// not really needed.
func createLdlSolver(G *matrix.FloatMatrix, dims *DimensionSet, A *matrix.FloatMatrix, mnl int) *kktLdlSolver {
kkt := new(kktLdlSolver)
kkt.p, kkt.n = A.Size()
kkt.ldK = kkt.n + kkt.p + mnl + dims.Sum("l", "q") + dims.SumPacked("s")
kkt.K = matrix.FloatZeros(kkt.ldK, kkt.ldK)
kkt.ipiv = make([]int32, kkt.ldK)
kkt.u = matrix.FloatZeros(kkt.ldK, 1)
kkt.g = matrix.FloatZeros(kkt.mnl+G.Rows(), 1)
kkt.G = G
kkt.A = A
kkt.dims = dims
kkt.mnl = mnl
return kkt
}
// Solution of KKT equations by a dense LDL factorization of the
// 3 x 3 system.
//
// Returns a function that (1) computes the LDL factorization of
//
// [ H A' GG'*W^{-1} ]
// [ A 0 0 ],
// [ W^{-T}*GG 0 -I ]
//
// given H, Df, W, where GG = [Df; G], and (2) returns a function for
// solving
//
// [ H A' GG' ] [ ux ] [ bx ]
// [ A 0 0 ] * [ uy ] = [ by ].
// [ GG 0 -W'*W ] [ uz ] [ bz ]
//
// H is n x n, A is p x n, Df is mnl x n, G is N x n where
// N = dims['l'] + sum(dims['q']) + sum( k**2 for k in dims['s'] ).
//
func kktLdl(G *matrix.FloatMatrix, dims *DimensionSet, A *matrix.FloatMatrix, mnl int) (kktFactor, error) {
p, n := A.Size()
ldK := n + p + mnl + dims.At("l")[0] + dims.Sum("q") + dims.SumPacked("s")
K := matrix.FloatZeros(ldK, ldK)
ipiv := make([]int32, ldK)
u := matrix.FloatZeros(ldK, 1)
g := matrix.FloatZeros(mnl+G.Rows(), 1)
factor := func(W *FloatMatrixSet, H, Df *matrix.FloatMatrix) (kktFunc, error) {
var err error = nil
// Zero K for each call.
blas.ScalFloat(K, 0.0)
if H != nil {
K.SetSubMatrix(0, 0, H)
}
K.SetSubMatrix(n, 0, A)
//fmt.Printf("G=\n%v\n", G)
for k := 0; k < n; k++ {
// g is (mnl + G.Rows(), 1) matrix, Df is (mnl, n), G is (N, n)
if mnl > 0 {
// set values g[0:mnl] = Df[,k]
g.SetIndexes(matrix.MakeIndexSet(0, mnl, 1), Df.GetColumnArray(k, nil))
}
// set values g[mnl:] = G[,k]
g.SetIndexes(matrix.MakeIndexSet(mnl, mnl+g.Rows(), 1), G.GetColumnArray(k, nil))
scale(g, W, true, true)
if err != nil {
fmt.Printf("scale error: %s\n", err)
}
pack(g, K, dims, &la_.IOpt{"mnl", mnl}, &la_.IOpt{"offsety", k*ldK + n + p})
}
setDiagonal(K, n+p, n+n, ldK, ldK, -1.0)
//fmt.Printf("K=\n%v\n", K)
err = lapack.Sytrf(K, ipiv)
//fmt.Printf("sytrf: K=\n%v\n", K)
if err != nil {
return nil, err
}
solve := func(x, y, z *matrix.FloatMatrix) (err error) {
// Solve
//
// [ H A' GG'*W^{-1} ] [ ux ] [ bx ]
// [ A 0 0 ] * [ uy [ = [ by ]
// [ W^{-T}*GG 0 -I ] [ W*uz ] [ W^{-T}*bz ]
//
// and return ux, uy, W*uz.
//
// On entry, x, y, z contain bx, by, bz. On exit, they contain
// the solution ux, uy, W*uz.
