// Returns min {t | x + t*e >= 0}, where e is defined as follows
//
// - For the nonlinear and 'l' blocks: e is the vector of ones.
// - For the 'q' blocks: e is the first unit vector.
// - For the 's' blocks: e is the identity matrix.
//
// When called with the argument sigma, also returns the eigenvalues
// (in sigma) and the eigenvectors (in x) of the 's' components of x.
func maxStep(x *matrix.FloatMatrix, dims *DimensionSet, mnl int, sigma *matrix.FloatMatrix) (rval float64, err error) {
/*DEBUGGED*/
rval = 0.0
err = nil
t := make([]float64, 0, 10)
ind := mnl + dims.Sum("l")
if ind > 0 {
t = append(t, -minvec(x.FloatArray()[:ind]))
}
for _, m := range dims.At("q") {
if m > 0 {
v := blas.Nrm2Float(x, &la_.IOpt{"offset", ind + 1}, &la_.IOpt{"n", m - 1})
v -= x.GetIndex(ind)
t = append(t, v)
}
ind += m
}
var Q *matrix.FloatMatrix
var w *matrix.FloatMatrix
ind2 := 0
if sigma == nil && len(dims.At("s")) > 0 {
mx := dims.Max("s")
Q = matrix.FloatZeros(mx, mx)
w = matrix.FloatZeros(mx, 1)
}
for _, m := range dims.At("s") {
if sigma == nil {
blas.Copy(x, Q, &la_.IOpt{"offsetx", ind}, &la_.IOpt{"n", m * m})
err = lapack.SyevrFloat(Q, w, nil, 0.0, nil, []int{1, 1}, la_.OptRangeInt,
&la_.IOpt{"n", m}, &la_.IOpt{"lda", m})
if m > 0 {
t = append(t, -w.GetIndex(0))
}
} else {
err = lapack.SyevdFloat(x, sigma, la_.OptJobZValue, &la_.IOpt{"n", m},
&la_.IOpt{"lda", m}, &la_.IOpt{"offseta", ind}, &la_.IOpt{"offsetw", ind2})
if m > 0 {
t = append(t, -sigma.GetIndex(ind2))
}
}
ind += m * m
ind2 += m
}
if len(t) > 0 {
rval = maxvec(t)
}
return
}
// 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
}
func doScale(G *matrix.FloatMatrix, W *cvx.FloatMatrixSet) {
g := matrix.FloatZeros(G.Rows(), 1)
g.SetIndexes(matrix.MakeIndexSet(0, g.Rows(), 1), G.GetColumn(0, nil))
fmt.Printf("** scaling g:\n%v\n", g)
cvx.Scale(g, W, true, true)
fmt.Printf("== scaled g:\n%v\n", g)
}
func main() {
// matrix string in row order presentation
sA :=
"[-7.44e-01 1.11e-01 1.29e+00 2.62e+00 -1.82e+00]" +
"[ 4.59e-01 7.06e-01 3.16e-01 -1.06e-01 7.80e-01]" +
"[-2.95e-02 -2.22e-01 -2.07e-01 -9.11e-01 -3.92e-01]" +
"[-7.75e-01 1.03e-01 -1.22e+00 -5.74e-01 -3.32e-01]" +
"[-1.80e+00 1.24e+00 -2.61e+00 -9.31e-01 -6.38e-01]"
sb :=
"[ 8.38e-01]" +
"[ 9.92e-01]" +
"[ 9.56e-01]" +
"[ 6.14e-01]" +
"[ 6.56e-01]" +
"[ 3.57e-01]" +
"[ 6.36e-01]" +
"[ 5.08e-01]" +
"[ 8.81e-03]" +
"[ 7.08e-02]"
b, _ := matrix.FloatParsePy(sb)
Al, _ := matrix.FloatParsePy(sA)
Au := Al.Copy().Neg()
A := matrix.FloatZeros(2*Al.Rows(), Al.Cols())
A.SetSubMatrix(0, 0, Al)
A.SetSubMatrix(Al.Rows(), 0, Au)
//fmt.Printf("A (%d,%d):\n%v\n", A.Rows(), A.Cols(), A)
//fmt.Printf("b (%d,%d):\n%v\n", b.Rows(), b.Cols(), b)
x, nt := Acent(A, b, 10)
fmt.Printf("nt:\n%v\n", nt)
fmt.Printf("x :\n%v\n", x)
}
func (p *FloorPlan) F0() (mnl int, x0 *matrix.FloatMatrix, err error) {
err = nil
mnl = 5
x0 = matrix.FloatZeros(22, 1)
// set last 5 elements to 1.0
x0.Set(1.0, -1, -2, -3, -4, -5)
return
}
// Solves a quadratic program
//
// minimize (1/2)*x'*P*x + q'*x
// subject to G*x <= h
// A*x = b.
//
//
// Input arguments.
//
// P is a n x n float matrix with the lower triangular part of P stored
// in the lower triangle. Must be positive semidefinite.
//
// q is an n x 1 matrix.
//
// G is an m x n matrix or nil.
//
// h is an m x 1 matrix or nil.
//
// A is a p x n matrix or nil.
//
// b is a p x 1 matrix or nil.
//
// The default values for G, h, A and b are empty matrices with zero rows.
//
//
func Qp(P, q, G, h, A, b *matrix.FloatMatrix, solopts *SolverOptions, initvals *FloatMatrixSet) (sol *Solution, err error) {
sol = nil
if P == nil || P.Rows() != P.Cols() {
err = errors.New("'P' must a non-nil square matrix")
return
}
if q == nil {
err = errors.New("'q' must a non-nil matrix")
return
}
if q.Rows() != P.Rows() || q.Cols() > 1 {
err = errors.New(fmt.Sprintf("'q' must be matrix of size (%d,1)", P.Rows()))
return
}
if G == nil {
G = matrix.FloatZeros(0, P.Rows())
}
if G.Cols() != P.Rows() {
err = errors.New(fmt.Sprintf("'G' must be matrix of %d columns", P.Rows()))
return
}
if h == nil {
h = matrix.FloatZeros(G.Rows(), 1)
}
if h.Rows() != G.Rows() || h.Cols() > 1 {
err = errors.New(fmt.Sprintf("'h' must be matrix of size (%d,1)", G.Rows()))
return
}
if A == nil {
A = matrix.FloatZeros(0, P.Rows())
}
if A.Cols() != P.Rows() {
err = errors.New(fmt.Sprintf("'A' must be matrix of %d columns", P.Rows()))
return
}
if b == nil {
b = matrix.FloatZeros(A.Rows(), 1)
}
if b.Rows() != A.Rows() {
err = errors.New(fmt.Sprintf("'b' must be matrix of size (%d,1)", A.Rows()))
return
}
return ConeQp(P, q, G, h, A, b, nil, solopts, initvals)
}
func TestPack() {
s := "{19 1 [0.34685034769697742 0.39585039411618006 -17.26426975698158017 13.79271773799920275 -13.13205395184397339 0.84649931876339113 1.62479401776324162 0.03868823612413173 0.05311904070719403 -0.13134270109326304 10.23788144936299993 -0.21362220612188865 0.27062567408202032 -58.02205901917197650 6.16596220003297013 -0.23193020346970489 -41.37875150278244973 45.61335347561063713 0.40295716371409468]}"
x, _ := matrix.FloatParseSpe(s)
y := matrix.FloatZeros(x.NumElements(), 1)
dims := makeDims()
fmt.Printf("x=\n%v\n", x.ConvertToString())
cvx.Pack(x, y, dims, &linalg.IOpt{"mnl", 0}, &linalg.IOpt{"offsety", 3})
fmt.Printf("y=\n%v\n", y.ConvertToString())
}
func (p *FloorPlan) F2(x, z *matrix.FloatMatrix) (f, Df, H *matrix.FloatMatrix, err error) {
f, Df, err = p.F1(x)
x17 := matrix.FloatVector(x.FloatArray()[17:])
tmp := p.Amin.Div(x17.Pow(3.0))
tmp = z.Mul(tmp).Scale(2.0)
diag := matrix.FloatDiagonal(5, tmp.FloatArray()...)
H = matrix.FloatZeros(22, 22)
H.SetSubMatrix(17, 17, diag)
return
}
func (p *FloorPlan) F1(x *matrix.FloatMatrix) (f, Df *matrix.FloatMatrix, err error) {
err = nil
mn := x.Min(-1, -2, -3, -4, -5)
if mn <= 0.0 {
f, Df = nil, nil
return
}
zeros := matrix.FloatZeros(5, 12)
dk1 := matrix.FloatDiagonal(5, -1.0)
dk2 := matrix.FloatZeros(5, 5)
x17 := matrix.FloatVector(x.FloatArray()[17:])
// -( Amin ./ (x17 .* x17) )
diag := p.Amin.Div(x17.Mul(x17)).Scale(-1.0)
dk2.SetIndexes(matrix.MakeDiagonalSet(5, 5), diag.FloatArray())
Df, _ = matrix.FloatMatrixCombined(matrix.StackRight, zeros, dk1, dk2)
x12 := matrix.FloatVector(x.FloatArray()[12:17])
// f = -x[12:17] + div(Amin, x[17:])
f = p.Amin.Div(x17).Minus(x12)
return
}
func TestComputeScaling() {
dims := makeDims()
sS :=
"[ 3.57e+01]" +
"[ 3.61e+01]" +
"[ 2.29e+01]" +
"[ 1.58e+01]" +
"[-1.50e+01]" +
"[ 1.22e+00]" +
"[ 4.39e+01]" +
"[ 2.99e-01]" +
"[ 3.71e-01]" +
"[-8.84e-01]" +
"[ 1.07e+02]" +
"[-2.80e+01]" +
"[-2.24e+01]" +
"[ 3.00e+01]" +
"[ 1.22e+02]" +
"[ 2.26e+01]" +
"[ 1.90e+01]" +
"[-2.30e+01]" +
"[ 4.57e+01]"
zS :=
"[ 1.28e+00]" +
"[ 1.21e+00]" +
"[ 1.23e+00]" +
"[ 1.00e-02]" +
"[ 7.99e-03]" +
"[ 9.00e-02]" +
"[ 1.16e+00]" +
"[-6.30e-03]" +
"[-4.97e-04]" +
"[-5.57e-03]" +
"[ 1.26e+00]" +
"[ 8.42e-03]" +
"[-3.01e-02]" +
"[ 0.00e+00]" +
"[ 1.13e+00]" +
"[-8.81e-02]" +
"[ 0.00e+00]" +
"[ 0.00e+00]" +
"[ 1.06e+00]"
s, _ := matrix.FloatParsePy(sS)
z, _ := matrix.FloatParsePy(zS)
lmbda := matrix.FloatZeros(14, 1)
W, err := cvx.ComputeScaling(s, z, lmbda, dims, 0)
fmt.Printf("lmbda=\n%v\n", lmbda)
W.Print()
if W == nil || err == nil {
}
}
// Solves a pair of primal and dual LPs
//
// minimize c'*x
// subject to G*x + s = h
// A*x = b
// s >= 0
//
// maximize -h'*z - b'*y
// subject to G'*z + A'*y + c = 0
// z >= 0.
//
func Lp(c, G, h, A, b *matrix.FloatMatrix, solopts *SolverOptions, primalstart, dualstart *FloatMatrixSet) (sol *Solution, err error) {
if c == nil {
err = errors.New("'c' must a column matrix")
return
}
n := c.Rows()
if n < 1 {
err = errors.New("Number of variables must be at least 1")
return
}
if G == nil || G.Cols() != n {
err = errors.New(fmt.Sprintf("'G' must be matrix with %d columns", n))
return
}
m := G.Rows()
if h == nil || !h.SizeMatch(m, 1) {
err = errors.New(fmt.Sprintf("'h' must be matrix of size (%d,1)", m))
return
}
if A == nil {
A = matrix.FloatZeros(0, n)
}
if A.Cols() != n {
err = errors.New(fmt.Sprintf("'A' must be matrix with %d columns", n))
return
}
p := A.Rows()
if b == nil {
b = matrix.FloatZeros(0, 1)
}
if !b.SizeMatch(p, 1) {
err = errors.New(fmt.Sprintf("'b' must be matrix of size (%d,1)", p))
return
}
dims := DSetNew("l", "q", "s")
dims.Set("l", []int{m})
return ConeLp(c, G, h, A, b, dims, solopts, primalstart, dualstart)
}
func main() {
Sdata := [][]float64{
[]float64{ 4e-2, 6e-3, -4e-3, 0.0 },
[]float64{ 6e-3, 1e-2, 0.0, 0.0 },
[]float64{-4e-3, 0.0, 2.5e-3, 0.0 },
[]float64{ 0.0, 0.0, 0.0, 0.0 }}
pbar := matrix.FloatVector([]float64{.12, .10, .07, .03})
S := matrix.FloatMatrixStacked(Sdata)
n := pbar.Rows()
G := matrix.FloatDiagonal(n, -1.0)
h := matrix.FloatZeros(n, 1)
A := matrix.FloatWithValue(1, n, 1.0)
b := matrix.FloatNew(1,1, []float64{1.0})
var solopts cvx.SolverOptions
solopts.MaxIter = 30
solopts.ShowProgress = true
mu := 1.0
Smu := S.Copy().Scale(mu)
pbarNeg := pbar.Copy().Scale(-1.0)
fmt.Printf("Smu=\n%v\n", Smu.String())
fmt.Printf("-pbar=\n%v\n", pbarNeg.String())
sol, err := cvx.Qp(Smu, pbarNeg, G, h, A, b, &solopts, nil)
fmt.Printf("status: %v\n", err)
if sol != nil && sol.Status == cvx.Optimal {
x := sol.Result.At("x")[0]
ret := blas.DotFloat(x, pbar)
risk := math.Sqrt(blas.DotFloat(x, S.Times(x)))
fmt.Printf("ret=%.3f, risk=%.3f\n", ret, risk)
fmt.Printf("x=\n%v\n", x)
}
}
func floorplan(Amin *matrix.FloatMatrix) *matrix.FloatMatrix {
rho := 1.0
gamma := 5.0
c := matrix.FloatZeros(22, 1)
c.SetAtColumnArray(0, []int{0, 1}, []float64{1.0, 1.0})
G := matrix.FloatZeros(26, 22)
h := matrix.FloatZeros(26, 1)
// -x1 <= 0
G.SetAt(0, 2, -1.0)
// -x2 <= 0
G.SetAt(1, 3, -1.0)
// -x4 <= 0
G.SetAt(2, 5, -1.0)
// x1 - x3 + w1 <= -rho
G.SetAtRowArray(3, []int{2, 4, 12}, []float64{1.0, -1.0, 1.0})
h.SetAt(3, 0, -rho)
// x2 - x3 + w2 <= -rho
G.SetAtRowArray(4, []int{3, 4, 13}, []float64{1.0, -1.0, 1.0})
h.SetAt(4, 0, -rho)
// x3 - x5 + w3 <= -rho
G.SetAtRowArray(5, []int{4, 6, 14}, []float64{1.0, -1.0, 1.0})
h.SetAt(5, 0, -rho)
// x4 - x5 + w4 <= -rho
G.SetAtRowArray(6, []int{5, 6, 15}, []float64{1.0, -1.0, 1.0})
h.SetAt(6, 0, -rho)
// -W + x5 + w5 <= 0
G.SetAtRowArray(7, []int{0, 6, 16}, []float64{-1.0, 1.0, 1.0})
// -y2 <= 0
G.SetAt(8, 8, -1.0)
// -y3 <= 0
G.SetAt(9, 9, -1.0)
// -y5 <= 0
G.SetAt(10, 11, -1.0)
// -y1 + y2 + h2 <= -rho
G.SetAtRowArray(11, []int{7, 8, 18}, []float64{-1.0, 1.0, 1.0})
h.SetAt(11, 0, -rho)
// y1 - y4 + h1 <= -rho
G.SetAtRowArray(12, []int{7, 10, 17}, []float64{1.0, -1.0, 1.0})
h.SetAt(12, 0, -rho)
// y3 - y4 + h3 <= -rho
G.SetAtRowArray(13, []int{9, 10, 19}, []float64{1.0, -1.0, 1.0})
h.SetAt(13, 0, -rho)
// -H + y4 + h4 <= 0
G.SetAtRowArray(14, []int{1, 10, 20}, []float64{-1.0, 1.0, 1.0})
// -H + y5 + h5 <= 0
G.SetAtRowArray(15, []int{1, 11, 21}, []float64{-1.0, 1.0, 1.0})
// -w1 + h1/gamma <= 0
G.SetAtRowArray(16, []int{12, 17}, []float64{-1.0, 1.0 / gamma})
// w1 - gamma * h1 <= 0
G.SetAtRowArray(17, []int{12, 17}, []float64{1.0, -gamma})
// -w2 + h2/gamma <= 0
G.SetAtRowArray(18, []int{13, 18}, []float64{-1.0, 1.0 / gamma})
// w2 - gamma * h2 <= 0
G.SetAtRowArray(19, []int{13, 18}, []float64{1.0, -gamma})
// -w3 + h3/gamma <= 0
G.SetAtRowArray(20, []int{14, 18}, []float64{-1.0, 1.0 / gamma})
// w3 - gamma * h3 <= 0
G.SetAtRowArray(21, []int{14, 19}, []float64{1.0, -gamma})
// -w4 + h4/gamma <= 0
G.SetAtRowArray(22, []int{15, 19}, []float64{-1.0, 1.0 / gamma})
// w4 - gamma * h4 <= 0
G.SetAtRowArray(23, []int{15, 20}, []float64{1.0, -gamma})
// -w5 + h5/gamma <= 0
G.SetAtRowArray(24, []int{16, 21}, []float64{-1.0, 1.0 / gamma})
// w5 - gamma * h5 <= 0.0
G.SetAtRowArray(25, []int{16, 21}, []float64{1.0, -gamma})
F := NewFloorPlan(Amin)
var dims *cvx.DimensionSet = nil
var solopts cvx.SolverOptions
solopts.MaxIter = 50
solopts.ShowProgress = true
sol, err := cvx.Cpl(F, c, G, h, nil, nil, dims, &solopts)
if err == nil && sol.Status == cvx.Optimal {
return sol.Result.At("x")[0]
} else {
fmt.Printf("result: %v\n", err)
}
return nil
}
/*
Applies Nesterov-Todd scaling or its inverse.
