func main() {
flag.Parse()
if len(spPath) > 0 {
checkpnt.Reset(spPath)
checkpnt.Activate()
checkpnt.Verbose(spVerbose)
checkpnt.Format("%.17f")
}
adata := [][]float64{
[]float64{0.3, -0.4, -0.2, -0.4, 1.3},
[]float64{0.6, 1.2, -1.7, 0.3, -0.3},
[]float64{-0.3, 0.0, 0.6, -1.2, -2.0}}
A := matrix.FloatMatrixFromTable(adata, matrix.ColumnOrder)
b := matrix.FloatVector([]float64{1.5, 0.0, -1.2, -0.7, 0.0})
_, n := A.Size()
N := n + 1 + n
h := matrix.FloatZeros(N, 1)
h.SetIndex(n, 1.0)
I0 := matrix.FloatDiagonal(n, -1.0)
I1 := matrix.FloatIdentity(n)
G, _ := matrix.FloatMatrixStacked(matrix.StackDown, I0, matrix.FloatZeros(1, n), I1)
At := A.Transpose()
P := At.Times(A)
q := At.Times(b).Scale(-1.0)
dims := sets.NewDimensionSet("l", "q", "s")
dims.Set("l", []int{n})
dims.Set("q", []int{n + 1})
var solopts cvx.SolverOptions
solopts.MaxIter = 20
solopts.ShowProgress = true
if maxIter > 0 {
solopts.MaxIter = maxIter
}
if len(solver) > 0 {
solopts.KKTSolverName = solver
}
sol, err := cvx.ConeQp(P, q, G, h, nil, nil, dims, &solopts, nil)
if err == nil {
x := sol.Result.At("x")[0]
s := sol.Result.At("s")[0]
z := sol.Result.At("z")[0]
fmt.Printf("Optimal\n")
fmt.Printf("x=\n%v\n", x.ToString("%.9f"))
fmt.Printf("s=\n%v\n", s.ToString("%.9f"))
fmt.Printf("z=\n%v\n", z.ToString("%.9f"))
check(x, s, z)
}
}
func main() {
m := 6
Vdata := [][]float64{
[]float64{1.0, -1.0, -2.0, -2.0, 0.0, 1.5, 1.0},
[]float64{1.0, 2.0, 1.0, -1.0, -2.0, -1.0, 1.0}}
V := matrix.FloatMatrixFromTable(Vdata, matrix.RowOrder)
// V[1, :m] - V[1,1:]
a0 := matrix.Minus(V.GetSubMatrix(1, 0, 1, m), V.GetSubMatrix(1, 1, 1))
// V[0, :m] - V[0,1:]
a1 := matrix.Minus(V.GetSubMatrix(0, 0, 1, m), V.GetSubMatrix(0, 1, 1))
A0, _ := matrix.FloatMatrixStacked(matrix.StackDown, a0.Scale(-1.0), a1)
A0 = A0.Transpose()
b0 := matrix.Mul(A0, V.GetSubMatrix(0, 0, 2, m).Transpose())
b0 = matrix.Times(b0, matrix.FloatWithValue(2, 1, 1.0))
A := make([]*matrix.FloatMatrix, 0)
b := make([]*matrix.FloatMatrix, 0)
A = append(A, A0)
b = append(b, b0)
// List of symbols
C := make([]*matrix.FloatMatrix, 0)
C = append(C, matrix.FloatZeros(2, 1))
var row *matrix.FloatMatrix = nil
for k := 0; k < m; k++ {
row = A0.GetRow(k, row)
nrm := blas.Nrm2Float(row)
row.Scale(2.0 * b0.GetIndex(k) / (nrm * nrm))
C = append(C, row.Transpose())
}
// Voronoi set around C[1]
A1 := matrix.FloatZeros(3, 2)
A1.SetSubMatrix(0, 0, A0.GetSubMatrix(0, 0, 1).Scale(-1.0))
A1.SetSubMatrix(1, 0, matrix.Minus(C[m], C[1]).Transpose())
A1.SetSubMatrix(2, 0, matrix.Minus(C[2], C[1]).Transpose())
b1 := matrix.FloatZeros(3, 1)
b1.SetIndex(0, -b0.GetIndex(0))
v := matrix.Times(A1.GetRow(1, nil), matrix.Plus(C[m], C[1])).Float() * 0.5
b1.SetIndex(1, v)
v = matrix.Times(A1.GetRow(2, nil), matrix.Plus(C[2], C[1])).Float() * 0.5
b1.SetIndex(2, v)
