp[j] *= beta
}
}
for l := 0; l < k; l++ {
tmp := alpha * a[l*lda+i]
if tmp != 0 {
f32.AxpyUnitary(tmp, a[l*lda+i:l*lda+n], ctmp)
}
}
}
return
}
for i := 0; i < n; i++ {
ctmp := c[i*ldc : i*ldc+i+1]
if beta != 1 {
for j := range ctmp {
ctmp[j] *= beta
}
}
for l := 0; l < k; l++ {
tmp := alpha * a[l*lda+i]
if tmp != 0 {
f32.AxpyUnitary(tmp, a[l*lda:l*lda+i+1], ctmp)
}
}
}
}
// Ssyr2k performs one of the symmetric rank 2k operations
// C = alpha * A * Bᵀ + alpha * B * Aᵀ + beta * C if tA == blas.NoTrans
// C = alpha * Aᵀ * B + alpha * Bᵀ * A + beta * C if tA == blas.Trans or tA == blas.ConjTrans
// where A and B are n×k or k×n matrices, C is an n×n symmetric matrix, and
// alpha and beta are scalars.
//
// Float32 implementations are autogenerated and not directly tested.
func (Implementation) Ssyr2k(ul blas.Uplo, tA blas.Transpose, n, k int, alpha float32, a []float32, lda int, b []float32, ldb int, beta float32, c []float32, ldc int) {
if ul != blas.Lower && ul != blas.Upper {
panic(badUplo)
}
if tA != blas.Trans && tA != blas.NoTrans && tA != blas.ConjTrans {
panic(badTranspose)
}
if n < 0 {
panic(nLT0)
}
if k < 0 {
panic(kLT0)
}
row, col := k, n
if tA == blas.NoTrans {
row, col = n, k
}
if lda < max(1, col) {
panic(badLdA)
}
if ldb < max(1, col) {
panic(badLdB)
}
if ldc < max(1, n) {
panic(badLdC)
}
// Quick return if possible.
if n == 0 {
return
}
// For zero matrix size the following slice length checks are trivially satisfied.
if len(a) < lda*(row-1)+col {
panic(shortA)
}
if len(b) < ldb*(row-1)+col {
panic(shortB)
}
if len(c) < ldc*(n-1)+n {
panic(shortC)
}
if alpha == 0 {
if beta == 0 {
if ul == blas.Upper {
for i := 0; i < n; i++ {
ctmp := c[i*ldc+i : i*ldc+n]
for j := range ctmp {
ctmp[j] = 0
}
}
return
}
for i := 0; i < n; i++ {
ctmp := c[i*ldc : i*ldc+i+1]
for j := range ctmp {
ctmp[j] = 0
}
}
return
}
if ul == blas.Upper {
for i := 0; i < n; i++ {
ctmp := c[i*ldc+i : i*ldc+n]
for j := range ctmp {
ctmp[j] *= beta
}
}
return
}
for i := 0; i < n; i++ {
ctmp := c[i*ldc : i*ldc+i+1]
for j := range ctmp {
ctmp[j] *= beta
}
}
return
}
if tA == blas.NoTrans {
if ul == blas.Upper {
for i := 0; i < n; i++ {
atmp := a[i*lda : i*lda+k]
btmp := b[i*ldb : i*ldb+k]
ctmp := c[i*ldc+i : i*ldc+n]
if beta == 0 {
for jc := range ctmp {
j := i + jc
var tmp1, tmp2 float32
binner := b[j*ldb : j*ldb+k]
for l, v := range a[j*lda : j*lda+k] {
tmp1 += v * btmp[l]
tmp2 += atmp[l] * binner[l]
}
ctmp[jc] = alpha * (tmp1 + tmp2)
}
} else {
for jc := range ctmp {
j := i + jc
var tmp1, tmp2 float32
binner := b[j*ldb : j*ldb+k]
for l, v := range a[j*lda : j*lda+k] {
tmp1 += v * btmp[l]
tmp2 += atmp[l] * binner[l]
}
ctmp[jc] *= beta