Computes
x := W*x (trans is false 'N', inverse = false 'N')
x := W^T*x (trans is true 'T', inverse = false 'N')
x := W^{-1}*x (trans is false 'N', inverse = true 'T')
x := W^{-T}*x (trans is true 'T', inverse = true 'T').
x is a dense float matrix.
W is a MatrixSet with entries:
- W['dnl']: positive vector
- W['dnli']: componentwise inverse of W['dnl']
- W['d']: positive vector
- W['di']: componentwise inverse of W['d']
- W['v']: lists of 2nd order cone vectors with unit hyperbolic norms
- W['beta']: list of positive numbers
- W['r']: list of square matrices
- W['rti']: list of square matrices. rti[k] is the inverse transpose
of r[k].
The 'dnl' and 'dnli' entries are optional, and only present when the
function is called from the nonlinear solver.
*/
func scale(x *matrix.FloatMatrix, W *FloatMatrixSet, trans, inverse bool) (err error) {
/*DEBUGGED*/
var wl []*matrix.FloatMatrix
var w *matrix.FloatMatrix
ind := 0
err = nil
// Scaling for nonlinear component xk is xk := dnl .* xk; inverse
// scaling is xk ./ dnl = dnli .* xk, where dnl = W['dnl'],
// dnli = W['dnli'].
if wl = W.At("dnl"); wl != nil {
if inverse {
w = W.At("dnli")[0]
} else {
w = W.At("dnl")[0]
}
for k := 0; k < x.Cols(); k++ {
err = blas.TbmvFloat(w, x, &la_.IOpt{"n", w.Rows()}, &la_.IOpt{"k", 0},
&la_.IOpt{"lda", 1}, &la_.IOpt{"offsetx", k * x.Rows()})
if err != nil {
return
}
}
ind += w.Rows()
}
// Scaling for linear 'l' component xk is xk := d .* xk; inverse
// scaling is xk ./ d = di .* xk, where d = W['d'], di = W['di'].
if inverse {
w = W.At("di")[0]
} else {
w = W.At("d")[0]
}
for k := 0; k < x.Cols(); k++ {
err = blas.TbmvFloat(w, x, &la_.IOpt{"n", w.Rows()}, &la_.IOpt{"k", 0},
&la_.IOpt{"lda", 1}, &la_.IOpt{"offsetx", k*x.Rows() + ind})
if err != nil {
return
}
}
ind += w.Rows()
// Scaling for 'q' component is
//
// xk := beta * (2*v*v' - J) * xk
// = beta * (2*v*(xk'*v)' - J*xk)
//
// where beta = W['beta'][k], v = W['v'][k], J = [1, 0; 0, -I].
//
//Inverse scaling is
//
// xk := 1/beta * (2*J*v*v'*J - J) * xk
// = 1/beta * (-J) * (2*v*((-J*xk)'*v)' + xk).
//wf := matrix.FloatZeros(x.Cols(), 1)
w = matrix.FloatZeros(x.Cols(), 1)
for k, v := range W.At("v") {
m := v.Rows()
if inverse {
blas.ScalFloat(x, -1.0, &la_.IOpt{"offset", ind}, &la_.IOpt{"inc", x.Rows()})
}
err = blas.GemvFloat(x, v, w, 1.0, 0.0, la_.OptTrans, &la_.IOpt{"m", m},
&la_.IOpt{"n", x.Cols()}, &la_.IOpt{"offsetA", ind},
&la_.IOpt{"lda", x.Rows()})
if err != nil {
return
}
err = blas.ScalFloat(x, -1.0, &la_.IOpt{"offset", ind}, &la_.IOpt{"inc", x.Rows()})
if err != nil {
return
}
err = blas.GerFloat(v, w, x, 2.0, &la_.IOpt{"m", m},
&la_.IOpt{"n", x.Cols()}, &la_.IOpt{"lda", x.Rows()},
&la_.IOpt{"offsetA", ind})
if err != nil {
return
}
var a float64
if inverse {
blas.ScalFloat(x, -1.0,
&la_.IOpt{"offset", ind}, &la_.IOpt{"inc", x.Rows()})
// a[i,j] := 1.0/W[i,j]
a = 1.0 / W.At("beta")[0].GetIndex(k)
} else {
a = W.At("beta")[0].GetIndex(k)
}
for i := 0; i < x.Cols(); i++ {
blas.ScalFloat(x, a, &la_.IOpt{"n", m}, &la_.IOpt{"offset", ind + i*x.Rows()})
}
ind += m
}
// Scaling for 's' component xk is
//
// xk := vec( r' * mat(xk) * r ) if trans = 'N'
// xk := vec( r * mat(xk) * r' ) if trans = 'T'.
//
// r is kth element of W['r'].
//
// Inverse scaling is
//
// xk := vec( rti * mat(xk) * rti' ) if trans = 'N'
// xk := vec( rti' * mat(xk) * rti ) if trans = 'T'.
//
// rti is kth element of W['rti'].
maxn := 0
for _, r := range W.At("r") {
if r.Rows() > maxn {
maxn = r.Rows()
}
}
a := matrix.FloatZeros(maxn, maxn)
for k, v := range W.At("r") {
t := trans
var r *matrix.FloatMatrix
if !inverse {
r = v
t = !trans
} else {
r = W.At("rti")[k]
}
n := r.Rows()
for i := 0; i < x.Cols(); i++ {
// scale diagonal of xk by 0.5
blas.ScalFloat(x, 0.5, &la_.IOpt{"offset", ind + i*x.Rows()},
&la_.IOpt{"inc", n + 1}, &la_.IOpt{"n", n})
// a = r*tril(x) (t is 'N') or a = tril(x)*r (t is 'T')
blas.Copy(r, a)
if !t {
err = blas.TrmmFloat(x, a, 1.0, la_.OptRight, &la_.IOpt{"m", n},
&la_.IOpt{"n", n}, &la_.IOpt{"lda", n}, &la_.IOpt{"ldb", n},
&la_.IOpt{"offsetA", ind + i*x.Rows()})
if err != nil {
return
}
// x := (r*a' + a*r') if t is 'N'
err = blas.Syr2kFloat(r, a, x, 1.0, 0.0, la_.OptNoTrans, &la_.IOpt{"n", n},
&la_.IOpt{"k", n}, &la_.IOpt{"ldb", n}, &la_.IOpt{"ldc", n},
&la_.IOpt{"offsetC", ind + i*x.Rows()})
if err != nil {
return
}
} else {
err = blas.TrmmFloat(x, a, 1.0, la_.OptLeft, &la_.IOpt{"m", n},
&la_.IOpt{"n", n}, &la_.IOpt{"lda", n}, &la_.IOpt{"ldb", n},
&la_.IOpt{"offsetA", ind + i*x.Rows()})
if err != nil {
return
}
// x := (r'*a + a'*r) if t is 'T'
err = blas.Syr2kFloat(r, a, x, 1.0, 0.0, la_.OptTrans, &la_.IOpt{"n", n},
&la_.IOpt{"k", n}, &la_.IOpt{"ldb", n}, &la_.IOpt{"ldc", n},
&la_.IOpt{"offsetC", ind + i*x.Rows()})
if err != nil {
return
}
}
}
ind += n * n
}
return
}
/*
Returns the Nesterov-Todd scaling W at points s and z, and stores the
scaled variable in lmbda.
W * z = W^{-T} * s = lmbda.
W is a MatrixSet with entries:
- W['dnl']: positive vector
- W['dnli']: componentwise inverse of W['dnl']
- W['d']: positive vector
- W['di']: componentwise inverse of W['d']
- W['v']: lists of 2nd order cone vectors with unit hyperbolic norms
- W['beta']: list of positive numbers
- W['r']: list of square matrices
- W['rti']: list of square matrices. rti[k] is the inverse transpose
of r[k].
*/
func computeScaling(s, z, lmbda *matrix.FloatMatrix, dims *DimensionSet, mnl int) (W *FloatMatrixSet, err error) {
/*DEBUGGED*/
err = nil
W = FloatSetNew("dnl", "dnli", "d", "di", "v", "beta", "r", "rti")
// For the nonlinear block:
//
// W['dnl'] = sqrt( s[:mnl] ./ z[:mnl] )
// W['dnli'] = sqrt( z[:mnl] ./ s[:mnl] )
// lambda[:mnl] = sqrt( s[:mnl] .* z[:mnl] )
var stmp, ztmp, lmd *matrix.FloatMatrix
if mnl > 0 {
stmp = matrix.FloatVector(s.FloatArray()[:mnl])
ztmp = matrix.FloatVector(z.FloatArray()[:mnl])
dnl := stmp.Div(ztmp)
dnl.Apply(dnl, math.Sqrt)
dnli := dnl.Copy()
dnli.Apply(dnli, func(a float64) float64 { return 1.0 / a })
W.Set("dnl", dnl)
W.Set("dnli", dnli)
lmd = stmp.Mul(ztmp)
lmd.Apply(lmd, math.Sqrt)
lmbda.SetIndexes(matrix.MakeIndexSet(0, mnl, 1), lmd.FloatArray())
} else {
mnl = 0
}
// For the 'l' block:
//
// W['d'] = sqrt( sk ./ zk )
// W['di'] = sqrt( zk ./ sk )
// lambdak = sqrt( sk .* zk )
//
// where sk and zk are the first dims['l'] entries of s and z.
// lambda_k is stored in the first dims['l'] positions of lmbda.
m := dims.At("l")[0]
td := s.FloatArray()
stmp = matrix.FloatVector(td[mnl : mnl+m])
zd := z.FloatArray()
//fmt.Printf("zdata=%v\n", zd[mnl:mnl+m])
ztmp = matrix.FloatVector(zd[mnl : mnl+m])
d := stmp.Div(ztmp)
d.Apply(d, math.Sqrt)
di := d.Copy()
di.Apply(di, func(a float64) float64 { return 1.0 / a })
//fmt.Printf("d:\n%v\n", d)
//fmt.Printf("di:\n%v\n", di)
W.Set("d", d)
W.Set("di", di)
lmd = stmp.Mul(ztmp)
lmd.Apply(lmd, math.Sqrt)
// lmd has indexes mnl:mnl+m and length of m
lmbda.SetIndexes(matrix.MakeIndexSet(mnl, mnl+m, 1), lmd.FloatArray())
//fmt.Printf("after l:\n%v\n", lmbda)
/*
For the 'q' blocks, compute lists 'v', 'beta'.
The vector v[k] has unit hyperbolic norm:
(sqrt( v[k]' * J * v[k] ) = 1 with J = [1, 0; 0, -I]).
beta[k] is a positive scalar.
The hyperbolic Householder matrix H = 2*v[k]*v[k]' - J
defined by v[k] satisfies
(beta[k] * H) * zk = (beta[k] * H) \ sk = lambda_k
where sk = s[indq[k]:indq[k+1]], zk = z[indq[k]:indq[k+1]].
lambda_k is stored in lmbda[indq[k]:indq[k+1]].
*/
ind := mnl + dims.At("l")[0]
var beta *matrix.FloatMatrix
for _, k := range dims.At("q") {
W.Append("v", matrix.FloatZeros(k, 1))
}
beta = matrix.FloatZeros(len(dims.At("q")), 1)
W.Set("beta", beta)
vset := W.At("v")
for k, m := range dims.At("q") {
v := vset[k]
// a = sqrt( sk' * J * sk ) where J = [1, 0; 0, -I]
aa := jnrm2(s, m, ind)
// b = sqrt( zk' * J * zk )
bb := jnrm2(z, m, ind)
// beta[k] = ( a / b )**1/2
beta.SetIndex(k, math.Sqrt(aa/bb))
// c = sqrt( (sk/a)' * (zk/b) + 1 ) / sqrt(2)
c0 := blas.DotFloat(s, z, &la_.IOpt{"n", m},
&la_.IOpt{"offsetx", ind}, &la_.IOpt{"offsety", ind})
cc := math.Sqrt((c0/aa/bb + 1.0) / 2.0)
// vk = 1/(2*c) * ( (sk/a) + J * (zk/b) )
blas.CopyFloat(z, v, &la_.IOpt{"offsetx", ind}, &la_.IOpt{"n", m})
blas.ScalFloat(v, -1.0/bb)
v.SetIndex(0, -1.0*v.GetIndex(0))
blas.AxpyFloat(s, v, 1.0/aa, &la_.IOpt{"offsetx", ind}, &la_.IOpt{"n", m})
blas.ScalFloat(v, 1.0/2.0/cc)
// v[k] = 1/sqrt(2*(vk0 + 1)) * ( vk + e ), e = [1; 0]
v.SetIndex(0, v.GetIndex(0)+1.0)
blas.ScalFloat(v, (1.0 / math.Sqrt(2.0*v.GetIndex(0))))
/*
To get the scaled variable lambda_k
d = sk0/a + zk0/b + 2*c
lambda_k = [ c;
(c + zk0/b)/d * sk1/a + (c + sk0/a)/d * zk1/b ]
lambda_k *= sqrt(a * b)
*/
lmbda.SetIndex(ind, cc)
dd := 2*cc + s.GetIndex(ind)/aa + z.GetIndex(ind)/bb
blas.CopyFloat(s, lmbda, &la_.IOpt{"offsetx", ind + 1}, &la_.IOpt{"offsety", ind + 1},
&la_.IOpt{"n", m - 1})
zz := (cc + z.GetIndex(ind)/bb) / dd / aa
ss := (cc + s.GetIndex(ind)/aa) / dd / bb
blas.ScalFloat(lmbda, zz, &la_.IOpt{"offset", ind + 1}, &la_.IOpt{"n", m - 1})
blas.AxpyFloat(z, lmbda, ss, &la_.IOpt{"offsetx", ind + 1},
&la_.IOpt{"offsety", ind + 1}, &la_.IOpt{"n", m - 1})
blas.ScalFloat(lmbda, math.Sqrt(aa*bb), &la_.IOpt{"offset", ind}, &la_.IOpt{"n", m})
ind += m
//fmt.Printf("after q[%d]:\n%v\n", k, lmbda)
}
/*
For the 's' blocks: compute two lists 'r' and 'rti'.
r[k]' * sk^{-1} * r[k] = diag(lambda_k)^{-1}
r[k]' * zk * r[k] = diag(lambda_k)
where sk and zk are the entries inds[k] : inds[k+1] of
s and z, reshaped into symmetric matrices.
rti[k] is the inverse of r[k]', so
rti[k]' * sk * rti[k] = diag(lambda_k)^{-1}
rti[k]' * zk^{-1} * rti[k] = diag(lambda_k).
The vectors lambda_k are stored in
lmbda[ dims['l'] + sum(dims['q']) : -1 ]
*/
for _, k := range dims.At("s") {
W.Append("r", matrix.FloatZeros(k, k))
W.Append("rti", matrix.FloatZeros(k, k))
}
maxs := maxdim(dims.At("s"))
work := matrix.FloatZeros(maxs*maxs, 1)
Ls := matrix.FloatZeros(maxs*maxs, 1)
Lz := matrix.FloatZeros(maxs*maxs, 1)
ind2 := ind
for k, m := range dims.At("s") {
r := W.At("r")[k]
rti := W.At("rti")[k]
// Factor sk = Ls*Ls'; store Ls in ds[inds[k]:inds[k+1]].
blas.CopyFloat(s, Ls, &la_.IOpt{"offsetx", ind2}, &la_.IOpt{"n", m * m})
lapack.PotrfFloat(Ls, &la_.IOpt{"n", m}, &la_.IOpt{"lda", m})
// Factor zs[k] = Lz*Lz'; store Lz in dz[inds[k]:inds[k+1]].
blas.CopyFloat(z, Lz, &la_.IOpt{"offsetx", ind2}, &la_.IOpt{"n", m * m})
lapack.PotrfFloat(Lz, &la_.IOpt{"n", m}, &la_.IOpt{"lda", m})
// SVD Lz'*Ls = U*diag(lambda_k)*V'. Keep U in work.