A = append(A, A1)
b = append(b, b1)
// Voronoi set around C[2] ... C[5]
for k := 2; k < 6; k++ {
A1 = matrix.FloatZeros(3, 2)
A1.SetSubMatrix(0, 0, A0.GetSubMatrix(k-1, 0, 1).Scale(-1.0))
A1.SetSubMatrix(1, 0, matrix.Minus(C[k-1], C[k]).Transpose())
A1.SetSubMatrix(2, 0, matrix.Minus(C[k+1], C[k]).Transpose())
b1 = matrix.FloatZeros(3, 1)
b1.SetIndex(0, -b0.GetIndex(k-1))
v := matrix.Times(A1.GetRow(1, nil), matrix.Plus(C[k-1], C[k])).Float() * 0.5
b1.SetIndex(1, v)
v = matrix.Times(A1.GetRow(2, nil), matrix.Plus(C[k+1], C[k])).Float() * 0.5
b1.SetIndex(2, v)
A = append(A, A1)
b = append(b, b1)
}
// Voronoi set around C[6]
A1 = matrix.FloatZeros(3, 2)
A1.SetSubMatrix(0, 0, A0.GetSubMatrix(5, 0, 1).Scale(-1.0))
A1.SetSubMatrix(1, 0, matrix.Minus(C[1], C[6]).Transpose())
A1.SetSubMatrix(2, 0, matrix.Minus(C[5], C[6]).Transpose())
b1 = matrix.FloatZeros(3, 1)
b1.SetIndex(0, -b0.GetIndex(5))
v = matrix.Times(A1.GetRow(1, nil), matrix.Plus(C[1], C[6])).Float() * 0.5
b1.SetIndex(1, v)
v = matrix.Times(A1.GetRow(2, nil), matrix.Plus(C[5], C[6])).Float() * 0.5
b1.SetIndex(2, v)
A = append(A, A1)
b = append(b, b1)
P := matrix.FloatIdentity(2)
q := matrix.FloatZeros(2, 1)
solopts := &cvx.SolverOptions{ShowProgress: false, MaxIter: 30}
ovals := make([]float64, 0)
for k := 1; k < 7; k++ {
sol, err := cvx.Qp(P, q, A[k], b[k], nil, nil, solopts, nil)
_ = err
x := sol.Result.At("x")[0]
ovals = append(ovals, math.Pow(blas.Nrm2Float(x), 2.0))
}
optvals := matrix.FloatVector(ovals)
//fmt.Printf("optvals=\n%v\n", optvals)
rangeFunc := func(n int) []float64 {
r := make([]float64, 0)
for i := 0; i < n; i++ {
r = append(r, float64(i))
}
return r
}
nopts := 200
sigmas := matrix.FloatVector(rangeFunc(nopts))
sigmas.Scale((0.5 - 0.2) / float64(nopts)).Add(0.2)
bndsVal := func(sigma float64) float64 {
// 1.0 - sum(exp( -optvals/(2*sigma**2)))
return 1.0 - matrix.Exp(matrix.Scale(optvals, -1.0/(2*sigma*sigma))).Sum()
}
bnds := matrix.FloatZeros(sigmas.NumElements(), 1)
for j, v := range sigmas.FloatArray() {
bnds.SetIndex(j, bndsVal(v))
}
plotData("plot.png", sigmas.FloatArray(), bnds.FloatArray())
}
func TestConeQp(t *testing.T) {
adata := [][]float64{
[]float64{0.3, -0.4, -0.2, -0.4, 1.3},
[]float64{0.6, 1.2, -1.7, 0.3, -0.3},
[]float64{-0.3, 0.0, 0.6, -1.2, -2.0}}
// reference values from cvxopt coneqp.py
xref := []float64{0.72558318685981904, 0.61806264311119252, 0.30253527966423444}
sref := []float64{
0.72558318685981904, 0.61806264311119263,
0.30253527966423449, 1.00000000000041678,
-0.72558318686012169, -0.61806264311145032,
-0.30253527966436067}
zref := []float64{
0.00000003332583626, 0.00000005116586239,
0.00000009993673262, 0.56869648433154019,
0.41264857754144563, 0.35149286573190930,
0.17201618570052318}
A := matrix.FloatMatrixFromTable(adata, matrix.ColumnOrder)