ctmp[jc] += alpha * (tmp1 + tmp2)
}
}
}
return
}
for i := 0; i < n; i++ {
atmp := a[i*lda : i*lda+k]
btmp := b[i*ldb : i*ldb+k]
ctmp := c[i*ldc : i*ldc+i+1]
if beta == 0 {
for j := 0; j <= i; j++ {
var tmp1, tmp2 float32
binner := b[j*ldb : j*ldb+k]
for l, v := range a[j*lda : j*lda+k] {
tmp1 += v * btmp[l]
tmp2 += atmp[l] * binner[l]
}
ctmp[j] = alpha * (tmp1 + tmp2)
}
} else {
for j := 0; j <= i; j++ {
var tmp1, tmp2 float32
binner := b[j*ldb : j*ldb+k]
for l, v := range a[j*lda : j*lda+k] {
tmp1 += v * btmp[l]
tmp2 += atmp[l] * binner[l]
}
ctmp[j] *= beta
ctmp[j] += alpha * (tmp1 + tmp2)
}
}
}
return
}
if ul == blas.Upper {
for i := 0; i < n; i++ {
ctmp := c[i*ldc+i : i*ldc+n]
switch beta {
case 0:
for j := range ctmp {
ctmp[j] = 0
}
case 1:
default:
for j := range ctmp {
ctmp[j] *= beta
}
}
for l := 0; l < k; l++ {
tmp1 := alpha * b[l*ldb+i]
tmp2 := alpha * a[l*lda+i]
btmp := b[l*ldb+i : l*ldb+n]
if tmp1 != 0 || tmp2 != 0 {
for j, v := range a[l*lda+i : l*lda+n] {
ctmp[j] += v*tmp1 + btmp[j]*tmp2
}
}
}
}
return
}
for i := 0; i < n; i++ {
ctmp := c[i*ldc : i*ldc+i+1]
switch beta {
case 0:
for j := range ctmp {
ctmp[j] = 0
}
case 1:
default:
for j := range ctmp {
ctmp[j] *= beta
}
}
for l := 0; l < k; l++ {
tmp1 := alpha * b[l*ldb+i]
tmp2 := alpha * a[l*lda+i]
btmp := b[l*ldb : l*ldb+i+1]
if tmp1 != 0 || tmp2 != 0 {
for j, v := range a[l*lda : l*lda+i+1] {
ctmp[j] += v*tmp1 + btmp[j]*tmp2
}
}
}
}
}
// Strmm performs one of the matrix-matrix operations
// B = alpha * A * B if tA == blas.NoTrans and side == blas.Left
// B = alpha * Aᵀ * B if tA == blas.Trans or blas.ConjTrans, and side == blas.Left
// B = alpha * B * A if tA == blas.NoTrans and side == blas.Right
// B = alpha * B * Aᵀ if tA == blas.Trans or blas.ConjTrans, and side == blas.Right
// where A is an n×n or m×m triangular matrix, B is an m×n matrix, and alpha is a scalar.
//
// Float32 implementations are autogenerated and not directly tested.
func (Implementation) Strmm(s blas.Side, ul blas.Uplo, tA blas.Transpose, d blas.Diag, m, n int, alpha float32, a []float32, lda int, b []float32, ldb int) {
if s != blas.Left && s != blas.Right {
panic(badSide)
}
if ul != blas.Lower && ul != blas.Upper {
panic(badUplo)
}
if tA != blas.NoTrans && tA != blas.Trans && tA != blas.ConjTrans {
panic(badTranspose)
}
if d != blas.NonUnit && d != blas.Unit {
panic(badDiag)
}
if m < 0 {
panic(mLT0)
}
if n < 0 {
panic(nLT0)
}
k := n
if s == blas.Left {
k = m
}
if lda < max(1, k) {
panic(badLdA)
}
if ldb < max(1, n) {
panic(badLdB)
}
// Quick return if possible.
if m == 0 || n == 0 {
return
}
// For zero matrix size the following slice length checks are trivially satisfied.