for i := 0; i < m; i++ {
blas.ScalFloat(Ls, 0.0, &la_.IOpt{"offset", i * m}, &la_.IOpt{"n", i})
}
blas.CopyFloat(Ls, work, &la_.IOpt{"n", m * m})
blas.TrmmFloat(Lz, work, 1.0, la_.OptTransA, &la_.IOpt{"lda", m}, &la_.IOpt{"ldb", m},
&la_.IOpt{"n", m}, &la_.IOpt{"m", m})
lapack.GesvdFloat(work, lmbda, nil, nil,
la_.OptJobuO, &la_.IOpt{"lda", m}, &la_.IOpt{"offsetS", ind},
&la_.IOpt{"n", m}, &la_.IOpt{"m", m})
// r = Lz^{-T} * U
blas.CopyFloat(work, r, &la_.IOpt{"n", m * m})
blas.TrsmFloat(Lz, r, 1.0, la_.OptTransA,
&la_.IOpt{"lda", m}, &la_.IOpt{"n", m}, &la_.IOpt{"m", m})
// rti = Lz * U
blas.CopyFloat(work, rti, &la_.IOpt{"n", m * m})
blas.TrmmFloat(Lz, rti, 1.0,
&la_.IOpt{"lda", m}, &la_.IOpt{"n", m}, &la_.IOpt{"m", m})
// r := r * diag(sqrt(lambda_k))
// rti := rti * diag(1 ./ sqrt(lambda_k))
for i := 0; i < m; i++ {
a := math.Sqrt(lmbda.GetIndex(ind + i))
blas.ScalFloat(r, a, &la_.IOpt{"offset", m * i}, &la_.IOpt{"n", m})
blas.ScalFloat(rti, 1.0/a, &la_.IOpt{"offset", m * i}, &la_.IOpt{"n", m})
}
ind += m
ind2 += m * m
}
return
}
func updateScaling(W *FloatMatrixSet, lmbda, s, z *matrix.FloatMatrix) (err error) {
err = nil
var stmp, ztmp *matrix.FloatMatrix
/*
Nonlinear and 'l' blocks
d := d .* sqrt( s ./ z )
lmbda := lmbda .* sqrt(s) .* sqrt(z)
*/
mnl := 0
dnlset := W.At("dnl")
dnliset := W.At("dnli")
dset := W.At("d")
diset := W.At("di")
beta := W.At("beta")[0]
if dnlset != nil && dnlset[0].NumElements() > 0 {
mnl = dnlset[0].NumElements()
}
ml := dset[0].NumElements()
m := mnl + ml
//fmt.Printf("ml=%d, mnl=%d, m=%d'n", ml, mnl, m)
stmp = matrix.FloatVector(s.FloatArray()[:m])
stmp.Apply(stmp, math.Sqrt)
s.SetIndexes(matrix.MakeIndexSet(0, m, 1), stmp.FloatArray())
ztmp = matrix.FloatVector(z.FloatArray()[:m])
ztmp.Apply(ztmp, math.Sqrt)
z.SetIndexes(matrix.MakeIndexSet(0, m, 1), ztmp.FloatArray())
// d := d .* s .* z
if len(dnlset) > 0 {
blas.TbmvFloat(s, dnlset[0], &la_.IOpt{"n", mnl}, &la_.IOpt{"k", 0}, &la_.IOpt{"lda", 1})
blas.TbsvFloat(z, dnlset[0], &la_.IOpt{"n", mnl}, &la_.IOpt{"k", 0}, &la_.IOpt{"lda", 1})
dnliset[0].Apply(dnlset[0], func(a float64) float64 { return 1.0 / a })
}
blas.TbmvFloat(s, dset[0], &la_.IOpt{"n", ml},
&la_.IOpt{"k", 0}, &la_.IOpt{"lda", 1}, &la_.IOpt{"offseta", mnl})
blas.TbsvFloat(z, dset[0], &la_.IOpt{"n", ml},
&la_.IOpt{"k", 0}, &la_.IOpt{"lda", 1}, &la_.IOpt{"offseta", mnl})
diset[0].Apply(dset[0], func(a float64) float64 { return 1.0 / a })
// lmbda := s .* z
blas.CopyFloat(s, lmbda, &la_.IOpt{"n", m})
blas.TbmvFloat(z, lmbda, &la_.IOpt{"n", m}, &la_.IOpt{"k", 0}, &la_.IOpt{"lda", 1})
//fmt.Printf("-- end of l:\nz=\n%v\nlmbda=\n%v\n", z.ConvertToString(), lmbda.ConvertToString())
//fmt.Printf("W[d]=\n%v\n", dset[0].ConvertToString())
//fmt.Printf("W[di]=\n%v\n", diset[0].ConvertToString())
// 'q' blocks.
// Let st and zt be the new variables in the old scaling:
//
// st = s_k, zt = z_k
//
// and a = sqrt(st' * J * st), b = sqrt(zt' * J * zt).
//
// 1. Compute the hyperbolic Householder transformation 2*q*q' - J
// that maps st/a to zt/b.
//
// c = sqrt( (1 + st'*zt/(a*b)) / 2 )
// q = (st/a + J*zt/b) / (2*c).
//
// The new scaling point is
//
// wk := betak * sqrt(a/b) * (2*v[k]*v[k]' - J) * q
//
// with betak = W['beta'][k].
//
// 3. The scaled variable:
//
// lambda_k0 = sqrt(a*b) * c
// lambda_k1 = sqrt(a*b) * ( (2vk*vk' - J) * (-d*q + u/2) )_1
//
// where
//
// u = st/a - J*zt/b
// d = ( vk0 * (vk'*u) + u0/2 ) / (2*vk0 *(vk'*q) - q0 + 1).
//
// 4. Update scaling
//
// v[k] := wk^1/2
// = 1 / sqrt(2*(wk0 + 1)) * (wk + e).
// beta[k] *= sqrt(a/b)
ind := m
for k, v := range W.At("v") {
m = v.NumElements()
// ln = sqrt( lambda_k' * J * lambda_k ) !! NOT USED!!
jnrm2(lmbda, m, ind) // ?? NOT USED ??
// a = sqrt( sk' * J * sk ) = sqrt( st' * J * st )
// s := s / a = st / a
aa := jnrm2(s, m, ind)
blas.ScalFloat(s, 1.0/aa, &la_.IOpt{"n", m}, &la_.IOpt{"offset", ind})
// b = sqrt( zk' * J * zk ) = sqrt( zt' * J * zt )
// z := z / a = zt / b
bb := jnrm2(z, m, ind)
blas.ScalFloat(z, 1.0/bb, &la_.IOpt{"n", m}, &la_.IOpt{"offset", ind})
// c = sqrt( ( 1 + (st'*zt) / (a*b) ) / 2 )
cc := blas.DotFloat(s, z, &la_.IOpt{"offsetx", ind}, &la_.IOpt{"offsety", ind},
&la_.IOpt{"n", m})
cc = math.Sqrt((1.0 + cc) / 2.0)
// vs = v' * st / a
vs := blas.DotFloat(v, s, &la_.IOpt{"offsety", ind}, &la_.IOpt{"n", m})
// vz = v' * J *zt / b
vz := jdot(v, z, m, 0, ind)
// vq = v' * q where q = (st/a + J * zt/b) / (2 * c)
vq := (vs + vz) / 2.0 / cc
// vq = v' * q where q = (st/a + J * zt/b) / (2 * c)
vu := vs - vz
// lambda_k0 = c
lmbda.SetIndex(ind, cc)
// wk0 = 2 * vk0 * (vk' * q) - q0
wk0 := 2.0*v.GetIndex(0)*vq - (s.GetIndex(ind)+z.GetIndex(ind))/2.0/cc
// d = (v[0] * (vk' * u) - u0/2) / (wk0 + 1)
dd := (v.GetIndex(0)*vu - s.GetIndex(ind)/2.0 + z.GetIndex(ind)/2.0) / (wk0 + 1.0)
// lambda_k1 = 2 * v_k1 * vk' * (-d*q + u/2) - d*q1 + u1/2
blas.CopyFloat(v, lmbda, &la_.IOpt{"offsetx", 1}, &la_.IOpt{"offsety", ind + 1},
&la_.IOpt{"n", m - 1})
blas.ScalFloat(lmbda, (2.0 * (-dd*vq + 0.5*vu)),
&la_.IOpt{"offsetx", ind + 1}, &la_.IOpt{"offsety", ind + 1}, &la_.IOpt{"n", m - 1})
blas.AxpyFloat(s, lmbda, 0.5*(1.0-dd/cc),
&la_.IOpt{"offsetx", ind + 1}, &la_.IOpt{"offsety", ind + 1}, &la_.IOpt{"n", m - 1})
blas.AxpyFloat(z, lmbda, 0.5*(1.0+dd/cc),
&la_.IOpt{"offsetx", ind + 1}, &la_.IOpt{"offsety", ind + 1}, &la_.IOpt{"n", m - 1})
// Scale so that sqrt(lambda_k' * J * lambda_k) = sqrt(aa*bb).
blas.ScalFloat(lmbda, math.Sqrt(aa*bb), &la_.IOpt{"offset", ind}, &la_.IOpt{"n", m})
// v := (2*v*v' - J) * q
// = 2 * (v'*q) * v' - (J* st/a + zt/b) / (2*c)
blas.ScalFloat(v, 2.0*vq)
v.SetIndex(0, v.GetIndex(0)-(s.GetIndex(ind)/2.0/cc))
blas.AxpyFloat(s, v, 0.5/cc, &la_.IOpt{"offsetx", ind + 1}, &la_.IOpt{"offsety", 1},
&la_.IOpt{"n", m - 1})
blas.AxpyFloat(z, v, -0.5/cc, &la_.IOpt{"offsetx", ind}, &la_.IOpt{"n", m})
// v := v^{1/2} = 1/sqrt(2 * (v0 + 1)) * (v + e)
v0 := v.GetIndex(0) + 1.0
v.SetIndex(0, v0)
blas.ScalFloat(v, 1.0/math.Sqrt(2.0*v0))
// beta[k] *= ( aa / bb )**1/2
bk := beta.GetIndex(k)
beta.SetIndex(k, bk*math.Sqrt(aa/bb))
ind += m
}
//fmt.Printf("-- end of q:\nz=\n%v\nlmbda=\n%v\n", z.ConvertToString(), lmbda.ConvertToString())
//fmt.Printf("beta=\n%v\n", beta.ConvertToString())
// 's' blocks
//
// Let st, zt be the updated variables in the old scaling:
//
// st = Ls * Ls', zt = Lz * Lz'.
//
// where Ls and Lz are the 's' components of s, z.
//
// 1. SVD Lz'*Ls = Uk * lambda_k^+ * Vk'.
//
// 2. New scaling is
//
// r[k] := r[k] * Ls * Vk * diag(lambda_k^+)^{-1/2}
// rti[k] := r[k] * Lz * Uk * diag(lambda_k^+)^{-1/2}.
//
maxr := 0
for _, m := range W.At("r") {
if m.Rows() > maxr {
maxr = m.Rows()
}
}
work := matrix.FloatZeros(maxr*maxr, 1)
vlensum := 0
for _, m := range W.At("v") {
vlensum += m.NumElements()
}
ind = mnl + ml + vlensum
ind2 := ind
ind3 := 0
rset := W.At("r")
rtiset := W.At("rti")
for k, _ := range rset {
r := rset[k]
rti := rtiset[k]
m = r.Rows()
//fmt.Printf("m=%d, r=\n%v\nrti=\n%v\n", m, r.ConvertToString(), rti.ConvertToString())
// r := r*sk = r*Ls
blas.GemmFloat(r, s, work, 1.0, 0.0, &la_.IOpt{"m", m}, &la_.IOpt{"n", m},
&la_.IOpt{"k", m}, &la_.IOpt{"ldb", m}, &la_.IOpt{"ldc", m},
&la_.IOpt{"offsetb", ind2})
//fmt.Printf("1 work=\n%v\n", work.ConvertToString())
blas.CopyFloat(work, r, &la_.IOpt{"n", m * m})
// rti := rti*zk = rti*Lz
blas.GemmFloat(rti, z, work, 1.0, 0.0, &la_.IOpt{"m", m}, &la_.IOpt{"n", m},
&la_.IOpt{"k", m}, &la_.IOpt{"ldb", m}, &la_.IOpt{"ldc", m},
&la_.IOpt{"offsetb", ind2})
//fmt.Printf("2 work=\n%v\n", work.ConvertToString())
blas.CopyFloat(work, rti, &la_.IOpt{"n", m * m})
// SVD Lz'*Ls = U * lmbds^+ * V'; store U in sk and V' in zk. '
blas.GemmFloat(z, s, work, 1.0, 0.0, la_.OptTransA, &la_.IOpt{"m", m},
&la_.IOpt{"n", m}, &la_.IOpt{"k", m}, &la_.IOpt{"lda", m}, &la_.IOpt{"ldb", m},
&la_.IOpt{"ldc", m}, &la_.IOpt{"offseta", ind2}, &la_.IOpt{"offsetb", ind2})
//fmt.Printf("3 work=\n%v\n", work.ConvertToString())
// U = s, Vt = z
lapack.GesvdFloat(work, lmbda, s, z, la_.OptJobuAll, la_.OptJobvtAll,
&la_.IOpt{"m", m}, &la_.IOpt{"n", m}, &la_.IOpt{"lda", m}, &la_.IOpt{"ldu", m},
&la_.IOpt{"ldvt", m}, &la_.IOpt{"offsets", ind}, &la_.IOpt{"offsetu", ind2},
&la_.IOpt{"offsetvt", ind2})
// r := r*V
blas.GemmFloat(r, z, work, 1.0, 0.0, la_.OptTransB, &la_.IOpt{"m", m},
&la_.IOpt{"n", m}, &la_.IOpt{"k", m}, &la_.IOpt{"ldb", m}, &la_.IOpt{"ldc", m},
&la_.IOpt{"offsetb", ind2})
//fmt.Printf("4 work=\n%v\n", work.ConvertToString())
blas.CopyFloat(work, r, &la_.IOpt{"n", m * m})
// rti := rti*U
blas.GemmFloat(rti, s, work, 1.0, 0.0, &la_.IOpt{"m", m}, &la_.IOpt{"n", m},
&la_.IOpt{"k", m}, &la_.IOpt{"ldb", m}, &la_.IOpt{"ldc", m},
&la_.IOpt{"offsetb", ind2})
//fmt.Printf("5 work=\n%v\n", work.ConvertToString())
blas.CopyFloat(work, rti, &la_.IOpt{"n", m * m})
for i := 0; i < m; i++ {
a := 1.0 / math.Sqrt(lmbda.GetIndex(ind+i))
blas.ScalFloat(r, a, &la_.IOpt{"n", m}, &la_.IOpt{"offset", m * i})
blas.ScalFloat(rti, a, &la_.IOpt{"n", m}, &la_.IOpt{"offset", m * i})
}
ind += m
ind2 += m * m
ind3 += m // !!NOT USED: ind3!!
}
//fmt.Printf("-- end of s:\nz=\n%v\nlmbda=\n%v\n", z.ConvertToString(), lmbda.ConvertToString())
return
}
// Solves a pair of primal and dual SOCPs
//
// minimize c'*x
// subject to Gl*x + sl = hl
// Gq[k]*x + sq[k] = hq[k], k = 0, ..., N-1
// A*x = b
// sl >= 0,
// sq[k] >= 0, k = 0, ..., N-1
//
// maximize -hl'*z - sum_k hq[k]'*zq[k] - b'*y
// subject to Gl'*zl + sum_k Gq[k]'*zq[k] + A'*y + c = 0
// zl >= 0, zq[k] >= 0, k = 0, ..., N-1.
//
// The inequalities sl >= 0 and zl >= 0 are elementwise vector
// inequalities. The inequalities sq[k] >= 0, zq[k] >= 0 are second
// order cone inequalities, i.e., equivalent to
//
// sq[k][0] >= || sq[k][1:] ||_2, zq[k][0] >= || zq[k][1:] ||_2.
//
func Socp(c, Gl, hl, A, b *matrix.FloatMatrix, Ghq *FloatMatrixSet, solopts *SolverOptions, primalstart, dualstart *FloatMatrixSet) (sol *Solution, err error) {
if c == nil {
err = errors.New("'c' must a column matrix")
return
}
n := c.Rows()
if n < 1 {
err = errors.New("Number of variables must be at least 1")
return
}
if Gl == nil {
Gl = matrix.FloatZeros(0, n)
}
if Gl.Cols() != n {
err = errors.New(fmt.Sprintf("'G' must be matrix with %d columns", n))
return
}
ml := Gl.Rows()
if hl == nil {
hl = matrix.FloatZeros(0, 1)
}
if !hl.SizeMatch(ml, 1) {
err = errors.New(fmt.Sprintf("'hl' must be matrix of size (%d,1)", ml))
return
}
Gqset := Ghq.At("Gq")
mq := make([]int, 0)
for i, Gq := range Gqset {
if Gq.Cols() != n {
err = errors.New(fmt.Sprintf("'Gq' must be list of matrices with %d columns", n))
return
}
if Gq.Rows() == 0 {
err = errors.New(fmt.Sprintf("the number of rows of 'Gq[%d]' is zero", i))
return
}
mq = append(mq, Gq.Rows())
}
hqset := Ghq.At("hq")
if len(Gqset) != len(hqset) {
err = errors.New(fmt.Sprintf("'hq' must be a list of %d matrices", len(Gqset)))
return
}
for i, hq := range hqset {
if !hq.SizeMatch(Gqset[i].Rows(), 1) {
s := fmt.Sprintf("hq[%d] has size (%d,%d). Expected size is (%d,1)",
i, hq.Rows(), hq.Cols(), Gqset[i].Rows())
err = errors.New(s)
return
}
}
if A == nil {
A = matrix.FloatZeros(0, n)
}
if A.Cols() != n {
err = errors.New(fmt.Sprintf("'A' must be matrix with %d columns", n))
return
}
p := A.Rows()
if b == nil {
b = matrix.FloatZeros(0, 1)
}
if !b.SizeMatch(p, 1) {
err = errors.New(fmt.Sprintf("'b' must be matrix of size (%d,1)", p))
return
}
dims := DSetNew("l", "q", "s")
dims.Set("l", []int{ml})
dims.Set("q", mq)
//N := dims.Sum("l", "q")
hargs := make([]*matrix.FloatMatrix, 0, len(hqset)+1)
hargs = append(hargs, hl)
hargs = append(hargs, hqset...)
h, indh := matrix.FloatMatrixCombined(matrix.StackDown, hargs...)
Gargs := make([]*matrix.FloatMatrix, 0, len(Gqset)+1)
Gargs = append(Gargs, Gl)
Gargs = append(Gargs, Gqset...)
