func newSimilarityCircuit(uVal, uGrad, vVal, vGrad []float64) *similarityCircuit {
s := similarityCircuit{
UVal: uVal,
UGrad: uGrad,
VVal: vVal,
VGrad: vGrad,
}
u := blas64.Vector{Inc: 1, Data: uVal}
v := blas64.Vector{Inc: 1, Data: vVal}
s.UV = blas64.Dot(len(uVal), u, v)
s.Unorm = blas64.Nrm2(len(uVal), u)
s.Vnorm = blas64.Nrm2(len(vVal), v)
s.TopVal = s.UV / (s.Unorm * s.Vnorm)
return &s
}
// LQ computes an LQ Decomposition for an m-by-n matrix a with m <= n by Householder
// reflections. The LQ decomposition is an m-by-n orthogonal matrix q and an m-by-m
// lower triangular matrix l so that a = l.q. LQ will panic with ErrShape if m > n.
//
// The LQ decomposition always exists, even if the matrix does not have full rank,
// so LQ will never fail unless m > n. The primary use of the LQ decomposition is
// in the least squares solution of non-square systems of simultaneous linear equations.
// This will fail if LQIsFullRank() returns false. The matrix a is overwritten by the
// decomposition.
func LQ(a *Dense) LQFactor {
// Initialize.
m, n := a.Dims()
if m > n {
panic(ErrShape)
}
lq := *a
lDiag := make([]float64, m)
projs := NewVector(m, nil)
// Main loop.
for k := 0; k < m; k++ {
hh := lq.RawRowView(k)[k:]
norm := blas64.Nrm2(len(hh), blas64.Vector{Inc: 1, Data: hh})
lDiag[k] = norm
if norm != 0 {
hhNorm := (norm * math.Sqrt(1-hh[0]/norm))
if hhNorm == 0 {
hh[0] = 0
} else {
// Form k-th Householder vector.
s := 1 / hhNorm
hh[0] -= norm
blas64.Scal(len(hh), s, blas64.Vector{Inc: 1, Data: hh})
// Apply transformation to remaining columns.
if k < m-1 {
a = lq.View(k+1, k, m-k-1, n-k).(*Dense)
projs = projs.ViewVec(0, m-k-1)
projs.MulVec(a, false, NewVector(len(hh), hh))
for j := 0; j < m-k-1; j++ {
dst := a.RawRowView(j)
blas64.Axpy(len(dst), -projs.at(j),
blas64.Vector{Inc: 1, Data: hh},
blas64.Vector{Inc: 1, Data: dst},
)
}
}
}
}
}
*a = lq
return LQFactor{a, lDiag}
}
// 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
}
}
func Dgelq2Test(t *testing.T, impl Dgelq2er) {
for c, test := range []struct {
m, n, lda int
}{
{1, 1, 0},
{2, 2, 0},
{3, 2, 0},
{2, 3, 0},
{1, 12, 0},
{2, 6, 0},
{3, 4, 0},
{4, 3, 0},
{6, 2, 0},
{1, 12, 0},
{1, 1, 20},
{2, 2, 20},
{3, 2, 20},
{2, 3, 20},
{1, 12, 20},
{2, 6, 20},
{3, 4, 20},
{4, 3, 20},
{6, 2, 20},
{1, 12, 20},
} {
n := test.n
m := test.m
lda := test.lda
if lda == 0 {
lda = test.n
}
k := min(m, n)
tau := make([]float64, k)
for i := range tau {
tau[i] = rand.Float64()
}
work := make([]float64, m)
for i := range work {
work[i] = rand.Float64()
}
a := make([]float64, m*lda)
for i := 0; i < m*lda; i++ {
a[i] = rand.Float64()
}
aCopy := make([]float64, len(a))
copy(aCopy, a)
impl.Dgelq2(m, n, a, lda, tau, work)
Q := constructQ("LQ", m, n, a, lda, tau)
// Check that Q is orthonormal
for i := 0; i < Q.Rows; i++ {
nrm := blas64.Nrm2(Q.Cols, blas64.Vector{Inc: 1, Data: Q.Data[i*Q.Stride:]})
