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mat: move SolveLU* onto LU

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kortschak
2017-06-30 14:41:42 +09:30
committed by Dan Kortschak
parent f9e7e06e3e
commit d87e74cea0
3 changed files with 11 additions and 11 deletions
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12
mat/lu.go
View File

@@ -265,16 +265,16 @@ func (m *Dense) Permutation(r int, swaps []int) {
} }
} }
// SolveLU solves a system of linear equations using the LU decomposition of a matrix. // Solve solves a system of linear equations using the LU decomposition of a matrix.
// It computes // It computes
// A * x = b if trans == false // A * x = b if trans == false
// A^T * x = b if trans == true // A^T * x = b if trans == true
// In both cases, A is represented in LU factorized form, and the matrix x is // In both cases, A is represented in LU factorized form, and the matrix x is
// stored into the receiver. // stored into m.
// //
// If A is singular or near-singular a Condition error is returned. Please see // If A is singular or near-singular a Condition error is returned. Please see
// the documentation for Condition for more information. // the documentation for Condition for more information.
func (m *Dense) SolveLU(lu *LU, trans bool, b Matrix) error { func (lu *LU) Solve(m *Dense, trans bool, b Matrix) error {
_, n := lu.lu.Dims() _, n := lu.lu.Dims()
br, bc := b.Dims() br, bc := b.Dims()
if br != n { if br != n {
@@ -308,16 +308,16 @@ func (m *Dense) SolveLU(lu *LU, trans bool, b Matrix) error {
return nil return nil
} }
// SolveLUVec solves a system of linear equations using the LU decomposition of a matrix. // SolveVec solves a system of linear equations using the LU decomposition of a matrix.
// It computes // It computes
// A * x = b if trans == false // A * x = b if trans == false
// A^T * x = b if trans == true // A^T * x = b if trans == true
// In both cases, A is represented in LU factorized form, and the matrix x is // In both cases, A is represented in LU factorized form, and the matrix x is
// stored into the receiver. // stored into v.
// //
// If A is singular or near-singular a Condition error is returned. Please see // If A is singular or near-singular a Condition error is returned. Please see
// the documentation for Condition for more information. // the documentation for Condition for more information.
func (v *Vector) SolveLUVec(lu *LU, trans bool, b *Vector) error { func (lu *LU) SolveVec(v *Vector, trans bool, b *Vector) error {
_, n := lu.lu.Dims() _, n := lu.lu.Dims()
bn := b.Len() bn := b.Len()
if bn != n { if bn != n {

8
mat/lu_test.go
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@@ -128,7 +128,7 @@ func TestSolveLU(t *testing.T) {
var lu LU var lu LU
lu.Factorize(a) lu.Factorize(a)
var x Dense var x Dense
if err := x.SolveLU(&lu, false, b); err != nil { if err := lu.Solve(&x, false, b); err != nil {
continue continue
} }
var got Dense var got Dense
@@ -149,13 +149,13 @@ func TestSolveLUCond(t *testing.T) {
lu.Factorize(test) lu.Factorize(test)
b := NewDense(m, 2, nil) b := NewDense(m, 2, nil)
var x Dense var x Dense
if err := x.SolveLU(&lu, false, b); err == nil { if err := lu.Solve(&x, false, b); err == nil {
t.Error("No error for near-singular matrix in matrix solve.") t.Error("No error for near-singular matrix in matrix solve.")
} }
bvec := NewVector(m, nil) bvec := NewVector(m, nil)
var xvec Vector var xvec Vector
if err := xvec.SolveLUVec(&lu, false, bvec); err == nil { if err := lu.SolveVec(&xvec, false, bvec); err == nil {
t.Error("No error for near-singular matrix in matrix solve.") t.Error("No error for near-singular matrix in matrix solve.")
} }
} }
@@ -176,7 +176,7 @@ func TestSolveLUVec(t *testing.T) {
var lu LU var lu LU
lu.Factorize(a) lu.Factorize(a)
var x Vector var x Vector
if err := x.SolveLUVec(&lu, false, b); err != nil { if err := lu.SolveVec(&x, false, b); err != nil {
continue continue
} }
var got Vector var got Vector

2
mat/solve.go
View File

@@ -91,7 +91,7 @@ func (m *Dense) Solve(a, b Matrix) error {
} }
var lu LU var lu LU
lu.Factorize(a) lu.Factorize(a)
return m.SolveLU(&lu, false, b) return lu.Solve(m, false, b)
case ar > ac: case ar > ac:
var qr QR var qr QR
qr.Factorize(a) qr.Factorize(a)