b := matrix.FloatVector([]float64{1.5, 0.0, -1.2, -0.7, 0.0})
_, n := A.Size()
N := n + 1 + n
h := matrix.FloatZeros(N, 1)
h.SetIndex(n, 1.0)
I0 := matrix.FloatDiagonal(n, -1.0)
I1 := matrix.FloatIdentity(n)
G, _ := matrix.FloatMatrixStacked(matrix.StackDown, I0, matrix.FloatZeros(1, n), I1)
At := A.Transpose()
P := matrix.Times(At, A)
q := matrix.Times(At, b).Scale(-1.0)
dims := sets.DSetNew("l", "q", "s")
dims.Set("l", []int{n})
dims.Set("q", []int{n + 1})
var solopts SolverOptions
solopts.MaxIter = 10
solopts.ShowProgress = false
sol, err := ConeQp(P, q, G, h, nil, nil, dims, &solopts, nil)
if err == nil {
fail := false
x := sol.Result.At("x")[0]
s := sol.Result.At("s")[0]
z := sol.Result.At("z")[0]
t.Logf("Optimal\n")
t.Logf("x=\n%v\n", x.ToString("%.9f"))
t.Logf("s=\n%v\n", s.ToString("%.9f"))
t.Logf("z=\n%v\n", z.ToString("%.9f"))
xe, _ := nrmError(matrix.FloatVector(xref), x)
if xe > TOL {
t.Logf("x differs [%.3e] from exepted too much.", xe)
fail = true
}
se, _ := nrmError(matrix.FloatVector(sref), s)
if se > TOL {
t.Logf("s differs [%.3e] from exepted too much.", se)
fail = true
}
ze, _ := nrmError(matrix.FloatVector(zref), z)
if ze > TOL {
t.Logf("z differs [%.3e] from exepted too much.", ze)
fail = true
}
if fail {
t.Fail()
}
}
}
func mcsdp(w *matrix.FloatMatrix) (*Solution, error) {
//
// Returns solution x, z to
//
// (primal) minimize sum(x)
// subject to w + diag(x) >= 0
//
// (dual) maximize -tr(w*z)
// subject to diag(z) = 1
// z >= 0.
//
n := w.Rows()
G := &matrixFs{n}
cngrnc := func(r, x *matrix.FloatMatrix, alpha float64) (err error) {
// Congruence transformation
//
// x := alpha * r'*x*r.
//
// r and x are square matrices.
//
err = nil
// tx = matrix(x, (n,n)) is copying and reshaping
// scale diagonal of x by 1/2, (x is (n,n))
tx := x.Copy()
matrix.Reshape(tx, n, n)
tx.Diag().Scale(0.5)
// a := tril(x)*r
// (python: a = +r is really making a copy of r)
a := r.Copy()
err = blas.TrmmFloat(tx, a, 1.0, linalg.OptLeft)
// x := alpha*(a*r' + r*a')
err = blas.Syr2kFloat(r, a, tx, alpha, 0.0, linalg.OptTrans)
// x[:] = tx[:]
tx.CopyTo(x)
return
}
Fkkt := func(W *sets.FloatMatrixSet) (KKTFunc, error) {
// Solve
// -diag(z) = bx
// -diag(x) - inv(rti*rti') * z * inv(rti*rti') = bs
//
// On entry, x and z contain bx and bs.
// On exit, they contain the solution, with z scaled
// (inv(rti)'*z*inv(rti) is returned instead of z).
//
// We first solve
//
// ((rti*rti') .* (rti*rti')) * x = bx - diag(t*bs*t)
//
// and take z = -rti' * (diag(x) + bs) * rti.
var err error = nil
rti := W.At("rti")[0]
// t = rti*rti' as a nonsymmetric matrix.
t := matrix.FloatZeros(n, n)
err = blas.GemmFloat(rti, rti, t, 1.0, 0.0, linalg.OptTransB)
if err != nil {
return nil, err
}
// Cholesky factorization of tsq = t.*t.