G, indg := matrix.FloatMatrixCombined(matrix.StackDown, Gargs...)
var pstart, dstart *FloatMatrixSet = nil, nil
if primalstart != nil {
pstart = FloatSetNew("x", "s")
pstart.Set("x", primalstart.At("x")[0])
slset := primalstart.At("sl")
margs := make([]*matrix.FloatMatrix, 0, len(slset)+1)
margs = append(margs, primalstart.At("s")[0])
margs = append(margs, slset...)
sl, _ := matrix.FloatMatrixCombined(matrix.StackDown, margs...)
pstart.Set("s", sl)
}
if dualstart != nil {
dstart = FloatSetNew("y", "z")
dstart.Set("y", dualstart.At("y")[0])
zlset := primalstart.At("zl")
margs := make([]*matrix.FloatMatrix, 0, len(zlset)+1)
margs = append(margs, dualstart.At("z")[0])
margs = append(margs, zlset...)
zl, _ := matrix.FloatMatrixCombined(matrix.StackDown, margs...)
dstart.Set("z", zl)
}
sol, err = ConeLp(c, G, h, A, b, dims, solopts, pstart, dstart)
// unpack sol.Result
if err == nil {
s := sol.Result.At("s")[0]
sl := matrix.FloatVector(s.FloatArray()[:ml])
sol.Result.Append("sl", sl)
ind := ml
for _, k := range indh[1:] {
sk := matrix.FloatVector(s.FloatArray()[ind : ind+k])
sol.Result.Append("sq", sk)
ind += k
}
z := sol.Result.At("z")[0]
zl := matrix.FloatVector(z.FloatArray()[:ml])
sol.Result.Append("zl", zl)
ind = ml
for _, k := range indg[1:] {
zk := matrix.FloatVector(z.FloatArray()[ind : ind+k])
sol.Result.Append("zq", zk)
ind += k
}
}
sol.Result.Remove("s")
sol.Result.Remove("z")
return
}
// The product x := (y o x). If diag is 'D', the 's' part of y is
// diagonal and only the diagonal is stored.
func sprod(x, y *matrix.FloatMatrix, dims *DimensionSet, mnl int, opts ...la_.Option) (err error) {
err = nil
diag := la_.GetStringOpt("diag", "N", opts...)
// For the nonlinear and 'l' blocks:
//
// yk o xk = yk .* xk.
ind := mnl + dims.At("l")[0]
err = blas.Tbmv(y, x, &la_.IOpt{"n", ind}, &la_.IOpt{"k", 0}, &la_.IOpt{"lda", 1})
if err != nil {
return
}
//fmt.Printf("Sprod l:x=\n%v\n", x)
// For 'q' blocks:
//
// [ l0 l1' ]
// yk o xk = [ ] * xk
// [ l1 l0*I ]
//
// where yk = (l0, l1).
for _, m := range dims.At("q") {
dd := blas.DotFloat(x, y, &la_.IOpt{"offsetx", ind}, &la_.IOpt{"offsety", ind},
&la_.IOpt{"n", m})
//fmt.Printf("dd=%v\n", dd)
alpha := y.GetIndex(ind)
//fmt.Printf("scal=%v\n", alpha)
blas.ScalFloat(x, alpha, &la_.IOpt{"offset", ind + 1}, &la_.IOpt{"n", m - 1})
alpha = x.GetIndex(ind)
//fmt.Printf("axpy=%v\n", alpha)
blas.AxpyFloat(y, x, alpha, &la_.IOpt{"offsetx", ind + 1}, &la_.IOpt{"offsety", ind + 1},
&la_.IOpt{"n", m - 1})
x.SetIndex(ind, dd)
ind += m
}
//fmt.Printf("Sprod q :x=\n%v\n", x)
// For the 's' blocks:
//
// yk o sk = .5 * ( Yk * mat(xk) + mat(xk) * Yk )
//
// where Yk = mat(yk) if diag is 'N' and Yk = diag(yk) if diag is 'D'.
if diag[0] == 'N' {
// DEBUGGED
maxm := maxdim(dims.At("s"))
A := matrix.FloatZeros(maxm, maxm)
for _, m := range dims.At("s") {
blas.Copy(x, A, &la_.IOpt{"offsetx", ind}, &la_.IOpt{"n", m * m})
for i := 0; i < m-1; i++ { // i < m-1 --> i < m
symm(A, m, 0)
symm(y, m, ind)
}
err = blas.Syr2kFloat(A, y, x, 0.5, 0.0, &la_.IOpt{"n", m}, &la_.IOpt{"k", m},
&la_.IOpt{"lda", m}, &la_.IOpt{"ldb", m}, &la_.IOpt{"ldc", m},
&la_.IOpt{"offsetb", ind}, &la_.IOpt{"offsetc", ind})
if err != nil {
return
}
ind += m * m
}
//fmt.Printf("Sprod diag=N s:x=\n%v\n", x)
} else {
ind2 := ind
for _, m := range dims.At("s") {
for i := 0; i < m; i++ {
// original: u = 0.5 * ( y[ind2+i:ind2+m] + y[ind2+i] )
// creates matrix of elements: [ind2+i ... ind2+m] then
// element wisely adds y[ind2+i] and scales by 0.5
iset := matrix.MakeIndexSet(ind2+i, ind2+m, 1)
u := matrix.FloatVector(y.GetIndexes(iset))
u.Add(y.GetIndex(ind2 + i))
u.Scale(0.5)
err = blas.Tbmv(u, x, &la_.IOpt{"n", m - i}, &la_.IOpt{"k", 0}, &la_.IOpt{"lda", 1},
&la_.IOpt{"offsetx", ind + i*(m+1)})
if err != nil {
return
}
}
ind += m * m
ind2 += m
}
//fmt.Printf("Sprod diag=T s:x=\n%v\n", x)
}
return
}
// Solves a pair of primal and dual SDPs
//
// minimize c'*x
// subject to Gl*x + sl = hl
// mat(Gs[k]*x) + ss[k] = hs[k], k = 0, ..., N-1
// A*x = b
// sl >= 0, ss[k] >= 0, k = 0, ..., N-1
//
// maximize -hl'*z - sum_k trace(hs[k]*zs[k]) - b'*y
// subject to Gl'*zl + sum_k Gs[k]'*vec(zs[k]) + A'*y + c = 0
// zl >= 0, zs[k] >= 0, k = 0, ..., N-1.
//
// The inequalities sl >= 0 and zl >= 0 are elementwise vector
// inequalities. The inequalities ss[k] >= 0, zs[k] >= 0 are matrix
// inequalities, i.e., the symmetric matrices ss[k] and zs[k] must be
// positive semidefinite. mat(Gs[k]*x) is the symmetric matrix X with
// X[:] = Gs[k]*x. For a symmetric matrix, zs[k], vec(zs[k]) is the
// vector zs[k][:].
//
func Sdp(c, Gl, hl, A, b *matrix.FloatMatrix, Ghs *FloatMatrixSet, solopts *SolverOptions, primalstart, dualstart *FloatMatrixSet) (sol *Solution, err error) {
if c == nil {
err = errors.New("'c' must a column matrix")
return
}
n := c.Rows()
if n < 1 {
err = errors.New("Number of variables must be at least 1")
return
}
if Gl == nil {
Gl = matrix.FloatZeros(0, n)
}
if Gl.Cols() != n {
err = errors.New(fmt.Sprintf("'G' must be matrix with %d columns", n))
return
}
ml := Gl.Rows()
if hl == nil {
hl = matrix.FloatZeros(0, 1)
}
if !hl.SizeMatch(ml, 1) {
err = errors.New(fmt.Sprintf("'hl' must be matrix of size (%d,1)", ml))
return
}
Gsset := Ghs.At("Gs")
ms := make([]int, 0)
for i, Gs := range Gsset {
if Gs.Cols() != n {
err = errors.New(fmt.Sprintf("'Gs' must be list of matrices with %d columns", n))
return
}
sz := int(math.Sqrt(float64(Gs.Rows())))
if Gs.Rows() != sz*sz {
err = errors.New(fmt.Sprintf("the squareroot of the number of rows of 'Gq[%d]' is not an integer", i))
return
}
ms = append(ms, sz)
}
hsset := Ghs.At("hs")
if len(Gsset) != len(hsset) {
err = errors.New(fmt.Sprintf("'hs' must be a list of %d matrices", len(Gsset)))
return
}
for i, hs := range hsset {
if !hs.SizeMatch(ms[i], ms[i]) {
s := fmt.Sprintf("hq[%d] has size (%d,%d). Expected size is (%d,%d)",
i, hs.Rows(), hs.Cols(), ms[i], ms[i])
err = errors.New(s)
return
}
}
if A == nil {
A = matrix.FloatZeros(0, n)
}
if A.Cols() != n {
err = errors.New(fmt.Sprintf("'A' must be matrix with %d columns", n))
return
}
p := A.Rows()
if b == nil {
b = matrix.FloatZeros(0, 1)
}
if !b.SizeMatch(p, 1) {
err = errors.New(fmt.Sprintf("'b' must be matrix of size (%d,1)", p))
return
}
dims := DSetNew("l", "q", "s")
dims.Set("l", []int{ml})
dims.Set("s", ms)
N := dims.Sum("l") + dims.SumSquared("s")
// Map hs matrices to h vector
h := matrix.FloatZeros(N, 1)
h.SetIndexes(matrix.MakeIndexSet(0, ml, 1), hl.FloatArray()[:ml])
ind := ml
for k, hs := range hsset {
h.SetIndexes(matrix.MakeIndexSet(ind, ind+ms[k]*ms[k], 1), hs.FloatArray())
ind += ms[k] * ms[k]
}
Gargs := make([]*matrix.FloatMatrix, 0)
Gargs = append(Gargs, Gl)
Gargs = append(Gargs, Gsset...)
G, sizeg := matrix.FloatMatrixCombined(matrix.StackDown, Gargs...)
var pstart, dstart *FloatMatrixSet = nil, nil
if primalstart != nil {
pstart = FloatSetNew("x", "s")
pstart.Set("x", primalstart.At("x")[0])
slset := primalstart.At("sl")
margs := make([]*matrix.FloatMatrix, 0, len(slset)+1)
margs = append(margs, primalstart.At("s")[0])
margs = append(margs, slset...)
sl, _ := matrix.FloatMatrixCombined(matrix.StackDown, margs...)
pstart.Set("s", sl)
}
if dualstart != nil {
dstart = FloatSetNew("y", "z")
dstart.Set("y", dualstart.At("y")[0])
zlset := primalstart.At("zl")
margs := make([]*matrix.FloatMatrix, 0, len(zlset)+1)
margs = append(margs, dualstart.At("z")[0])
margs = append(margs, zlset...)
zl, _ := matrix.FloatMatrixCombined(matrix.StackDown, margs...)
dstart.Set("z", zl)
}
sol, err = ConeLp(c, G, h, A, b, dims, solopts, pstart, dstart)
// unpack sol.Result
if err == nil {
s := sol.Result.At("s")[0]
sl := matrix.FloatVector(s.FloatArray()[:ml])
sol.Result.Append("sl", sl)
ind := ml
for _, m := range ms {
sk := matrix.FloatNew(m, m, s.FloatArray()[ind:ind+m*m])
sol.Result.Append("ss", sk)
ind += m * m
}
z := sol.Result.At("z")[0]
zl := matrix.FloatVector(s.FloatArray()[:ml])
sol.Result.Append("zl", zl)
ind = ml
for i, k := range sizeg[1:] {
zk := matrix.FloatNew(ms[i], ms[i], z.FloatArray()[ind:ind+k])
sol.Result.Append("zs", zk)
ind += k
}
}
sol.Result.Remove("s")
sol.Result.Remove("z")
return
}
// Solves a pair of primal and dual cone programs
//
// minimize c'*x
// subject to G*x + s = h
// A*x = b
// s >= 0
//
// maximize -h'*z - b'*y
// subject to G'*z + A'*y + c = 0
// z >= 0.
//
// The inequalities are with respect to a cone C defined as the Cartesian
// product of N + M + 1 cones:
//
// C = C_0 x C_1 x .... x C_N x C_{N+1} x ... x C_{N+M}.
//
// The first cone C_0 is the nonnegative orthant of dimension ml.
// The next N cones are second order cones of dimension mq[0], ...,
// mq[N-1]. The second order cone of dimension m is defined as
//
// { (u0, u1) in R x R^{m-1} | u0 >= ||u1||_2 }.
//
// The next M cones are positive semidefinite cones of order ms[0], ...,
// ms[M-1] >= 0.
//
func ConeLp(c, G, h, A, b *matrix.FloatMatrix, dims *DimensionSet, solopts *SolverOptions, primalstart, dualstart *FloatMatrixSet) (sol *Solution, err error) {
err = nil
const EXPON = 3
const STEP = 0.99
sol = &Solution{Unknown,
nil, nil, nil, nil, nil,
0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0}
//var primalstart *FloatMatrixSet = nil
//var dualstart *FloatMatrixSet = nil
var refinement int
if solopts.Refinement > 0 {
refinement = solopts.Refinement
} else {
refinement = 0
if len(dims.At("q")) > 0 || len(dims.At("s")) > 0 {
refinement = 1
}
}
feasTolerance := FEASTOL
absTolerance := ABSTOL
relTolerance := RELTOL
if solopts.FeasTol > 0.0 {
feasTolerance = solopts.FeasTol
}
if solopts.AbsTol > 0.0 {
absTolerance = solopts.AbsTol
}
if solopts.RelTol > 0.0 {
relTolerance = solopts.RelTol
}
solvername := solopts.KKTSolverName
if len(solvername) == 0 {
if dims != nil && (len(dims.At("q")) > 0 || len(dims.At("s")) > 0) {
solvername = "qr"
} else {
solvername = "chol2"
}
}
if c == nil || c.Cols() > 1 {
err = errors.New("'c' must be matrix with 1 column")
return
}
if h == nil || h.Cols() > 1 {
err = errors.New("'h' must be matrix with 1 column")
return
}
if dims == nil {
dims = DSetNew("l", "q", "s")
dims.Set("l", []int{h.Rows()})
}
if err = checkConeLpDimensions(dims); err != nil {
return
}
cdim := dims.Sum("l", "q") + dims.SumSquared("s")
cdim_diag := dims.Sum("l", "q", "s")
if h.Rows() != cdim {
err = errors.New(fmt.Sprintf("'h' must be float matrix of size (%d,1)", cdim))
return
}
// Data for kth 'q' constraint are found in rows indq[k]:indq[k+1] of G.
indq := make([]int, 0, 100)
indq = append(indq, dims.At("l")[0])
for _, k := range dims.At("q") {
indq = append(indq, indq[len(indq)-1]+k)
}
// Data for kth 's' constraint are found in rows inds[k]:inds[k+1] of G.
inds := make([]int, 0, 100)
inds = append(inds, indq[len(indq)-1])
for _, k := range dims.At("s") {
inds = append(inds, inds[len(inds)-1]+k*k)
}
if G != nil && !G.SizeMatch(cdim, c.Rows()) {
estr := fmt.Sprintf("'G' must be of size (%d,%d)", cdim, c.Rows())
err = errors.New(estr)
return
}
Gf := func(x, y *matrix.FloatMatrix, alpha, beta float64, opts ...la.Option) error {
return sgemv(G, x, y, alpha, beta, dims, opts...)
}
// Check A and set defaults if it is nil
if A == nil {
// zeros rows reduces Gemv to vector products
A = matrix.FloatZeros(0, c.Rows())
}
if A.Cols() != c.Rows() {
estr := fmt.Sprintf("'A' must have %d columns", c.Rows())
err = errors.New(estr)
return
}
Af := func(x, y *matrix.FloatMatrix, alpha, beta float64, opts ...la.Option) error {
return blas.GemvFloat(A, x, y, alpha, beta, opts...)
}
// Check b and set defaults if it is nil
if b == nil {
b = matrix.FloatZeros(0, 1)
}
if b.Cols() != 1 {
estr := fmt.Sprintf("'b' must be a matrix with 1 column")
err = errors.New(estr)
return
}
if b.Rows() != A.Rows() {
estr := fmt.Sprintf("'b' must have length %d", A.Rows())
err = errors.New(estr)
return
}
// kktsolver(W) returns a routine for solving 3x3 block KKT system
//
// [ 0 A' G'*W^{-1} ] [ ux ] [ bx ]
// [ A 0 0 ] [ uy ] = [ by ].