if math.Abs(nrm-1) > 1e-14 {
t.Errorf("Q not normal. Norm is %v", nrm)
}
for j := 0; j < i; j++ {
dot := blas64.Dot(Q.Rows,
blas64.Vector{Inc: 1, Data: Q.Data[i*Q.Stride:]},
blas64.Vector{Inc: 1, Data: Q.Data[j*Q.Stride:]},
)
if math.Abs(dot) > 1e-14 {
t.Errorf("Q not orthogonal. Dot is %v", dot)
}
}
}
L := blas64.General{
Rows: m,
Cols: n,
Stride: n,
Data: make([]float64, m*n),
}
for i := 0; i < m; i++ {
for j := 0; j <= min(i, n-1); j++ {
L.Data[i*L.Stride+j] = a[i*lda+j]
}
}
ans := blas64.General{
Rows: m,
Cols: n,
Stride: lda,
Data: make([]float64, m*lda),
}
copy(ans.Data, aCopy)
blas64.Gemm(blas.NoTrans, blas.NoTrans, 1, L, Q, 0, ans)
if !floats.EqualApprox(aCopy, ans.Data, 1e-14) {
t.Errorf("Case %v, LQ mismatch. Want %v, got %v.", c, aCopy, ans.Data)
}
}
}
func Dgeqr2Test(t *testing.T, impl Dgeqr2er) {
for c, test := range []struct {
m, n, lda int
}{
{1, 1, 0},
{2, 2, 0},
{3, 2, 0},
{2, 3, 0},
{1, 12, 0},
{2, 6, 0},
{3, 4, 0},
{4, 3, 0},
{6, 2, 0},
{12, 1, 0},
{1, 1, 20},
{2, 2, 20},
{3, 2, 20},
{2, 3, 20},
{1, 12, 20},
{2, 6, 20},
{3, 4, 20},
{4, 3, 20},
{6, 2, 20},
{12, 1, 20},
} {
n := test.n
m := test.m
lda := test.lda
if lda == 0 {
lda = test.n
}
a := make([]float64, m*lda)
for i := range a {
a[i] = rand.Float64()
}
aCopy := make([]float64, len(a))
k := min(m, n)
tau := make([]float64, k)
for i := range tau {
tau[i] = rand.Float64()
}
work := make([]float64, n)
for i := range work {
work[i] = rand.Float64()
}
copy(aCopy, a)
impl.Dgeqr2(m, n, a, lda, tau, work)
// Test that the QR factorization has completed successfully. Compute
// Q based on the vectors.
q := constructQ("QR", m, n, a, lda, tau)
// Check that q is orthonormal
for i := 0; i < m; i++ {
nrm := blas64.Nrm2(m, blas64.Vector{1, q.Data[i*m:]})
if math.Abs(nrm-1) > 1e-14 {
t.Errorf("Case %v, q not normal", c)
}
for j := 0; j < i; j++ {
dot := blas64.Dot(m, blas64.Vector{1, q.Data[i*m:]}, blas64.Vector{1, q.Data[j*m:]})
if math.Abs(dot) > 1e-14 {
t.Errorf("Case %v, q not orthogonal", i)
}
}
}
// Check that A = Q * R
r := blas64.General{
Rows: m,
Cols: n,
Stride: n,
Data: make([]float64, m*n),
}
for i := 0; i < m; i++ {
for j := i; j < n; j++ {
r.Data[i*n+j] = a[i*lda+j]
}
}
atmp := blas64.General{
Rows: m,
Cols: n,
Stride: lda,
Data: make([]float64, m*lda),
}
copy(atmp.Data, a)
blas64.Gemm(blas.NoTrans, blas.NoTrans, 1, q, r, 0, atmp)
if !floats.EqualApprox(atmp.Data, aCopy, 1e-14) {
t.Errorf("Q*R != a")
}
}
}
func DlangeTest(t *testing.T, impl Dlanger) {
for _, test := range []struct {
m, n, lda int
}{
{4, 3, 0},
{3, 4, 0},
{4, 3, 100},
{3, 4, 100},
} {
m := test.m
n := test.n
lda := test.lda
if lda == 0 {
lda = n
}
a := make([]float64, m*lda)
for i := range a {
a[i] = (rand.Float64() - 0.5)
}
work := make([]float64, n)
for i := range work {
work[i] = rand.Float64()
}
aCopy := make([]float64, len(a))
copy(aCopy, a)
// Test MaxAbs norm.