tsq := matrix.Mul(t, t)
err = lapack.Potrf(tsq)
if err != nil {
return nil, err
}
f := func(x, y, z *matrix.FloatMatrix) (err error) {
// tbst := t * zs * t = t * bs * t
tbst := z.Copy()
matrix.Reshape(tbst, n, n)
cngrnc(t, tbst, 1.0)
// x := x - diag(tbst) = bx - diag(rti*rti' * bs * rti*rti')
diag := tbst.Diag().Transpose()
x.Minus(diag)
// x := (t.*t)^{-1} * x = (t.*t)^{-1} * (bx - diag(t*bs*t))
err = lapack.Potrs(tsq, x)
// z := z + diag(x) = bs + diag(x)
// z, x are really column vectors here
z.AddIndexes(matrix.MakeIndexSet(0, n*n, n+1), x.FloatArray())
// z := -rti' * z * rti = -rti' * (diag(x) + bs) * rti
cngrnc(rti, z, -1.0)
return nil
}
return f, nil
}
c := matrix.FloatWithValue(n, 1, 1.0)
// initial feasible x: x = 1.0 - min(lmbda(w))
lmbda := matrix.FloatZeros(n, 1)
wp := w.Copy()
lapack.Syevx(wp, lmbda, nil, 0.0, nil, []int{1, 1}, linalg.OptRangeInt)
x0 := matrix.FloatZeros(n, 1).Add(-lmbda.GetAt(0, 0) + 1.0)
s0 := w.Copy()
s0.Diag().Plus(x0.Transpose())
matrix.Reshape(s0, n*n, 1)
// initial feasible z is identity
z0 := matrix.FloatIdentity(n)
matrix.Reshape(z0, n*n, 1)
dims := sets.DSetNew("l", "q", "s")
dims.Set("s", []int{n})
primalstart := sets.FloatSetNew("x", "s")
dualstart := sets.FloatSetNew("z")
primalstart.Set("x", x0)
primalstart.Set("s", s0)
dualstart.Set("z", z0)
var solopts SolverOptions
solopts.MaxIter = 30
solopts.ShowProgress = false
h := w.Copy()
matrix.Reshape(h, h.NumElements(), 1)
return ConeLpCustomMatrix(c, G, h, nil, nil, dims, Fkkt, &solopts, primalstart, dualstart)
}
func conelp_solver(c MatrixVariable, G MatrixVarG, h *matrix.FloatMatrix,
A MatrixVarA, b MatrixVariable, dims *sets.DimensionSet, kktsolver KKTConeSolverVar,
solopts *SolverOptions, primalstart, dualstart *sets.FloatMatrixSet) (sol *Solution, err error) {
err = nil
const EXPON = 3
const STEP = 0.99
sol = &Solution{Unknown,
nil,
0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0}
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
maxIter := MAXITERS
if solopts.FeasTol > 0.0 {
feasTolerance = solopts.FeasTol
}
if solopts.AbsTol > 0.0 {
absTolerance = solopts.AbsTol
}
if solopts.RelTol > 0.0 {
relTolerance = solopts.RelTol
}
if solopts.MaxIter > 0 {
maxIter = solopts.MaxIter
}
if err = checkConeLpDimensions(dims); 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)
}
Gf := func(x, y MatrixVariable, alpha, beta float64, trans la.Option) error {
return G.Gf(x, y, alpha, beta, trans)
}
Af := func(x, y MatrixVariable, alpha, beta float64, trans la.Option) error {
return A.Af(x, y, alpha, beta, trans)
}
// 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 ]
if kktsolver == nil {
err = errors.New("nil kktsolver not allowed.")
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)
checkpnt.AddMatrixVar("ws3", ws3)
checkpnt.AddMatrixVar("wz3", wz3)
//
res := func(ux, uy MatrixVariable, uz, utau, us, ukappa *matrix.FloatMatrix,
vx, vy MatrixVariable, vz, vtau, vs, vkappa *matrix.FloatMatrix,
W *sets.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(&matrixVar{wz3}, vx, -1.0, 1.0, la.OptTrans)
//blas.AxpyFloat(c, vx, -utau.Float()/dg)
c.Axpy(vx, -utau.Float()/dg)
// vy := vy + A*ux - b*utau/dg
Af(ux, vy, 1.0, 1.0, la.OptNoTrans)
//blas.AxpyFloat(b, vy, -utau.Float()/dg)
b.Axpy(vy, -utau.Float()/dg)
// vz := vz + G*ux - h*utau/dg + W'*us
Gf(ux, &matrixVar{vz}, 1.0, 1.0, la.OptNoTrans)
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() + c.Dot(ux) +
b.Dot(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(-1)
var vkplus float64 = lscale * (utau.Float() + ukappa.Float())
vkappa.SetValue(vkappa.Float() + vkplus)
return
}
resx0 := math.Max(1.0, math.Sqrt(c.Dot(c)))
resy0 := math.Max(1.0, math.Sqrt(b.Dot(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)
x.Scal(0.0)
y := b.Copy()
//blas.ScalFloat(y, 0.0)
y.Scal(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)
checkpnt.AddVerifiable("x", x)
checkpnt.AddMatrixVar("s", s)
checkpnt.AddMatrixVar("z", z)
checkpnt.AddVerifiable("dx", dx)
checkpnt.AddMatrixVar("ds", ds)
checkpnt.AddMatrixVar("dz", dz)
checkpnt.Check("00init", 1)
var W *sets.FloatMatrixSet
var f KKTFuncVar
if primalstart == nil || dualstart == nil {
// Factor
//
// [ 0 A' G' ]
// [ A 0 0 ].