// [ G 0 -W' ] [ uz ] [ bz ]
var factor kktFactor
var kktsolver kktFactor = nil
if kktfunc, ok := solvers[solvername]; ok {
// kkt function returns us problem spesific factor function.
factor, err = kktfunc(G, dims, A, 0)
// solver is
kktsolver = func(W *FloatMatrixSet, H, Df *matrix.FloatMatrix) (kktFunc, error) {
return factor(W, nil, nil)
}
} else {
err = errors.New(fmt.Sprintf("solver '%s' not known", solvername))
return
}
// res() evaluates residual in 5x5 block KKT system
//
// [ vx ] [ 0 ] [ 0 A' G' c ] [ ux ]
// [ vy ] [ 0 ] [-A 0 0 b ] [ uy ]
// [ vz ] += [ W'*us ] - [-G 0 0 h ] [ W^{-1}*uz ]
// [ vtau ] [ dg*ukappa ] [-c' -b' -h' 0 ] [ utau/dg ]
//
// vs += lmbda o (dz + ds)
// vkappa += lmbdg * (dtau + dkappa).
ws3 := matrix.FloatZeros(cdim, 1)
wz3 := matrix.FloatZeros(cdim, 1)
//
res := func(ux, uy, uz, utau, us, ukappa, vx, vy, vz, vtau, vs, vkappa *matrix.FloatMatrix, W *FloatMatrixSet, dg float64, lmbda *matrix.FloatMatrix) (err error) {
err = nil
// vx := vx - A'*uy - G'*W^{-1}*uz - c*utau/dg
Af(uy, vx, -1.0, 1.0, la.OptTrans)
//fmt.Printf("post-Af vx=\n%v\n", vx)
blas.Copy(uz, wz3)
scale(wz3, W, false, true)
Gf(wz3, vx, -1.0, 1.0, la.OptTrans)
blas.AxpyFloat(c, vx, -utau.Float()/dg)
// vy := vy + A*ux - b*utau/dg
Af(ux, vy, 1.0, 1.0)
blas.AxpyFloat(b, vy, -utau.Float()/dg)
// vz := vz + G*ux - h*utau/dg + W'*us
Gf(ux, vz, 1.0, 1.0)
blas.AxpyFloat(h, vz, -utau.Float()/dg)
blas.Copy(us, ws3)
scale(ws3, W, true, false)
blas.AxpyFloat(ws3, vz, 1.0)
// vtau := vtau + c'*ux + b'*uy + h'*W^{-1}*uz + dg*ukappa
var vtauplus float64 = dg*ukappa.Float() + blas.DotFloat(c, ux) +
blas.DotFloat(b, uy) + sdot(h, wz3, dims, 0)
vtau.SetValue(vtau.Float() + vtauplus)
// vs := vs + lmbda o (uz + us)
blas.Copy(us, ws3)
blas.AxpyFloat(uz, ws3, 1.0)
sprod(ws3, lmbda, dims, 0, &la.SOpt{"diag", "D"})
blas.AxpyFloat(ws3, vs, 1.0)
// vkappa += vkappa + lmbdag * (utau + ukappa)
lscale := lmbda.GetIndex(lmbda.NumElements() - 1)
var vkplus float64 = lscale * (utau.Float() + ukappa.Float())
vkappa.SetValue(vkappa.Float() + vkplus)
return
}
resx0 := math.Max(1.0, math.Sqrt(blas.DotFloat(c, c)))
resy0 := math.Max(1.0, math.Sqrt(blas.DotFloat(b, b)))
resz0 := math.Max(1.0, snrm2(h, dims, 0))
// select initial points
//fmt.Printf("** initial resx0=%.4f, resy0=%.4f, resz0=%.4f \n", resx0, resy0, resz0)
x := c.Copy()
blas.ScalFloat(x, 0.0)
y := b.Copy()
blas.ScalFloat(y, 0.0)
s := matrix.FloatZeros(cdim, 1)
z := matrix.FloatZeros(cdim, 1)
dx := c.Copy()
dy := b.Copy()
ds := matrix.FloatZeros(cdim, 1)
dz := matrix.FloatZeros(cdim, 1)
// these are singleton matrix
dkappa := matrix.FloatValue(0.0)
dtau := matrix.FloatValue(0.0)
var W *FloatMatrixSet
var f kktFunc
if primalstart == nil || dualstart == nil {
// Factor
//
// [ 0 A' G' ]
// [ A 0 0 ].
// [ G 0 -I ]
//
W = FloatSetNew("d", "di", "v", "beta", "r", "rti")
dd := dims.At("l")[0]
mat := matrix.FloatOnes(dd, 1)
W.Set("d", mat)
mat = matrix.FloatOnes(dd, 1)
W.Set("di", mat)
dq := len(dims.At("q"))
W.Set("beta", matrix.FloatOnes(dq, 1))
for _, n := range dims.At("q") {
vm := matrix.FloatZeros(n, 1)
vm.SetIndex(0, 1.0)
W.Append("v", vm)
}
for _, n := range dims.At("s") {
W.Append("r", matrix.FloatIdentity(n))
W.Append("rti", matrix.FloatIdentity(n))
}
f, err = kktsolver(W, nil, nil)
if err != nil {
fmt.Printf("kktsolver error: %s\n", err)
return
}
}
if primalstart == nil {
// minimize || G * x - h ||^2
// subject to A * x = b
//
// by solving
//
// [ 0 A' G' ] [ x ] [ 0 ]
// [ A 0 0 ] * [ dy ] = [ b ].
// [ G 0 -I ] [ -s ] [ h ]
blas.ScalFloat(x, 0.0)
blas.CopyFloat(y, dy)
blas.CopyFloat(h, s)
err = f(x, dy, s)
if err != nil {
fmt.Printf("f(x,dy,s): %s\n", err)
return
}
blas.ScalFloat(s, -1.0)
//fmt.Printf("** initial s:\n%v\n", s)
} else {
blas.Copy(primalstart.At("x")[0], x)
blas.Copy(primalstart.At("s")[0], s)
}
// ts = min{ t | s + t*e >= 0 }
ts, _ := maxStep(s, dims, 0, nil)
if ts >= 0 && primalstart != nil {
err = errors.New("initial s is not positive")
return
}
if dualstart == nil {
// minimize || z ||^2
// subject to G'*z + A'*y + c = 0
//
// by solving
//
// [ 0 A' G' ] [ dx ] [ -c ]
// [ A 0 0 ] [ y ] = [ 0 ].
// [ G 0 -I ] [ z ] [ 0 ]
blas.Copy(c, dx)
blas.ScalFloat(dx, -1.0)
blas.ScalFloat(y, 0.0)
err = f(dx, y, z)
if err != nil {
fmt.Printf("f(dx,y,z): %s\n", err)
return
}
} else {
if len(dualstart.At("y")) > 0 {
blas.Copy(dualstart.At("y")[0], y)
}
blas.Copy(dualstart.At("z")[0], z)
}
// ts = min{ t | z + t*e >= 0 }
tz, _ := maxStep(z, dims, 0, nil)
if tz >= 0 && dualstart != nil {
err = errors.New("initial z is not positive")
return
}
nrms := snrm2(s, dims, 0)
nrmz := snrm2(z, dims, 0)
gap := 0.0
pcost := 0.0
dcost := 0.0
relgap := 0.0
if primalstart == nil && dualstart == nil {
gap = sdot(s, z, dims, 0)
pcost = blas.DotFloat(c, x)
dcost = -blas.DotFloat(b, y) - sdot(h, z, dims, 0)
if pcost < 0.0 {
relgap = gap / -pcost
} else if dcost > 0.0 {
relgap = gap / dcost
} else {
relgap = math.NaN()
}
if ts <= 0 && tz < 0 &&
(gap <= absTolerance || (!math.IsNaN(relgap) && relgap <= relTolerance)) {
// Constructed initial points happen to be feasible and optimal
ind := dims.At("l")[0] + dims.Sum("q")
for _, m := range dims.At("s") {
symm(s, m, ind)
symm(z, m, ind)
ind += m * m
}
// rx = A'*y + G'*z + c
rx := c.Copy()
Af(y, rx, 1.0, 1.0, la.OptTrans)
Gf(z, rx, 1.0, 1.0, la.OptTrans)
resx := math.Sqrt(blas.Dot(rx, rx).Float())
// ry = b - A*x
ry := b.Copy()
Af(x, ry, -1.0, -1.0)
resy := math.Sqrt(blas.Dot(ry, ry).Float())
// rz = s + G*x - h
rz := matrix.FloatZeros(cdim, 1)
Gf(x, rz, 1.0, 0.0)
blas.AxpyFloat(s, rz, 1.0)
blas.AxpyFloat(h, rz, -1.0)
resz := snrm2(rz, dims, 0)
pres := math.Max(resy/resy0, resz/resz0)
dres := resx / resx0
cx := blas.Dot(c, x).Float()
by := blas.Dot(b, y).Float()
hz := sdot(h, z, dims, 0)
sol.X = x
sol.Y = y
sol.S = s
sol.Z = z
sol.Result = FloatSetNew("x", "y", "s", "x")
sol.Result.Append("x", x)
sol.Result.Append("y", y)
sol.Result.Append("s", s)
sol.Result.Append("z", z)
sol.Status = Optimal
sol.Gap = gap
sol.RelativeGap = relgap
sol.PrimalObjective = cx
sol.DualObjective = -(by + hz)
sol.PrimalInfeasibility = pres
sol.DualInfeasibility = dres
sol.PrimalSlack = -ts
sol.DualSlack = -tz
return
}
if ts >= -1e-8*math.Max(nrms, 1.0) {
a := 1.0 + ts
is := make([]int, 0)
// indexes s[:dims['l']]
is = append(is, matrix.MakeIndexSet(0, dims.At("l")[0], 1)...)
// indexes s[indq[:-1]]
is = append(is, indq[:len(indq)-1]...)
// indexes s[ind:ind+m*m:m+1] (diagonal)
ind := dims.Sum("l", "q")
for _, m := range dims.At("s") {
is = append(is, matrix.MakeIndexSet(ind, ind+m*m, m+1)...)
}
for _, k := range is {
s.SetIndex(k, a+s.GetIndex(k))
}
//fmt.Printf("scaled s=\n%v\n", s.ConvertToString())
}
if tz >= -1e-8*math.Max(nrmz, 1.0) {
a := 1.0 + tz
is := make([]int, 0)
// indexes z[:dims['l']]
is = append(is, matrix.MakeIndexSet(0, dims.At("l")[0], 1)...)
// indexes z[indq[:-1]]
is = append(is, indq[:len(indq)-1]...)
// indexes z[ind:ind+m*m:m+1] (diagonal)
ind := dims.Sum("l", "q")
for _, m := range dims.At("s") {
is = append(is, matrix.MakeIndexSet(ind, ind+m*m, m+1)...)
}
for _, k := range is {
z.SetIndex(k, a+z.GetIndex(k))
}
//fmt.Printf("scaled z=\n%v\n", z.ConvertToString())
}
} else if primalstart == nil && dualstart != nil {
if ts >= -1e-8*math.Max(nrms, 1.0) {
a := 1.0 + ts
is := make([]int, 0)
is = append(is, matrix.MakeIndexSet(0, dims.At("l")[0], 1)...)
is = append(is, indq[:len(indq)-1]...)
ind := dims.Sum("l", "q")
for _, m := range dims.At("s") {
is = append(is, matrix.MakeIndexSet(ind, ind+m*m, m+1)...)
}
for _, k := range is {
s.SetIndex(k, a+s.GetIndex(k))
}
}
} else if primalstart != nil && dualstart == nil {
if tz >= -1e-8*math.Max(nrmz, 1.0) {
a := 1.0 + tz
is := make([]int, 0)
is = append(is, matrix.MakeIndexSet(0, dims.At("l")[0], 1)...)
is = append(is, indq[:len(indq)-1]...)
ind := dims.Sum("l", "q")
for _, m := range dims.At("s") {
is = append(is, matrix.MakeIndexSet(ind, ind+m*m, m+1)...)
}
for _, k := range is {
z.SetIndex(k, a+z.GetIndex(k))
}
}
}
tau := matrix.FloatValue(1.0)
kappa := matrix.FloatValue(1.0)
wkappa3 := matrix.FloatValue(0.0)
rx := c.Copy()
hrx := c.Copy()
ry := b.Copy()
hry := b.Copy()
rz := matrix.FloatZeros(cdim, 1)
hrz := matrix.FloatZeros(cdim, 1)
sigs := matrix.FloatZeros(dims.Sum("s"), 1)
sigz := matrix.FloatZeros(dims.Sum("s"), 1)
lmbda := matrix.FloatZeros(cdim_diag+1, 1)
lmbdasq := matrix.FloatZeros(cdim_diag+1, 1)
gap = sdot(s, z, dims, 0)
var x1, y1, z1 *matrix.FloatMatrix
var dg, dgi float64
var th *matrix.FloatMatrix
var WS fClosure
var f3 kktFunc
//fmt.Printf("preloop x=\n%v\n", x.ConvertToString())
//fmt.Printf("preloop z=\n%v\n", z.ConvertToString())
//fmt.Printf("preloop s=\n%v\n", s.ConvertToString())
for iter := 0; iter < solopts.MaxIter+1; iter++ {
// hrx = -A'*y - G'*z
Af(y, hrx, -1.0, 0.0, la.OptTrans)
Gf(z, hrx, -1.0, 1.0, la.OptTrans)
hresx := math.Sqrt(blas.DotFloat(hrx, hrx))
// rx = hrx - c*tau
// = -A'*y - G'*z - c*tau
blas.Copy(hrx, rx)
err = blas.AxpyFloat(c, rx, -tau.Float())
resx := math.Sqrt(blas.DotFloat(rx, rx)) / tau.Float()
// hry = A*x
Af(x, hry, 1.0, 0.0)
hresy := math.Sqrt(blas.DotFloat(hry, hry))
// ry = hry - b*tau
// = A*x - b*tau
blas.Copy(hry, ry)
blas.AxpyFloat(b, ry, -tau.Float())
resy := math.Sqrt(blas.DotFloat(ry, ry)) / tau.Float()
// hrz = s + G*x
Gf(x, hrz, 1.0, 0.0)
blas.AxpyFloat(s, hrz, 1.0)
hresz := snrm2(hrz, dims, 0)
// rz = hrz - h*tau
// = s + G*x - h*tau
blas.ScalFloat(rz, 0.0)
blas.AxpyFloat(hrz, rz, 1.0)
blas.AxpyFloat(h, rz, -tau.Float())
resz := snrm2(rz, dims, 0) / tau.Float()
// rt = kappa + c'*x + b'*y + h'*z '
cx := blas.DotFloat(c, x)
by := blas.DotFloat(b, y)
hz := sdot(h, z, dims, 0)
rt := kappa.Float() + cx + by + hz
// Statistics for stopping criteria
pcost = cx / tau.Float()
dcost = -(by + hz) / tau.Float()
if pcost < 0.0 {
relgap = gap / -pcost
} else if dcost > 0.0 {
relgap = gap / dcost
} else {
relgap = math.NaN()
}
pres := math.Max(resy/resy0, resz/resz0)
dres := resx / resx0
pinfres := math.NaN()
if hz+by < 0.0 {
pinfres = hresx / resx0 / (-hz - by)
}
dinfres := math.NaN()
if cx < 0.0 {
dinfres = math.Max(hresy/resy0, hresz/resz0) / (-cx)
}
if solopts.ShowProgress {
if iter == 0 {
// show headers of something
fmt.Printf("% 10s% 12s% 10s% 8s% 7s % 5s\n",
"pcost", "dcost", "gap", "pres", "dres", "k/t")
}
// show something
fmt.Printf("%2d: % 8.4e % 8.4e % 4.0e% 7.0e% 7.0e% 7.0e\n",
iter, pcost, dcost, gap, pres, dres, kappa.GetIndex(0)/tau.GetIndex(0))
}
if (pres <= feasTolerance && dres <= feasTolerance &&
(gap <= absTolerance || (!math.IsNaN(relgap) && relgap <= relTolerance))) ||
iter == solopts.MaxIter {
// done
blas.ScalFloat(x, 1.0/tau.Float())
blas.ScalFloat(y, 1.0/tau.Float())
blas.ScalFloat(s, 1.0/tau.Float())
blas.ScalFloat(z, 1.0/tau.Float())
ind := dims.Sum("l", "q")
for _, m := range dims.At("s") {
symm(s, m, ind)
symm(z, m, ind)
ind += m * m
}
ts, _ = maxStep(s, dims, 0, nil)
tz, _ = maxStep(z, dims, 0, nil)
if iter == solopts.MaxIter {
// MaxIterations exceeded
if solopts.ShowProgress {
fmt.Printf("No solution. Max iterations exceeded\n")
}
err = errors.New("No solution. Max iterations exceeded")
sol.X = x
sol.Y = y
sol.S = s
sol.Z = z
sol.Result = FloatSetNew("x", "y", "s", "x")
sol.Result.Append("x", x)
sol.Result.Append("y", y)
sol.Result.Append("s", s)
sol.Result.Append("z", z)
sol.Status = Unknown
sol.Gap = gap
sol.RelativeGap = relgap
sol.PrimalObjective = pcost
sol.DualObjective = dcost
sol.PrimalInfeasibility = pres
sol.DualInfeasibility = dres
sol.PrimalSlack = -ts
sol.DualSlack = -tz
sol.PrimalResidualCert = pinfres
sol.DualResidualCert = dinfres
sol.Iterations = iter
return
} else {
// Optimal
if solopts.ShowProgress {
fmt.Printf("Optimal solution.\n")
}
err = nil
sol.X = x
sol.Y = y
sol.S = s
sol.Z = z
sol.Result = FloatSetNew("x", "y", "s", "x")
sol.Result.Append("x", x)
sol.Result.Append("y", y)
sol.Result.Append("s", s)
sol.Result.Append("z", z)
sol.Status = Optimal
sol.Gap = gap
sol.RelativeGap = relgap
sol.PrimalObjective = pcost
sol.DualObjective = dcost
sol.PrimalInfeasibility = pres
sol.DualInfeasibility = dres
sol.PrimalSlack = -ts
sol.DualSlack = -tz
sol.PrimalResidualCert = math.NaN()
sol.DualResidualCert = math.NaN()
sol.Iterations = iter
return
}
} else if !math.IsNaN(pinfres) && pinfres <= feasTolerance {
// Primal Infeasible
if solopts.ShowProgress {
fmt.Printf("Primal infeasible.\n")
}
err = errors.New("Primal infeasible")
blas.ScalFloat(y, 1.0/(-hz-by))
blas.ScalFloat(z, 1.0/(-hz-by))
sol.X = nil
sol.Y = nil
sol.S = nil
sol.Z = nil
ind := dims.Sum("l", "q")
for _, m := range dims.At("s") {
symm(z, m, ind)
ind += m * m
}
tz, _ = maxStep(z, dims, 0, nil)
sol.Status = PrimalInfeasible
sol.Result = FloatSetNew("x", "y", "s", "x")
sol.Result.Append("x", nil)
sol.Result.Append("y", nil)
sol.Result.Append("s", nil)
sol.Result.Append("z", nil)
sol.Gap = math.NaN()
sol.RelativeGap = math.NaN()
sol.PrimalObjective = math.NaN()
sol.DualObjective = 1.0
sol.PrimalInfeasibility = math.NaN()
sol.DualInfeasibility = math.NaN()
sol.PrimalSlack = math.NaN()
sol.DualSlack = -tz
sol.PrimalResidualCert = pinfres
sol.DualResidualCert = math.NaN()
sol.Iterations = iter
return
} else if !math.IsNaN(dinfres) && dinfres <= feasTolerance {
// Dual Infeasible
if solopts.ShowProgress {
fmt.Printf("Dual infeasible.\n")
}
err = errors.New("Primal infeasible")
blas.ScalFloat(x, 1.0/(-cx))
blas.ScalFloat(s, 1.0/(-cx))
sol.X = nil
sol.Y = nil
sol.S = nil
sol.Z = nil
ind := dims.Sum("l", "q")
for _, m := range dims.At("s") {
symm(s, m, ind)
ind += m * m
}
ts, _ = maxStep(s, dims, 0, nil)
sol.Status = PrimalInfeasible
sol.Result = FloatSetNew("x", "y", "s", "x")
sol.Result.Append("x", nil)
sol.Result.Append("y", nil)
sol.Result.Append("s", nil)
sol.Result.Append("z", nil)
sol.Gap = math.NaN()
sol.RelativeGap = math.NaN()
sol.PrimalObjective = 1.0
sol.DualObjective = math.NaN()
sol.PrimalInfeasibility = math.NaN()
sol.DualInfeasibility = math.NaN()
sol.PrimalSlack = -ts
sol.DualSlack = math.NaN()
sol.PrimalResidualCert = math.NaN()
sol.DualResidualCert = dinfres
sol.Iterations = iter
return
}
// Compute initial scaling W:
//
// W * z = W^{-T} * s = lambda
// dg * tau = 1/dg * kappa = lambdag.
if iter == 0 {
W, err = computeScaling(s, z, lmbda, dims, 0)
// dg = sqrt( kappa / tau )
// dgi = sqrt( tau / kappa )
// lambda_g = sqrt( tau * kappa )
//
// lambda_g is stored in the last position of lmbda.
dg = math.Sqrt(kappa.Float() / tau.Float())
dgi = math.Sqrt(float64(tau.Float() / kappa.Float()))
lmbda.SetIndex(-1, math.Sqrt(float64(tau.Float()*kappa.Float())))
}
// lmbdasq := lmbda o lmbda
ssqr(lmbdasq, lmbda, dims, 0)
lmbdasq.SetIndex(-1, lmbda.GetIndex(-1)*lmbda.GetIndex(-1))
// f3(x, y, z) solves
//
// [ 0 A' G' ] [ ux ] [ bx ]
// [ A 0 0 ] [ uy ] = [ by ].