norm := impl.Dlange(lapack.MaxAbs, m, n, a, lda, work)
var ans float64
for i := 0; i < m; i++ {
idx := blas64.Iamax(n, blas64.Vector{1, aCopy[i*lda:]})
ans = math.Max(ans, math.Abs(a[i*lda+idx]))
}
// Should be strictly equal because there is no floating point summation error.
if ans != norm {
t.Errorf("MaxAbs mismatch. Want %v, got %v.", ans, norm)
}
// Test MaxColumnSum norm.
norm = impl.Dlange(lapack.MaxColumnSum, m, n, a, lda, work)
ans = 0
for i := 0; i < n; i++ {
sum := blas64.Asum(m, blas64.Vector{lda, aCopy[i:]})
ans = math.Max(ans, sum)
}
if math.Abs(norm-ans) > 1e-14 {
t.Errorf("MaxColumnSum mismatch. Want %v, got %v.", ans, norm)
}
// Test MaxRowSum norm.
norm = impl.Dlange(lapack.MaxRowSum, m, n, a, lda, work)
ans = 0
for i := 0; i < m; i++ {
sum := blas64.Asum(n, blas64.Vector{1, aCopy[i*lda:]})
ans = math.Max(ans, sum)
}
if math.Abs(norm-ans) > 1e-14 {
t.Errorf("MaxRowSum mismatch. Want %v, got %v.", ans, norm)
}
// Test Frobenius norm
norm = impl.Dlange(lapack.NormFrob, m, n, a, lda, work)
ans = 0
for i := 0; i < m; i++ {
sum := blas64.Nrm2(n, blas64.Vector{1, aCopy[i*lda:]})
ans += sum * sum
}
ans = math.Sqrt(ans)
if math.Abs(norm-ans) > 1e-14 {
t.Errorf("NormFrob mismatch. Want %v, got %v.", ans, norm)
}
}
}
// SymRankOne performs a rank-1 update of the original matrix A and refactorizes
// its Cholesky factorization, storing the result into the reciever. That is, if
// in the original Cholesky factorization
// U^T * U = A,
// in the updated factorization
// U'^T * U' = A + alpha * x * x^T = A'.
//
// Note that when alpha is negative, the updating problem may be ill-conditioned
// and the results may be inaccurate, or the updated matrix A' may not be
// positive definite and not have a Cholesky factorization. SymRankOne returns
// whether the updated matrix A' is positive definite.
//
// SymRankOne updates a Cholesky factorization in O(n²) time. The Cholesky
// factorization computation from scratch is O(n³).
func (c *Cholesky) SymRankOne(orig *Cholesky, alpha float64, x *Vector) (ok bool) {
if !orig.valid() {
panic(badCholesky)
}
n := orig.Size()
if x.Len() != n {
panic(matrix.ErrShape)
}
if orig != c {
if c.isZero() {
c.chol = NewTriDense(n, matrix.Upper, nil)
} else if c.chol.mat.N != n {
panic(matrix.ErrShape)
}
c.chol.Copy(orig.chol)
}
if alpha == 0 {
return true
}
// Algorithms for updating and downdating the Cholesky factorization are
// described, for example, in
// - J. J. Dongarra, J. R. Bunch, C. B. Moler, G. W. Stewart: LINPACK
// Users' Guide. SIAM (1979), pages 10.10--10.14
// or
// - P. E. Gill, G. H. Golub, W. Murray, and M. A. Saunders: Methods for
// modifying matrix factorizations. Mathematics of Computation 28(126)
// (1974), Method C3 on page 521
//
// The implementation is based on LINPACK code
// http://www.netlib.org/linpack/dchud.f
// http://www.netlib.org/linpack/dchdd.f
// and
// https://icl.cs.utk.edu/lapack-forum/viewtopic.php?f=2&t=2646
//
// According to http://icl.cs.utk.edu/lapack-forum/archives/lapack/msg00301.html
// LINPACK is released under BSD license.