//fmt.Printf("** start solve **\n")
//fmt.Printf("x=\n%v\n", x.ConvertToString())
//fmt.Printf("z=\n%v\n", z.ConvertToString())
err = nil
blas.Copy(x, u)
blas.Copy(y, u, &la_.IOpt{"offsety", n})
//fmt.Printf("solving: u=\n%v\n", u.ConvertToString())
//W.Print()
err = scale(z, W, true, true)
//fmt.Printf("solving: post-scale z=\n%v\n", z.ConvertToString())
if err != nil {
return
}
err = pack(z, u, dims, &la_.IOpt{"mnl", mnl}, &la_.IOpt{"offsety", n + p})
//fmt.Printf("solve: post-Pack {mnl=%d, n=%d, p=%d} u=\n%v\n",
// mnl, n, p, u.ConvertToString())
if err != nil {
return
}
err = lapack.Sytrs(K, u, ipiv)
if err != nil {
return
}
blas.Copy(u, x, &la_.IOpt{"n", n})
blas.Copy(u, y, &la_.IOpt{"n", p}, &la_.IOpt{"offsetx", n})
err = unpack(u, z, dims, &la_.IOpt{"mnl", mnl}, &la_.IOpt{"offsetx", n + p})
//fmt.Printf("** end solve **\n")
//fmt.Printf("x=\n%v\n", x.ConvertToString())
//fmt.Printf("z=\n%v\n", z.ConvertToString())
return
}
return solve, err
}
return factor, nil
}
// Computes analytic center of A*x <= b with A m by n of rank n.
// We assume that b > 0 and the feasible set is bounded.
func Acent(A, b *matrix.FloatMatrix, niters int) (*matrix.FloatMatrix, []float64) {
if niters <= 0 {
niters = MAXITERS
}
ntdecrs := make([]float64, 0, niters)
if A.Rows() != b.Rows() {
return nil, nil
}
m, n := A.Size()
x := matrix.FloatZeros(n, 1)
H := matrix.FloatZeros(n, n)
// Helper m*n matrix
Dmn := matrix.FloatZeros(m, n)
for i := 0; i < niters; i++ {
// Gradient is g = A^T * (1.0/(b - A*x)). d = 1.0/(b - A*x)
// d is m*1 matrix, g is n*1 matrix
d := b.Minus(A.Times(x))
d.Apply(d, func(a float64) float64 { return 1.0 / a })
g := A.Transpose().Times(d)
// Hessian is H = A^T * diag(1./(b-A*x))^2 * A.
// in the original python code expression d[:,n*[0]] creates
// a m*n matrix where each column is copy of column 0.
// We do it here manually.
for i := 0; i < n; i++ {
Dmn.SetColumnMatrix(i, d)
}
// Function mul creates element wise product of matrices.
Asc := Dmn.Mul(A)
blas.SyrkFloat(Asc, H, 1.0, 0.0, linalg.OptTrans)
// Newton step is v = H^-1 * g.
v := g.Copy().Neg()
lapack.PosvFloat(H, v)
// Directional derivative and Newton decrement.
lam := blas.DotFloat(g, v)
ntdecrs = append(ntdecrs, math.Sqrt(-lam))
if ntdecrs[len(ntdecrs)-1] < TOL {
fmt.Printf("last Newton decrement < TOL(%v)\n", TOL)
return x, ntdecrs
}
// Backtracking line search.
// y = d .* A*v
y := d.Mul(A.Times(v))
step := 1.0
for 1-step*y.Max() < 0 {
step *= BETA
}
search:
for {
// t = -step*y
t := y.Copy().Scale(-step)
// t = (1 + t) [e.g. t = 1 - step*y]
t.Add(1.0)
// ts = sum(log(1-step*y))
ts := t.Log().Sum()
if -ts < ALPHA*step*lam {
break search
}
step *= BETA
}
v.Scale(step)
x = x.Plus(v)
}
// no solution !!
fmt.Printf("Iteration %d exhausted\n", niters)
return x, ntdecrs
}