// [ G 0 -W'*W ] [ W^{-1}*uz ] [ bz ]
//
// On entry, x, y, z contain bx, by, bz.
// On exit, they contain ux, uy, uz.
//
// Also solve
//
// [ 0 A' G' ] [ x1 ] [ c ]
// [-A 0 0 ]*[ y1 ] = -dgi * [ b ].
// [-G 0 W'*W ] [ W^{-1}*z1 ] [ h ]
f3, err = kktsolver(W, nil, nil)
if err != nil {
fmt.Printf("kktsolver error=%v\n", err)
return
}
if iter == 0 {
x1 = c.Copy()
y1 = b.Copy()
z1 = matrix.FloatZeros(cdim, 1)
}
blas.Copy(c, x1)
blas.ScalFloat(x1, -1.0)
blas.Copy(b, y1)
blas.Copy(h, z1)
err = f3(x1, y1, z1)
//fmt.Printf("f3 result: x1=\n%v\nf3 result: z1=\n%v\n", x1, z1)
blas.ScalFloat(x1, dgi)
blas.ScalFloat(y1, dgi)
blas.ScalFloat(z1, dgi)
if err != nil {
if iter == 0 && primalstart != nil && dualstart != nil {
err = errors.New("Rank(A) < p or Rank([G; A]) < n")
return
} else {
t_ := 1.0 / tau.Float()
blas.ScalFloat(x, t_)
blas.ScalFloat(y, t_)
blas.ScalFloat(s, t_)
blas.ScalFloat(z, t_)
ind := dims.Sum("l", "q")
for _, m := range dims.At("s") {
symm(s, m, ind)
symm(z, m, ind)
ind += m * m
}
ts, _ = maxStep(s, dims, 0, nil)
tz, _ = maxStep(z, dims, 0, nil)
err = errors.New("Terminated (singular KKT matrix).")
sol.X = x
sol.Y = y
sol.S = s
sol.Z = z
sol.Result = FloatSetNew("x", "y", "s", "x")
sol.Result.Append("x", x)
sol.Result.Append("y", y)
sol.Result.Append("s", s)
sol.Result.Append("z", z)
sol.Status = Unknown
sol.RelativeGap = relgap
sol.PrimalObjective = pcost
sol.DualObjective = dcost
sol.PrimalInfeasibility = pres
sol.DualInfeasibility = dres
sol.PrimalSlack = -ts
sol.DualSlack = -tz
sol.Iterations = iter
return
}
}
// f6_no_ir(x, y, z, tau, s, kappa) solves
//
// [ 0 ] [ 0 A' G' c ] [ ux ] [ bx ]
// [ 0 ] [ -A 0 0 b ] [ uy ] [ by ]
// [ W'*us ] - [ -G 0 0 h ] [ W^{-1}*uz ] = -[ bz ]
// [ dg*ukappa ] [ -c' -b' -h' 0 ] [ utau/dg ] [ btau ]
//
// lmbda o (uz + us) = -bs
// lmbdag * (utau + ukappa) = -bkappa.
//
// On entry, x, y, z, tau, s, kappa contain bx, by, bz, btau,
// bkappa. On exit, they contain ux, uy, uz, utau, ukappa.
// th = W^{-T} * h
if iter == 0 {
th = matrix.FloatZeros(cdim, 1)
}
blas.Copy(h, th)
scale(th, W, true, true)
f6_no_ir := func(x, y, z, tau, s, kappa *matrix.FloatMatrix) (err error) {
// Solve
//
// [ 0 A' G' 0 ] [ ux ]
// [ -A 0 0 b ] [ uy ]
// [ -G 0 W'*W h ] [ W^{-1}*uz ]
// [ -c' -b' -h' k/t ] [ utau/dg ]
//
// [ bx ]
// [ by ]
// = [ bz - W'*(lmbda o\ bs) ]
// [ btau - bkappa/tau ]
//
// us = -lmbda o\ bs - uz
// ukappa = -bkappa/lmbdag - utau.
// First solve
//
// [ 0 A' G' ] [ ux ] [ bx ]
// [ A 0 0 ] [ uy ] = [ -by ]
// [ G 0 -W'*W ] [ W^{-1}*uz ] [ -bz + W'*(lmbda o\ bs) ]
err = nil
// y := -y = -by
blas.ScalFloat(y, -1.0)
// s := -lmbda o\ s = -lmbda o\ bs
err = sinv(s, lmbda, dims, 0)
blas.ScalFloat(s, -1.0)
// z := -(z + W'*s) = -bz + W'*(lambda o\ bs)
blas.Copy(s, ws3)
err = scale(ws3, W, true, false)
blas.AxpyFloat(ws3, z, 1.0)
blas.ScalFloat(z, -1.0)
err = f3(x, y, z)
// Combine with solution of
//
// [ 0 A' G' ] [ x1 ] [ c ]
// [-A 0 0 ] [ y1 ] = -dgi * [ b ]
// [-G 0 W'*W ] [ W^{-1}*dzl ] [ h ]
//
// to satisfy
//
// -c'*x - b'*y - h'*W^{-1}*z + dg*tau = btau - bkappa/tau. '
// , kappa[0] := -kappa[0] / lmbd[-1] = -bkappa / lmbdag
kap_ := kappa.Float()
tau_ := tau.Float()
kap_ = -kap_ / lmbda.GetIndex(-1)
// tau[0] = tau[0] + kappa[0] / dgi = btau[0] - bkappa / tau
tau_ = tau_ + kap_/dgi
//tau[0] = dgi * ( tau[0] + xdot(c,x) + ydot(b,y) +
// misc.sdot(th, z, dims) ) / (1.0 + misc.sdot(z1, z1, dims))
//tau_ = tau_ + blas.DotFloat(c, x) + blas.DotFloat(b, y) + sdot(th, z, dims, 0)
tau_ += blas.DotFloat(c, x)
tau_ += blas.DotFloat(b, y)
tau_ += sdot(th, z, dims, 0)
tau_ = dgi * tau_ / (1.0 + sdot(z1, z1, dims, 0))
tau.SetValue(tau_)
blas.AxpyFloat(x1, x, tau_)
blas.AxpyFloat(y1, y, tau_)
blas.AxpyFloat(z1, z, tau_)
blas.AxpyFloat(z, s, -1.0)
kap_ = kap_ - tau_
kappa.SetValue(kap_)
return
}
// f6(x, y, z, tau, s, kappa) solves the same system as f6_no_ir,
// but applies iterative refinement. Following variables part of f6-closure
// and ~ 12 is the limit. We wrap them to a structure.
if iter == 0 {
if refinement > 0 || solopts.Debug {
WS.wx = c.Copy()
WS.wy = b.Copy()
WS.wz = matrix.FloatZeros(cdim, 1)
WS.ws = matrix.FloatZeros(cdim, 1)
WS.wtau = matrix.FloatValue(0.0)
WS.wkappa = matrix.FloatValue(0.0)
}
if refinement > 0 {
WS.wx2 = c.Copy()
WS.wy2 = b.Copy()
WS.wz2 = matrix.FloatZeros(cdim, 1)
WS.ws2 = matrix.FloatZeros(cdim, 1)
WS.wtau2 = matrix.FloatValue(0.0)
WS.wkappa2 = matrix.FloatValue(0.0)
}
}
f6 := func(x, y, z, tau, s, kappa *matrix.FloatMatrix) error {
var err error = nil
if refinement > 0 || solopts.Debug {
blas.Copy(x, WS.wx)
blas.Copy(y, WS.wy)
blas.Copy(z, WS.wz)
blas.Copy(s, WS.ws)
WS.wtau.SetValue(tau.Float())
WS.wkappa.SetValue(kappa.Float())
}
err = f6_no_ir(x, y, z, tau, s, kappa)
for i := 0; i < refinement; i++ {
blas.Copy(WS.wx, WS.wx2)
blas.Copy(WS.wy, WS.wy2)
blas.Copy(WS.wz, WS.wz2)
blas.Copy(WS.ws, WS.ws2)
WS.wtau2.SetValue(WS.wtau.Float())
WS.wkappa2.SetValue(WS.wkappa.Float())
err = res(x, y, z, tau, s, kappa, WS.wx2, WS.wy2, WS.wz2, WS.wtau2, WS.ws2, WS.wkappa2, W, dg, lmbda)
err = f6_no_ir(WS.wx2, WS.wy2, WS.wz2, WS.wtau2, WS.ws2, WS.wkappa2)
blas.AxpyFloat(WS.wx2, x, 1.0)
blas.AxpyFloat(WS.wy2, y, 1.0)
blas.AxpyFloat(WS.wz2, z, 1.0)
blas.AxpyFloat(WS.ws2, s, 1.0)
tau.SetValue(tau.Float() + WS.wtau2.Float())
kappa.SetValue(kappa.Float() + WS.wkappa2.Float())
}
if solopts.Debug {
res(x, y, z, tau, s, kappa, WS.wx, WS.wy, WS.wz, WS.wtau, WS.ws, WS.wkappa, W, dg, lmbda)
fmt.Printf("KKT residuals\n")
}
return err
}
var nrm float64 = blas.Nrm2(lmbda).Float()
mu := math.Pow(nrm, 2.0) / (1.0 + float64(cdim_diag))
sigma := 0.0
var step, tt, tk float64
for i := 0; i < 2; i++ {
// Solve
//
// [ 0 ] [ 0 A' G' c ] [ dx ]
// [ 0 ] [ -A 0 0 b ] [ dy ]
// [ W'*ds ] - [ -G 0 0 h ] [ W^{-1}*dz ]
// [ dg*dkappa ] [ -c' -b' -h' 0 ] [ dtau/dg ]
//
// [ rx ]
// [ ry ]
// = - (1-sigma) [ rz ]
// [ rtau ]
//
// lmbda o (dz + ds) = -lmbda o lmbda + sigma*mu*e
// lmbdag * (dtau + dkappa) = - kappa * tau + sigma*mu
//
// ds = -lmbdasq if i is 0
// = -lmbdasq - dsa o dza + sigma*mu*e if i is 1
// dkappa = -lambdasq[-1] if i is 0
// = -lambdasq[-1] - dkappaa*dtaua + sigma*mu if i is 1.
ind := dims.Sum("l", "q")
ind2 := ind
blas.Copy(lmbdasq, ds, &la.IOpt{"n", ind})
blas.ScalFloat(ds, 0.0, &la.IOpt{"offset", ind})
for _, m := range dims.At("s") {
blas.Copy(lmbdasq, ds, &la.IOpt{"n", m}, &la.IOpt{"offsetx", ind2},
&la.IOpt{"offsety", ind}, &la.IOpt{"incy", m + 1})
ind += m * m
ind2 += m
}
// dkappa[0] = lmbdasq[-1]
dkappa.SetValue(lmbdasq.GetIndex(-1))
if i == 1 {
blas.AxpyFloat(ws3, ds, 1.0)
ind = dims.Sum("l", "q")
is := make([]int, 0)
is = append(is, matrix.MakeIndexSet(0, dims.At("l")[0], 1)...)
is = append(is, indq[:len(indq)-1]...)
for _, m := range dims.At("s") {
is = append(is, matrix.MakeIndexSet(ind, ind+m*m, m+1)...)
}
for _, k := range is {
ds.SetIndex(k, ds.GetIndex(k)-sigma*mu)
}
dk_ := dkappa.Float()
wk_ := wkappa3.Float()
dkappa.SetValue(dk_ + wk_ - sigma*mu)
}
// (dx, dy, dz, dtau) = (1-sigma)*(rx, ry, rz, rt)
blas.Copy(rx, dx)
blas.ScalFloat(dx, 1.0-sigma)
blas.Copy(ry, dy)
blas.ScalFloat(dy, 1.0-sigma)
blas.Copy(rz, dz)
blas.ScalFloat(dz, 1.0-sigma)
// dtau[0] = (1.0 - sigma) * rt
dtau.SetValue((1.0 - sigma) * rt)
err = f6(dx, dy, dz, dtau, ds, dkappa)
// Save ds o dz and dkappa * dtau for Mehrotra correction
if i == 0 {
blas.Copy(ds, ws3)
sprod(ws3, dz, dims, 0)
wkappa3.SetValue(dtau.Float() * dkappa.Float())
}
// Maximum step to boundary.
//
// If i is 1, also compute eigenvalue decomposition of the 's'
// blocks in ds, dz. The eigenvectors Qs, Qz are stored in
// dsk, dzk. The eigenvalues are stored in sigs, sigz.
var ts, tz float64
scale2(lmbda, ds, dims, 0, false)
scale2(lmbda, dz, dims, 0, false)
if i == 0 {
ts, _ = maxStep(ds, dims, 0, nil)
tz, _ = maxStep(dz, dims, 0, nil)
} else {
ts, _ = maxStep(ds, dims, 0, sigs)
tz, _ = maxStep(dz, dims, 0, sigz)
}
dt_ := dtau.Float()
dk_ := dkappa.Float()
tt = -dt_ / lmbda.GetIndex(-1)
tk = -dk_ / lmbda.GetIndex(-1)
t := maxvec([]float64{0.0, ts, tz, tt, tk})
if t == 0.0 {
step = 1.0
} else {
if i == 0 {
step = math.Min(1.0, 1.0/t)
} else {
step = math.Min(1.0, STEP/t)
}
}
if i == 0 {
// sigma = (1 - step)^3
sigma = (1.0 - step) * (1.0 - step) * (1.0 - step)
//sigma = math.Pow((1.0 - step), EXPON)
}
}
//fmt.Printf("** tau = %.17f, kappa = %.17f\n", tau.Float(), kappa.Float())
//fmt.Printf("** step = %.17f, sigma = %.17f\n", step, sigma)
// Update x, y
blas.AxpyFloat(dx, x, step)
blas.AxpyFloat(dy, y, step)
// Replace 'l' and 'q' blocks of ds and dz with the updated
// variables in the current scaling.
// Replace 's' blocks of ds and dz with the factors Ls, Lz in a
// factorization Ls*Ls', Lz*Lz' of the updated variables in the
// current scaling.
//
// ds := e + step*ds for 'l' and 'q' blocks.
// dz := e + step*dz for 'l' and 'q' blocks.
blas.ScalFloat(ds, step, &la.IOpt{"n", dims.Sum("l", "q")})
blas.ScalFloat(dz, step, &la.IOpt{"n", dims.Sum("l", "q")})
is := make([]int, 0)
is = append(is, matrix.MakeIndexSet(0, dims.At("l")[0], 1)...)
is = append(is, indq[:len(indq)-1]...)
for _, k := range is {
ds.SetIndex(k, 1.0+ds.GetIndex(k))
dz.SetIndex(k, 1.0+dz.GetIndex(k))
}
// ds := H(lambda)^{-1/2} * ds and dz := H(lambda)^{-1/2} * dz.
//
// This replaces the 'l' and 'q' components of ds and dz with the
// updated variables in the current scaling.
// The 's' components of ds and dz are replaced with
//
// diag(lmbda_k)^{1/2} * Qs * diag(lmbda_k)^{1/2}
// diag(lmbda_k)^{1/2} * Qz * diag(lmbda_k)^{1/2}
scale2(lmbda, ds, dims, 0, true)
scale2(lmbda, dz, dims, 0, true)
// sigs := ( e + step*sigs ) ./ lambda for 's' blocks.