if len(a) < lda*(k-1)+k {
panic(shortA)
}
if len(b) < ldb*(m-1)+n {
panic(shortB)
}
if alpha == 0 {
for i := 0; i < m; i++ {
btmp := b[i*ldb : i*ldb+n]
for j := range btmp {
btmp[j] = 0
}
}
return
}
nonUnit := d == blas.NonUnit
if s == blas.Left {
if tA == blas.NoTrans {
if ul == blas.Upper {
for i := 0; i < m; i++ {
tmp := alpha
if nonUnit {
tmp *= a[i*lda+i]
}
btmp := b[i*ldb : i*ldb+n]
f32.ScalUnitary(tmp, btmp)
for ka, va := range a[i*lda+i+1 : i*lda+m] {
k := ka + i + 1
if va != 0 {
f32.AxpyUnitary(alpha*va, b[k*ldb:k*ldb+n], btmp)
}
}
}
return
}
for i := m - 1; i >= 0; i-- {
tmp := alpha
if nonUnit {
tmp *= a[i*lda+i]
}
btmp := b[i*ldb : i*ldb+n]
f32.ScalUnitary(tmp, btmp)
for k, va := range a[i*lda : i*lda+i] {
if va != 0 {
f32.AxpyUnitary(alpha*va, b[k*ldb:k*ldb+n], btmp)
}
}
}
return
}
// Cases where a is transposed.
if ul == blas.Upper {
for k := m - 1; k >= 0; k-- {
btmpk := b[k*ldb : k*ldb+n]
for ia, va := range a[k*lda+k+1 : k*lda+m] {
i := ia + k + 1
btmp := b[i*ldb : i*ldb+n]
if va != 0 {
f32.AxpyUnitary(alpha*va, btmpk, btmp)
}
}
tmp := alpha
if nonUnit {
tmp *= a[k*lda+k]
}
if tmp != 1 {
f32.ScalUnitary(tmp, btmpk)
}
}
return
}
for k := 0; k < m; k++ {
btmpk := b[k*ldb : k*ldb+n]
for i, va := range a[k*lda : k*lda+k] {
btmp := b[i*ldb : i*ldb+n]
if va != 0 {
f32.AxpyUnitary(alpha*va, btmpk, btmp)
}
}
tmp := alpha
if nonUnit {
tmp *= a[k*lda+k]
}
if tmp != 1 {
f32.ScalUnitary(tmp, btmpk)
}
}
return
}
// Cases where a is on the right
if tA == blas.NoTrans {
if ul == blas.Upper {
for i := 0; i < m; i++ {
btmp := b[i*ldb : i*ldb+n]
for k := n - 1; k >= 0; k-- {
tmp := alpha * btmp[k]
if tmp == 0 {
continue
}
btmp[k] = tmp
if nonUnit {
btmp[k] *= a[k*lda+k]
}
f32.AxpyUnitary(tmp, a[k*lda+k+1:k*lda+n], btmp[k+1:n])
}
}
return
}
for i := 0; i < m; i++ {
btmp := b[i*ldb : i*ldb+n]
for k := 0; k < n; k++ {
tmp := alpha * btmp[k]
if tmp == 0 {
continue
}
btmp[k] = tmp
if nonUnit {
btmp[k] *= a[k*lda+k]
}
f32.AxpyUnitary(tmp, a[k*lda:k*lda+k], btmp[:k])
}
}
return
}
// Cases where a is transposed.
if ul == blas.Upper {
for i := 0; i < m; i++ {
btmp := b[i*ldb : i*ldb+n]
for j, vb := range btmp {
tmp := vb
if nonUnit {
tmp *= a[j*lda+j]
}
tmp += f32.DotUnitary(a[j*lda+j+1:j*lda+n], btmp[j+1:n])
btmp[j] = alpha * tmp
}
}
return
}
for i := 0; i < m; i++ {
btmp := b[i*ldb : i*ldb+n]
for j := n - 1; j >= 0; j-- {
tmp := btmp[j]
if nonUnit {
tmp *= a[j*lda+j]
}
tmp += f32.DotUnitary(a[j*lda:j*lda+j], btmp[:j])
btmp[j] = alpha * tmp
}
}
}