// [ G 0 -I ]
//
W = sets.NewFloatSet("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)
if err != nil {
fmt.Printf("kktsolver error: %s\n", err)
return
}
checkpnt.AddScaleVar(W)
}
checkpnt.Check("05init", 5)
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(b, dy)
checkpnt.MinorPush(5)
x.Scal(0.0)
mCopy(b, 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.ToString("%.5f"))
checkpnt.MinorPop()
} else {
mCopy(&matrixVar{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
}
//fmt.Printf("initial ts=%.5f\n", ts)
checkpnt.Check("10init", 10)
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)
checkpnt.MinorPush(10)
mCopy(c, dx)
dx.Scal(-1.0)
y.Scal(0.0)
blas.ScalFloat(z, 0.0)
err = f(dx, y, z)
if err != nil {
fmt.Printf("f(dx,y,z): %s\n", err)
return
}
//fmt.Printf("initial z=\n%v\n", z.ToString("%.5f"))
checkpnt.MinorPop()
} else {
if len(dualstart.At("y")) > 0 {
mCopy(&matrixVar{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
}
//fmt.Printf("initial tz=%.5f\n", tz)
nrms := snrm2(s, dims, 0)
nrmz := snrm2(z, dims, 0)
gap := 0.0
pcost := 0.0
dcost := 0.0
relgap := 0.0
checkpnt.Check("20init", 0)
if primalstart == nil && dualstart == nil {
gap = sdot(s, z, dims, 0)
pcost = c.Dot(x)
dcost = -b.Dot(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(&matrixVar{z}, rx, 1.0, 1.0, la.OptTrans)
resx := math.Sqrt(rx.Dot(rx))
// ry = b - A*x
ry := b.Copy()
Af(x, ry, -1.0, -1.0, la.OptNoTrans)
resy := math.Sqrt(ry.Dot(ry))
// rz = s + G*x - h
rz := matrix.FloatZeros(cdim, 1)
Gf(x, &matrixVar{rz}, 1.0, 0.0, la.OptNoTrans)
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 := c.Dot(x)
by := b.Dot(y)
hz := sdot(h, z, dims, 0)
//sol.X = x; sol.Y = y; sol.S = s; sol.Z = z
sol.Result = sets.NewFloatSet("x", "y", "s", "x")
sol.Result.Append("x", x.Matrix())
sol.Result.Append("y", y.Matrix())
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']]
if dims.At("l")[0] > 0 {
is = append(is, matrix.MakeIndexSet(0, dims.At("l")[0], 1)...)
}
// indexes s[indq[:-1]]
if len(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)...)
ind += m * m
}
for _, k := range is {
s.SetIndex(k, a+s.GetIndex(k))
}
}
if tz >= -1e-8*math.Max(nrmz, 1.0) {
a := 1.0 + tz
is := make([]int, 0)
// indexes z[:dims['l']]
if dims.At("l")[0] > 0 {
is = append(is, matrix.MakeIndexSet(0, dims.At("l")[0], 1)...)
}
// indexes z[indq[:-1]]
if len(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)...)
ind += m * m
}
for _, k := range is {
z.SetIndex(k, a+z.GetIndex(k))
}
}
} else if primalstart == nil && dualstart != nil {
if ts >= -1e-8*math.Max(nrms, 1.0) {
a := 1.0 + ts
is := make([]int, 0)
if dims.At("l")[0] > 0 {
is = append(is, matrix.MakeIndexSet(0, dims.At("l")[0], 1)...)