// sigz := ( e + step*sigz ) ./ lambda for 's' blocks.
blas.ScalFloat(sigs, step)
blas.ScalFloat(sigz, step)
sigs.Add(1.0)
sigz.Add(1.0)
sdimsum := dims.Sum("s")
qdimsum := dims.Sum("l", "q")
blas.TbsvFloat(lmbda, sigs, &la.IOpt{"n", sdimsum}, &la.IOpt{"k", 0},
&la.IOpt{"lda", 1}, &la.IOpt{"offseta", qdimsum})
blas.TbsvFloat(lmbda, sigz, &la.IOpt{"n", sdimsum}, &la.IOpt{"k", 0},
&la.IOpt{"lda", 1}, &la.IOpt{"offseta", qdimsum})
ind2 := qdimsum
ind3 := 0
sdims := dims.At("s")
for k := 0; k < len(sdims); k++ {
m := sdims[k]
for i := 0; i < m; i++ {
a := math.Sqrt(sigs.GetIndex(ind3 + i))
blas.ScalFloat(ds, a, &la.IOpt{"offset", ind2 + m*i}, &la.IOpt{"n", m})
a = math.Sqrt(sigz.GetIndex(ind3 + i))
blas.ScalFloat(dz, a, &la.IOpt{"offset", ind2 + m*i}, &la.IOpt{"n", m})
}
ind2 += m * m
ind3 += m
}
err = updateScaling(W, lmbda, ds, dz)
// For kappa, tau block:
//
// dg := sqrt( (kappa + step*dkappa) / (tau + step*dtau) )
// = dg * sqrt( (1 - step*tk) / (1 - step*tt) )
//
// lmbda[-1] := sqrt((tau + step*dtau) * (kappa + step*dkappa))
// = lmbda[-1] * sqrt(( 1 - step*tt) * (1 - step*tk))
dg *= math.Sqrt(1.0-step*tk) / math.Sqrt(1.0-step*tt)
dgi = 1.0 / dg
a := math.Sqrt(1.0-step*tk) * math.Sqrt(1.0-step*tt)
lmbda.SetIndex(-1, a*lmbda.GetIndex(-1))
// Unscale s, z, tau, kappa (unscaled variables are used only to
// compute feasibility residuals).
ind := dims.Sum("l", "q")
ind2 = ind
blas.Copy(lmbda, s, &la.IOpt{"n", ind})
for _, m := range dims.At("s") {
blas.ScalFloat(s, 0.0, &la.IOpt{"offset", ind2})
blas.Copy(lmbda, s, &la.IOpt{"offsetx", ind}, &la.IOpt{"offsety", ind2},
&la.IOpt{"n", m}, &la.IOpt{"incy", m + 1})
ind += m
ind2 += m * m
}
scale(s, W, true, false)
ind = dims.Sum("l", "q")
ind2 = ind
blas.Copy(lmbda, z, &la.IOpt{"n", ind})
for _, m := range dims.At("s") {
blas.ScalFloat(z, 0.0, &la.IOpt{"offset", ind2})
blas.Copy(lmbda, z, &la.IOpt{"offsetx", ind}, &la.IOpt{"offsety", ind2},
&la.IOpt{"n", m}, &la.IOpt{"incy", m + 1})
ind += m
ind2 += m * m
}
scale(z, W, false, true)
kappa.SetValue(lmbda.GetIndex(-1) / dgi)
tau.SetValue(lmbda.GetIndex(-1) * dgi)
g := blas.Nrm2Float(lmbda, &la.IOpt{"n", lmbda.Rows() - 1}) / tau.Float()
gap = g * g
//fmt.Printf(" ** kappa=%.10f, tau=%.10f, gap=%.10f\n", kappa.Float(), tau.Float(), gap)
}
return
}
// 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
}
// 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
}
// Solves a pair of primal and dual convex quadratic cone programs
//
// minimize (1/2)*x'*P*x + q'*x
// subject to G*x + s = h
// A*x = b
// s >= 0
//
// maximize -(1/2)*(q + G'*z + A'*y)' * pinv(P) * (q + G'*z + A'*y)
// - h'*z - b'*y
// subject to q + G'*z + A'*y in range(P)
// z >= 0.
//
// The inequalities are with respect to a cone C defined as the Cartesian
// product of N + M + 1 cones:
//
// C = C_0 x C_1 x .... x C_N x C_{N+1} x ... x C_{N+M}.
//
// The first cone C_0 is the nonnegative orthant of dimension ml.
// The next N cones are 2nd order cones of dimension mq[0], ..., mq[N-1].
// The second order cone of dimension m is defined as
//
// { (u0, u1) in R x R^{m-1} | u0 >= ||u1||_2 }.
//
// The next M cones are positive semidefinite cones of order ms[0], ...,
// ms[M-1] >= 0.
//
func ConeQp(P, q, G, h, A, b *matrix.FloatMatrix, dims *DimensionSet, solopts *SolverOptions, initvals *FloatMatrixSet) (sol *Solution, err error) {
err = nil
EXPON := 3
STEP := 0.99
sol = &Solution{Unknown,
nil, nil, nil, nil, nil,
0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0}
var kktsolver func(*FloatMatrixSet) (kktFunc, error) = nil
var refinement int
var correction bool = true
feasTolerance := FEASTOL
absTolerance := ABSTOL
relTolerance := RELTOL
if solopts.FeasTol > 0.0 {
feasTolerance = solopts.FeasTol
}
if solopts.AbsTol > 0.0 {
absTolerance = solopts.AbsTol
}
if solopts.RelTol > 0.0 {
relTolerance = solopts.RelTol
}
solvername := solopts.KKTSolverName
if len(solvername) == 0 {
if dims != nil && (len(dims.At("q")) > 0 || len(dims.At("s")) > 0) {
solvername = "qr"
//kktsolver = solvers["qr"]
} else {
solvername = "chol2"
//kktsolver = solvers["chol2"]
}
}
if q == nil || q.Cols() != 1 {
err = errors.New("'q' must be non-nil matrix with one column")
return
}
if P == nil || P.Rows() != q.Rows() || P.Cols() != q.Rows() {
err = errors.New(fmt.Sprintf("'P' must be non-nil matrix of size (%d, %d)",
q.Rows(), q.Rows()))
return
}
fP := func(x, y *matrix.FloatMatrix, alpha, beta float64) error {
return blas.SymvFloat(P, x, y, alpha, beta)
}
if h == nil {
h = matrix.FloatZeros(0, 1)
}
if h.Cols() != 1 {
err = errors.New("'h' must be non-nil matrix with one column")
return
}
if dims == nil {
dims = DSetNew("l", "q", "s")
dims.Set("l", []int{h.Rows()})
}
err = checkConeQpDimensions(dims)
if err != nil {
return
}
cdim := dims.Sum("l", "q") + dims.SumSquared("s")
//cdim_pckd := dims.Sum("l", "q") + dims.SumPacked("s")
cdim_diag := dims.Sum("l", "q", "s")
if h.Rows() != cdim {
err = errors.New(fmt.Sprintf("'h' must be float matrix of size (%d,1)", cdim))
return
}
// Data for kth 'q' constraint are found in rows indq[k]:indq[k+1] of G.
indq := make([]int, 0)
indq = append(indq, dims.At("l")[0])
for _, k := range dims.At("q") {
indq = append(indq, indq[len(indq)-1]+k)
}
// Data for kth 's' constraint are found in rows inds[k]:inds[k+1] of G.
inds := make([]int, 0)
inds = append(inds, indq[len(indq)-1])
for _, k := range dims.At("s") {
inds = append(inds, inds[len(inds)-1]+k*k)
}
if G != nil && !G.SizeMatch(cdim, q.Rows()) {
estr := fmt.Sprintf("'G' must be of size (%d,%d)", cdim, q.Rows())
err = errors.New(estr)
return
}
fG := func(x, y *matrix.FloatMatrix, alpha, beta float64, opts ...la.Option) error {
return sgemv(G, x, y, alpha, beta, dims, opts...)
}
// Check A and set defaults if it is nil
if A == nil {
// zeros rows reduces Gemv to vector products
A = matrix.FloatZeros(0, q.Rows())
}
if A.Cols() != q.Rows() {
estr := fmt.Sprintf("'A' must have %d columns", q.Rows())
err = errors.New(estr)
return
}
fA := func(x, y *matrix.FloatMatrix, alpha, beta float64, opts ...la.Option) error {
return blas.GemvFloat(A, x, y, alpha, beta, opts...)
}
// Check b and set defaults if it is nil
if b == nil {
b = matrix.FloatZeros(0, 1)
}
if b.Cols() != 1 {
estr := fmt.Sprintf("'b' must be a matrix with 1 column")
err = errors.New(estr)
return
}
if b.Rows() != A.Rows() {
estr := fmt.Sprintf("'b' must have length %d", A.Rows())
err = errors.New(estr)
return
}
// kktsolver(W) returns a routine for solving 3x3 block KKT system
//
// [ 0 A' G'*W^{-1} ] [ ux ] [ bx ]
// [ A 0 0 ] [ uy ] = [ by ].
// [ G 0 -W' ] [ uz ] [ bz ]
var factor kktFactor
if kkt, ok := solvers[solvername]; ok {
if b.Rows() > q.Rows() {
err = errors.New("1: Rank(A) < p or Rank[G; A] < n")
return
}
if kkt == nil {
err = errors.New(fmt.Sprintf("solver '%s' not yet implemented", solvername))
return
}
// kkt function returns us problem spesific factor function.
factor, err = kkt(G, dims, A, 0)
if err != nil {
fmt.Printf("error on factoring: %s\n", err)
}
// solver is
kktsolver = func(W *FloatMatrixSet) (kktFunc, error) {
return factor(W, P, nil)
}
} else {
err = errors.New(fmt.Sprintf("solver '%s' not known", solvername))
return
}
ws3 := matrix.FloatZeros(cdim, 1)
wz3 := matrix.FloatZeros(cdim, 1)
//
res := func(ux, uy, uz, us, vx, vy, vz, vs *matrix.FloatMatrix, W *FloatMatrixSet, lmbda *matrix.FloatMatrix) (err error) {
// Evaluates residual in Newton equations:
//
// [ vx ] [ vx ] [ 0 ] [ P A' G' ] [ ux ]
// [ vy ] := [ vy ] - [ 0 ] - [ A 0 0 ] * [ uy ]
// [ vz ] [ vz ] [ W'*us ] [ G 0 0 ] [ W^{-1}*uz ]
//
// vs := vs - lmbda o (uz + us).
// vx := vx - P*ux - A'*uy - G'*W^{-1}*uz
fP(ux, vx, -1.0, 1.0)
fA(uy, vx, -1.0, 1.0, la.OptTrans)
blas.Copy(uz, wz3)
scale(wz3, W, true, false)
fG(wz3, vx, -1.0, 1.0, la.OptTrans)
// vy := vy - A*ux
fA(ux, vy, -1.0, 1.0)
// vz := vz - G*ux - W'*us
fG(ux, vz, -1.0, 1.0)
blas.Copy(us, ws3)
scale(ws3, W, true, false)
blas.AxpyFloat(ws3, vz, -1.0)
// vs := vs - lmbda o (uz + us)
blas.Copy(us, ws3)
blas.AxpyFloat(uz, ws3, 1.0)
sprod(ws3, lmbda, dims, 0, la.OptDiag)
blas.AxpyFloat(ws3, vs, -1.0)
return
}
resx0 := math.Max(1.0, math.Sqrt(blas.Dot(q, q).Float()))
resy0 := math.Max(1.0, math.Sqrt(blas.Dot(b, b).Float()))
resz0 := math.Max(1.0, snrm2(h, dims, 0))
//fmt.Printf("resx0: %.17f, resy0: %.17f, resz0: %.17f\n", resx0, resy0, resz0)
var x, y, z, s, dx, dy, ds, dz, rx, ry, rz *matrix.FloatMatrix
var lmbda, lmbdasq, sigs, sigz *matrix.FloatMatrix
var W *FloatMatrixSet
var f, f3 kktFunc
var resx, resy, resz, step, sigma, mu, eta float64
var gap, pcost, dcost, relgap, pres, dres, f0 float64
if cdim == 0 {
// Solve
//
// [ P A' ] [ x ] [ -q ]
// [ ] [ ] = [ ].
// [ A 0 ] [ y ] [ b ]
//
Wtmp := FloatSetNew("d", "di", "beta", "v", "r", "rti")
Wtmp.Set("d", matrix.FloatZeros(0, 1))
Wtmp.Set("di", matrix.FloatZeros(0, 1))
f3, err = kktsolver(Wtmp)
if err != nil {
s := fmt.Sprintf("kkt error: %s", err)
err = errors.New("2: Rank(A) < p or Rank(([P; A; G;]) < n : " + s)
return
}
x = q.Copy()
blas.ScalFloat(x, 0.0)
y = b.Copy()
f3(x, y, matrix.FloatZeros(0, 1))
// dres = || P*x + q + A'*y || / resx0
rx = q.Copy()
fP(x, rx, 1.0, 1.0)
pcost = 0.5 * (blas.DotFloat(x, rx) + blas.DotFloat(x, q))
fA(y, rx, 1.0, 1.0, la.OptTrans)
dres = math.Sqrt(blas.DotFloat(rx, rx) / resx0)
ry = b.Copy()
fA(x, ry, 1.0, -1.0)
pres = math.Sqrt(blas.DotFloat(ry, ry) / resy0)
relgap = 0.0
if pcost == 0.0 {
relgap = math.NaN()
}
sol.Result = FloatSetNew("x", "y", "s", "z")
sol.Result.Set("x", x)
sol.Result.Set("y", y)
sol.Result.Set("s", matrix.FloatZeros(0, 1))
sol.Result.Set("z", matrix.FloatZeros(0, 1))
sol.Status = Optimal
sol.Gap = 0.0
sol.RelativeGap = relgap
sol.PrimalObjective = pcost
sol.DualObjective = pcost
sol.PrimalInfeasibility = pres
sol.DualInfeasibility = dres
sol.PrimalSlack = 0.0
sol.DualSlack = 0.0
return
}
x = q.Copy()
y = b.Copy()
s = matrix.FloatZeros(cdim, 1)
z = matrix.FloatZeros(cdim, 1)
var ts, tz, nrms, nrmz float64
if initvals == nil {
// Factor
//
// [ 0 A' G' ]
// [ A 0 0 ].
// [ G 0 -I ]
//
W = FloatSetNew("d", "di", "v", "beta", "r", "rti")
W.Set("d", matrix.FloatOnes(dims.At("l")[0], 1))
W.Set("di", matrix.FloatOnes(dims.At("l")[0], 1))
W.Set("beta", matrix.FloatOnes(len(dims.At("q")), 1))
for _, n := range dims.At("q") {
vm := matrix.FloatZeros(n, 1)
vm.SetIndex(0, 1.0)
W.Append("v", vm)
}
for _, n := range dims.At("s") {
W.Append("r", matrix.FloatIdentity(n))
W.Append("rti", matrix.FloatIdentity(n))
}
f, err = kktsolver(W)
if err != nil {
s := fmt.Sprintf("kkt error: %s", err)
err = errors.New("3: Rank(A) < p or Rank([P; G; A]) < n : " + s)
return
}
// Solve
//
// [ P A' G' ] [ x ] [ -q ]
// [ A 0 0 ] * [ y ] = [ b ].
// [ G 0 -I ] [ z ] [ h ]
x = q.Copy()
blas.ScalFloat(x, -1.0)
y = b.Copy()
z = h.Copy()
err = f(x, y, z)
if err != nil {
s := fmt.Sprintf("kkt error: %s", err)
err = errors.New("4: Rank(A) < p or Rank([P; G; A]) < n : " + s)
return
}
s = z.Copy()
blas.ScalFloat(s, -1.0)
nrms = snrm2(s, dims, 0)
ts, _ = maxStep(s, dims, 0, nil)
if ts >= -1e-8*math.Max(nrms, 1.0) {
// a = 1.0 + ts
a := 1.0 + ts
is := make([]int, 0)
// indexes s[:dims['l']]
is = append(is, matrix.MakeIndexSet(0, dims.At("l")[0], 1)...)
// indexes s[indq[:-1]]
is = append(is, indq[:len(indq)-1]...)
ind := dims.Sum("l", "q")
// indexes s[ind:ind+m*m:m+1] (diagonal)
for _, m := range dims.At("s") {
is = append(is, matrix.MakeIndexSet(ind, ind+m*m, m+1)...)
ind += m * m
}
for _, k := range is {
s.SetIndex(k, a+s.GetIndex(k))
}
}
nrmz = snrm2(z, dims, 0)
tz, _ = maxStep(z, dims, 0, nil)
if tz >= -1e-8*math.Max(nrmz, 1.0) {
a := 1.0 + tz
is := make([]int, 0)
is = append(is, matrix.MakeIndexSet(0, dims.At("l")[0], 1)...)
is = append(is, indq[:len(indq)-1]...)
ind := dims.Sum("l", "q")
for _, m := range dims.At("s") {
is = append(is, matrix.MakeIndexSet(ind, ind+m*m, m+1)...)
ind += m * m
}
for _, k := range is {
z.SetIndex(k, a+z.GetIndex(k))
}
}
} else {
ix := initvals.At("x")[0]
if ix != nil {
blas.Copy(ix, x)
} else {
blas.ScalFloat(x, 0.0)
}
is := initvals.At("s")[0]
if is != nil {
blas.Copy(is, s)
} else {
iset := make([]int, 0)
iset = append(iset, matrix.MakeIndexSet(0, dims.At("l")[0], 1)...)
iset = append(iset, indq[:len(indq)-1]...)
ind := dims.Sum("l", "q")
for _, m := range dims.At("s") {
iset = append(iset, matrix.MakeIndexSet(ind, ind+m*m, m+1)...)
ind += m * m
}
for _, k := range iset {
s.SetIndex(k, 1.0)
}
}
iy := initvals.At("y")[0]
if iy != nil {
blas.Copy(iy, y)
} else {
blas.ScalFloat(y, 0.0)
}
iz := initvals.At("z")[0]
if iz != nil {
blas.Copy(iz, z)
} else {
iset := make([]int, 0)
iset = append(iset, matrix.MakeIndexSet(0, dims.At("l")[0], 1)...)
iset = append(iset, indq[:len(indq)-1]...)
ind := dims.Sum("l", "q")
for _, m := range dims.At("s") {
iset = append(iset, matrix.MakeIndexSet(ind, ind+m*m, m+1)...)
ind += m * m
}
for _, k := range iset {
z.SetIndex(k, 1.0)
}
}
}
rx = q.Copy()
ry = b.Copy()
rz = matrix.FloatZeros(cdim, 1)
dx = x.Copy()
dy = y.Copy()
dz = matrix.FloatZeros(cdim, 1)
ds = matrix.FloatZeros(cdim, 1)
lmbda = matrix.FloatZeros(cdim_diag, 1)
lmbdasq = matrix.FloatZeros(cdim_diag, 1)
sigs = matrix.FloatZeros(dims.Sum("s"), 1)
sigz = matrix.FloatZeros(dims.Sum("s"), 1)
var WS fClosure
gap = sdot(s, z, dims, 0)
for iter := 0; iter < solopts.MaxIter+1; iter++ {
// f0 = (1/2)*x'*P*x + q'*x + r and rx = P*x + q + A'*y + G'*z.
blas.Copy(q, rx)
fP(x, rx, 1.0, 1.0)
f0 = 0.5 * (blas.DotFloat(x, rx) + blas.DotFloat(x, q))
fA(y, rx, 1.0, 1.0, la.OptTrans)
fG(z, rx, 1.0, 1.0, la.OptTrans)
resx = math.Sqrt(blas.DotFloat(rx, rx))
// ry = A*x - b
blas.Copy(b, ry)
fA(x, ry, 1.0, -1.0)
resy = math.Sqrt(blas.DotFloat(ry, ry))
// rz = s + G*x - h
blas.Copy(s, rz)
blas.AxpyFloat(h, rz, -1.0)
fG(x, rz, 1.0, 1.0)
resz = snrm2(rz, dims, 0)
//fmt.Printf("resx: %.17f, resy: %.17f, resz: %.17f\n", resx, resy, resz)
// Statistics for stopping criteria.