//
// See also:
// - M. A. Saunders: Large-scale Linear Programming Using the Cholesky
// Factorization. Technical Report Stanford University (1972)
// http://i.stanford.edu/pub/cstr/reports/cs/tr/72/252/CS-TR-72-252.pdf
// - Matthias Seeger: Low rank updates for the Cholesky decomposition.
// EPFL Technical Report 161468 (2004)
// http://infoscience.epfl.ch/record/161468
work := make([]float64, n)
blas64.Copy(n, x.RawVector(), blas64.Vector{1, work})
if alpha > 0 {
// Compute rank-1 update.
if alpha != 1 {
blas64.Scal(n, math.Sqrt(alpha), blas64.Vector{1, work})
}
umat := c.chol.mat
stride := umat.Stride
for i := 0; i < n; i++ {
// Compute parameters of the Givens matrix that zeroes
// the i-th element of x.
c, s, r, _ := blas64.Rotg(umat.Data[i*stride+i], work[i])
if r < 0 {
// Multiply by -1 to have positive diagonal
// elemnts.
r *= -1
c *= -1
s *= -1
}
umat.Data[i*stride+i] = r
if i < n-1 {
// Multiply the extended factorization matrix by
// the Givens matrix from the left. Only
// the i-th row and x are modified.
blas64.Rot(n-i-1,
blas64.Vector{1, umat.Data[i*stride+i+1 : i*stride+n]},
blas64.Vector{1, work[i+1 : n]},
c, s)
}
}
c.updateCond(-1)
return true
}
// Compute rank-1 downdate.
alpha = math.Sqrt(-alpha)
if alpha != 1 {
blas64.Scal(n, alpha, blas64.Vector{1, work})
}
// Solve U^T * p = x storing the result into work.
ok = lapack64.Trtrs(blas.Trans, c.chol.RawTriangular(), blas64.General{
Rows: n,
Cols: 1,
Stride: 1,
Data: work,
})
if !ok {
// The original matrix is singular. Should not happen, because
// the factorization is valid.
panic(badCholesky)
}
norm := blas64.Nrm2(n, blas64.Vector{1, work})
if norm >= 1 {
// The updated matrix is not positive definite.
return false
}
norm = math.Sqrt((1 + norm) * (1 - norm))
cos := make([]float64, n)
sin := make([]float64, n)
for i := n - 1; i >= 0; i-- {
// Compute parameters of Givens matrices that zero elements of p
// backwards.
cos[i], sin[i], norm, _ = blas64.Rotg(norm, work[i])
if norm < 0 {
norm *= -1
cos[i] *= -1
sin[i] *= -1
}
}
umat := c.chol.mat
stride := umat.Stride
for i := n - 1; i >= 0; i-- {
// Apply Givens matrices to U.
// TODO(vladimir-ch): Use workspace to avoid modifying the
// receiver in case an invalid factorization is created.
blas64.Rot(n-i, blas64.Vector{1, work[i:n]}, blas64.Vector{1, umat.Data[i*stride+i : i*stride+n]}, cos[i], sin[i])
if umat.Data[i*stride+i] == 0 {
// The matrix is singular (may rarely happen due to
// floating-point effects?).
ok = false
} else if umat.Data[i*stride+i] < 0 {
// Diagonal elements should be positive. If it happens
// that on the i-th row the diagonal is negative,
// multiply U from the left by an identity matrix that
// has -1 on the i-th row.
blas64.Scal(n-i, -1, blas64.Vector{1, umat.Data[i*stride+i : i*stride+n]})
}
}
if ok {
c.updateCond(-1)
} else {
c.Reset()
}
return ok
}