// pcost = (1/2)*x'*P*x + q'*x
// dcost = (1/2)*x'*P*x + q'*x + y'*(A*x-b) + z'*(G*x-h) '
// = (1/2)*x'*P*x + q'*x + y'*(A*x-b) + z'*(G*x-h+s) - z'*s
// = (1/2)*x'*P*x + q'*x + y'*ry + z'*rz - gap
pcost = f0
dcost = f0 + blas.DotFloat(y, ry) + sdot(z, rz, dims, 0) - gap
if pcost < 0.0 {
relgap = gap / -pcost
} else if dcost > 0.0 {
relgap = gap / dcost
} else {
relgap = math.NaN()
}
pres = math.Max(resy/resy0, resz/resz0)
dres = resx / resx0
if solopts.ShowProgress {
if iter == 0 {
// show headers of something
fmt.Printf("% 10s% 12s% 10s% 8s% 7s\n",
"pcost", "dcost", "gap", "pres", "dres")
}
// show something
fmt.Printf("%2d: % 8.4e % 8.4e % 4.0e% 7.0e% 7.0e\n",
iter, pcost, dcost, gap, pres, dres)
}
if pres <= feasTolerance && dres <= feasTolerance &&
(gap <= absTolerance || (!math.IsNaN(relgap) && relgap <= relTolerance)) ||
iter == solopts.MaxIter {
ind := dims.Sum("l", "q")
for _, m := range dims.At("s") {
symm(s, m, ind)
symm(z, m, ind)
ind += m * m
}
ts, _ = maxStep(s, dims, 0, nil)
tz, _ = maxStep(z, dims, 0, nil)
if iter == solopts.MaxIter {
// terminated on max iterations.
sol.Status = Unknown
err = errors.New("Terminated (maximum iterations reached)")
fmt.Printf("Terminated (maximum iterations reached)\n")
return
}
// optimal solution found
//fmt.Print("Optimal solution.\n")
err = nil
sol.Result = FloatSetNew("x", "y", "s", "z")
sol.Result.Set("x", x)
sol.Result.Set("y", y)
sol.Result.Set("s", s)
sol.Result.Set("z", z)
sol.Status = Optimal
sol.Gap = gap
sol.RelativeGap = relgap
sol.PrimalObjective = pcost
sol.DualObjective = dcost
sol.PrimalInfeasibility = pres
sol.DualInfeasibility = dres
sol.PrimalSlack = -ts
sol.DualSlack = -tz
sol.PrimalResidualCert = math.NaN()
sol.DualResidualCert = math.NaN()
sol.Iterations = iter
return
}
// Compute initial scaling W and scaled iterates:
//
// W * z = W^{-T} * s = lambda.
//
// lmbdasq = lambda o lambda.
if iter == 0 {
W, err = computeScaling(s, z, lmbda, dims, 0)
}
ssqr(lmbdasq, lmbda, dims, 0)
f3, err = kktsolver(W)
if err != nil {
if iter == 0 {
s := fmt.Sprintf("kkt error: %s", err)
err = errors.New("5: Rank(A) < p or Rank([P; A; G]) < n : " + s)
return
} else {
ind := dims.Sum("l", "q")
for _, m := range dims.At("s") {
symm(s, m, ind)
symm(z, m, ind)
ind += m * m
}
ts, _ = maxStep(s, dims, 0, nil)
tz, _ = maxStep(z, dims, 0, nil)
// terminated (singular KKT matrix)
fmt.Printf("Terminated (singular KKT matrix).\n")
err = errors.New("Terminated (singular KKT matrix).")
sol.Result = FloatSetNew("x", "y", "s", "z")
sol.Result.Set("x", x)
sol.Result.Set("y", y)
sol.Result.Set("s", s)
sol.Result.Set("z", z)
sol.Status = Unknown
sol.RelativeGap = relgap
sol.PrimalObjective = pcost
sol.DualObjective = dcost
sol.PrimalInfeasibility = pres
sol.DualInfeasibility = dres
sol.PrimalSlack = -ts
sol.DualSlack = -tz
sol.Iterations = iter
return
}
}
// f4_no_ir(x, y, z, s) solves
//
// [ 0 ] [ P A' G' ] [ ux ] [ bx ]
// [ 0 ] + [ A 0 0 ] * [ uy ] = [ by ]
// [ W'*us ] [ G 0 0 ] [ W^{-1}*uz ] [ bz ]
//
// lmbda o (uz + us) = bs.
//
// On entry, x, y, z, s contain bx, by, bz, bs.
// On exit, they contain ux, uy, uz, us.
f4_no_ir := func(x, y, z, s *matrix.FloatMatrix) error {
// Solve
//
// [ P A' G' ] [ ux ] [ bx ]
// [ A 0 0 ] [ uy ] = [ by ]
// [ G 0 -W'*W ] [ W^{-1}*uz ] [ bz - W'*(lmbda o\ bs) ]
//
// us = lmbda o\ bs - uz.
//
// On entry, x, y, z, s contains bx, by, bz, bs.
// On exit they contain x, y, z, s.
// s := lmbda o\ s
// = lmbda o\ bs
sinv(s, lmbda, dims, 0)
// z := z - W'*s
// = bz - W'*(lambda o\ bs)
blas.Copy(s, ws3)
scale(ws3, W, true, false)
blas.AxpyFloat(ws3, z, -1.0)
err := f3(x, y, z)
if err != nil {
return err
}
// s := s - z
// = lambda o\ bs - uz.
blas.AxpyFloat(z, s, -1.0)
return nil
}
if iter == 0 {
if refinement > 0 || solopts.Debug {
WS.wx = q.Copy()
WS.wy = y.Copy()
WS.ws = matrix.FloatZeros(cdim, 1)
WS.wz = matrix.FloatZeros(cdim, 1)
}
if refinement > 0 {
WS.wx2 = q.Copy()
WS.wy2 = y.Copy()
WS.ws2 = matrix.FloatZeros(cdim, 1)
WS.wz2 = matrix.FloatZeros(cdim, 1)
}
}
f4 := func(x, y, z, s *matrix.FloatMatrix) (err error) {
err = nil
if refinement > 0 || solopts.Debug {
blas.Copy(x, WS.wx)
blas.Copy(y, WS.wy)
blas.Copy(z, WS.wz)
blas.Copy(s, WS.ws)
}
err = f4_no_ir(x, y, z, s)
for i := 0; i < refinement; i++ {
blas.Copy(WS.wx, WS.wx2)
blas.Copy(WS.wy, WS.wy2)
blas.Copy(WS.wz, WS.wz2)
blas.Copy(WS.ws, WS.ws2)
res(x, y, z, s, WS.wx2, WS.wy2, WS.wz2, WS.ws2, W, lmbda)
f4_no_ir(WS.wx2, WS.wy2, WS.wz2, WS.ws2)
blas.AxpyFloat(WS.wx2, x, 1.0)
blas.AxpyFloat(WS.wy2, y, 1.0)
blas.AxpyFloat(WS.wz2, z, 1.0)
blas.AxpyFloat(WS.ws2, s, 1.0)
}
return
}
//var mu, sigma, eta float64
mu = gap / float64(dims.Sum("l", "s")+len(dims.At("q")))
sigma, eta = 0.0, 0.0
for i := 0; i < 2; i++ {
// Solve
//
// [ 0 ] [ P A' G' ] [ dx ]
// [ 0 ] + [ A 0 0 ] * [ dy ] = -(1 - eta) * r
// [ W'*ds ] [ G 0 0 ] [ W^{-1}*dz ]
//
// lmbda o (dz + ds) = -lmbda o lmbda + sigma*mu*e (i=0)
// lmbda o (dz + ds) = -lmbda o lmbda - dsa o dza
// + sigma*mu*e (i=1) where dsa, dza
// are the solution for i=0.
// ds = -lmbdasq + sigma * mu * e (if i is 0)
// = -lmbdasq - dsa o dza + sigma * mu * e (if i is 1),
// where ds, dz are solution for i is 0.
blas.ScalFloat(ds, 0.0)
if correction && i == 1 {
blas.AxpyFloat(ws3, ds, -1.0)
}
blas.AxpyFloat(lmbdasq, ds, -1.0, &la.IOpt{"n", dims.Sum("l", "q")})
ind := dims.At("l")[0]
ds.Add(sigma*mu, matrix.MakeIndexSet(0, ind, 1)...)
for _, m := range dims.At("q") {
ds.SetIndex(ind, sigma*mu+ds.GetIndex(ind))
ind += m
}
ind2 := ind
for _, m := range dims.At("s") {
blas.AxpyFloat(lmbdasq, ds, -1.0, &la.IOpt{"n", m}, &la.IOpt{"incy", m + 1},
&la.IOpt{"offsetx", ind2}, &la.IOpt{"offsety", ind})
ds.Add(sigma*mu, matrix.MakeIndexSet(ind, ind+m*m, m+1)...)
ind += m * m
ind2 += m
}
// (dx, dy, dz) := -(1 - eta) * (rx, ry, rz)
blas.ScalFloat(dx, 0.0)
blas.AxpyFloat(rx, dx, -1.0+eta)
blas.ScalFloat(dy, 0.0)
blas.AxpyFloat(ry, dy, -1.0+eta)
blas.ScalFloat(dz, 0.0)
blas.AxpyFloat(rz, dz, -1.0+eta)
//fmt.Printf("== Calling f4 %d\n", i)
//fmt.Printf("dx=\n%v\n", dx.ToString("%.17f"))
//fmt.Printf("ds=\n%v\n", ds.ToString("%.17f"))
//fmt.Printf("dz=\n%v\n", dz.ToString("%.17f"))
//fmt.Printf("== Entering f4 %d\n", i)
err = f4(dx, dy, dz, ds)
if err != nil {
if iter == 0 {
s := fmt.Sprintf("kkt error: %s", err)
err = errors.New("6: Rank(A) < p or Rank([P; A; G]) < n : " + s)
return
} else {
ind = dims.Sum("l", "q")
for _, m := range dims.At("s") {
symm(s, m, ind)
symm(z, m, ind)
ind += m * m
}
ts, _ = maxStep(s, dims, 0, nil)
tz, _ = maxStep(z, dims, 0, nil)
return
}
}
dsdz := sdot(ds, dz, dims, 0)
if correction && i == 0 {
blas.Copy(ds, ws3)
sprod(ws3, dz, dims, 0)
}
// Maximum step to boundary.
//
// If i is 1, also compute eigenvalue decomposition of the 's'
// blocks in ds, dz. The eigenvectors Qs, Qz are stored in
// dsk, dzk. The eigenvalues are stored in sigs, sigz.
scale2(lmbda, ds, dims, 0, false)
scale2(lmbda, dz, dims, 0, false)
if i == 0 {
ts, _ = maxStep(ds, dims, 0, nil)
tz, _ = maxStep(dz, dims, 0, nil)
} else {
ts, _ = maxStep(ds, dims, 0, sigs)
tz, _ = maxStep(dz, dims, 0, sigz)
}
t := maxvec([]float64{0.0, ts, tz})
//fmt.Printf("== t=%.17f from %v\n", t, []float64{ts, tz})
if t == 0.0 {
step = 1.0
} else {
if i == 0 {
step = math.Min(1.0, 1.0/t)
} else {
step = math.Min(1.0, STEP/t)
}
}
if i == 0 {
m := math.Max(0.0, 1.0-step+dsdz/gap*(step*step))
sigma = math.Pow(math.Min(1.0, m), float64(EXPON))
eta = 0.0
}
//fmt.Printf("== step=%.17f sigma=%.17f dsdz=%.17f\n", step, sigma, dsdz)
}
blas.AxpyFloat(dx, x, step)
blas.AxpyFloat(dy, y, step)
//fmt.Printf("x=\n%v\n", x.ConvertToString())
//fmt.Printf("y=\n%v\n", y.ConvertToString())
//fmt.Printf("ds=\n%v\n", ds.ConvertToString())
//fmt.Printf("dz=\n%v\n", dz.ConvertToString())
// We will now replace the 'l' and 'q' blocks of ds and dz with
// the updated iterates in the current scaling.
// We also replace the 's' blocks of ds and dz with the factors
// Ls, Lz in a factorization Ls*Ls', Lz*Lz' of the updated variables
// in the current scaling.
// ds := e + step*ds for nonlinear, 'l' and 'q' blocks.
// dz := e + step*dz for nonlinear, 'l' and 'q' blocks.
blas.ScalFloat(ds, step, &la.IOpt{"n", dims.Sum("l", "q")})
blas.ScalFloat(dz, step, &la.IOpt{"n", dims.Sum("l", "q")})
ind := dims.At("l")[0]
is := matrix.MakeIndexSet(0, ind, 1)
ds.Add(1.0, is...)
dz.Add(1.0, is...)
for _, m := range dims.At("q") {
ds.SetIndex(ind, 1.0+ds.GetIndex(ind))
dz.SetIndex(ind, 1.0+dz.GetIndex(ind))
ind += m
}
// ds := H(lambda)^{-1/2} * ds and dz := H(lambda)^{-1/2} * dz.
//
// This replaces the 'l' and 'q' components of ds and dz with the
// updated variables in the current scaling.
// The 's' components of ds and dz are replaced with
//
// diag(lmbda_k)^{1/2} * Qs * diag(lmbda_k)^{1/2}
// diag(lmbda_k)^{1/2} * Qz * diag(lmbda_k)^{1/2}
scale2(lmbda, ds, dims, 0, true)
scale2(lmbda, dz, dims, 0, true)
// sigs := ( e + step*sigs ) ./ lambda for 's' blocks.
// sigz := ( e + step*sigz ) ./ lambda for 's' blocks.
blas.ScalFloat(sigs, step)
blas.ScalFloat(sigz, step)
sigs.Add(1.0)
sigz.Add(1.0)
sdimsum := dims.Sum("s")
qdimsum := dims.Sum("l", "q")
blas.TbsvFloat(lmbda, sigs, &la.IOpt{"n", sdimsum}, &la.IOpt{"k", 0},
&la.IOpt{"lda", 1}, &la.IOpt{"offseta", qdimsum})
blas.TbsvFloat(lmbda, sigz, &la.IOpt{"n", sdimsum}, &la.IOpt{"k", 0},
&la.IOpt{"lda", 1}, &la.IOpt{"offseta", qdimsum})
ind2 := qdimsum
ind3 := 0
sdims := dims.At("s")
for k := 0; k < len(sdims); k++ {
m := sdims[k]
for i := 0; i < m; i++ {
a := math.Sqrt(sigs.GetIndex(ind3 + i))
blas.ScalFloat(ds, a, &la.IOpt{"offset", ind2 + m*i}, &la.IOpt{"n", m})
a = math.Sqrt(sigz.GetIndex(ind3 + i))
blas.ScalFloat(dz, a, &la.IOpt{"offset", ind2 + m*i}, &la.IOpt{"n", m})
}
ind2 += m * m
ind3 += m
}
err = updateScaling(W, lmbda, ds, dz)
// Unscale s, z, tau, kappa (unscaled variables are used only to
// compute feasibility residuals).
ind = dims.Sum("l", "q")
ind2 = ind
blas.Copy(lmbda, s, &la.IOpt{"n", ind})
for _, m := range dims.At("s") {
blas.ScalFloat(s, 0.0, &la.IOpt{"offset", ind2})
blas.Copy(lmbda, s, &la.IOpt{"offsetx", ind}, &la.IOpt{"offsety", ind2},
&la.IOpt{"n", m}, &la.IOpt{"incy", m + 1})
ind += m
ind2 += m * m
}
scale(s, W, true, false)
ind = dims.Sum("l", "q")
ind2 = ind
blas.Copy(lmbda, z, &la.IOpt{"n", ind})
for _, m := range dims.At("s") {
blas.ScalFloat(z, 0.0, &la.IOpt{"offset", ind2})
blas.Copy(lmbda, z, &la.IOpt{"offsetx", ind}, &la.IOpt{"offsety", ind2},
&la.IOpt{"n", m}, &la.IOpt{"incy", m + 1})
ind += m
ind2 += m * m
}
scale(z, W, false, true)
gap = blas.DotFloat(lmbda, lmbda)
//fmt.Printf("== gap = %.17f\n", gap)
}
return
}