}
if len(indq) > 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 {
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)
if dims.At("l")[0] > 0 {
is = append(is, matrix.MakeIndexSet(0, dims.At("l")[0], 1)...)
}
if len(indq) > 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))
}
}
}
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 MatrixVariable
var z1 *matrix.FloatMatrix
var dg, dgi float64
var th *matrix.FloatMatrix
var WS fVarClosure
var f3 KKTFuncVar
var cx, by, hz, rt float64
var hresx, resx, hresy, resy, hresz, resz float64
var dres, pres, dinfres, pinfres float64
// check point variables
checkpnt.AddMatrixVar("lmbda", lmbda)
checkpnt.AddMatrixVar("lmbdasq", lmbdasq)
checkpnt.AddVerifiable("rx", rx)
checkpnt.AddVerifiable("ry", ry)
checkpnt.AddMatrixVar("rz", rz)
checkpnt.AddFloatVar("resx", &resx)
checkpnt.AddFloatVar("resy", &resy)
checkpnt.AddFloatVar("resz", &resz)
checkpnt.AddFloatVar("hresx", &hresx)
checkpnt.AddFloatVar("hresy", &hresy)
checkpnt.AddFloatVar("hresz", &hresz)
checkpnt.AddFloatVar("cx", &cx)
checkpnt.AddFloatVar("by", &by)
checkpnt.AddFloatVar("hz", &hz)
checkpnt.AddFloatVar("gap", &gap)
checkpnt.AddFloatVar("pres", &pres)
checkpnt.AddFloatVar("dres", &dres)
for iter := 0; iter < maxIter+1; iter++ {
checkpnt.MajorNext()
checkpnt.Check("loop-start", 100)
// hrx = -A'*y - G'*z
Af(y, hrx, -1.0, 0.0, la.OptTrans)
Gf(&matrixVar{z}, hrx, -1.0, 1.0, la.OptTrans)
hresx = math.Sqrt(hrx.Dot(hrx))
// rx = hrx - c*tau
// = -A'*y - G'*z - c*tau
mCopy(hrx, rx)
c.Axpy(rx, -tau.Float())
resx = math.Sqrt(rx.Dot(rx)) / tau.Float()
// hry = A*x
Af(x, hry, 1.0, 0.0, la.OptNoTrans)
hresy = math.Sqrt(hry.Dot(hry))
// ry = hry - b*tau
// = A*x - b*tau
mCopy(hry, ry)
b.Axpy(ry, -tau.Float())
resy = math.Sqrt(ry.Dot(ry)) / tau.Float()
// hrz = s + G*x
Gf(x, &matrixVar{hrz}, 1.0, 0.0, la.OptNoTrans)
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 = c.Dot(x)
by = b.Dot(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))
}
checkpnt.Check("isready", 200)
if (pres <= feasTolerance && dres <= feasTolerance &&
(gap <= absTolerance || (!math.IsNaN(relgap) && relgap <= relTolerance))) ||
iter == maxIter {
// done
x.Scal(1.0 / tau.Float())
y.Scal(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 == 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 = sets.NewFloatSet("x", "y", "s", "x")
sol.Result.Append("x", x.Matrix())
sol.Result.Append("y", y.Matrix())
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 = sets.NewFloatSet("x", "y", "s", "x")
sol.Result.Append("x", x.Matrix())
sol.Result.Append("y", y.Matrix())
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")
y.Scal(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 = sets.NewFloatSet("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")
x.Scal(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 = sets.NewFloatSet("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 {
//fmt.Printf("compute scaling: lmbda=\n%v\n", lmbda.ToString("%.17f"))
//fmt.Printf("s=\n%v\n", s.ToString("%.17f"))
//fmt.Printf("z=\n%v\n", z.ToString("%.17f"))
W, err = computeScaling(s, z, lmbda, dims, 0)
checkpnt.AddScaleVar(W)
// 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())))
//fmt.Printf("lmbda=\n%v\n", lmbda.ToString("%.17f"))
//W.Print()
checkpnt.Check("compute_scaling", 300)
}
// 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)
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)
checkpnt.AddVerifiable("x1", x1)
checkpnt.AddMatrixVar("z1", z1)
}
mCopy(c, x1)
x1.Scal(-1.0)
mCopy(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)
x1.Scal(dgi)
y1.Scal(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()
x.Scal(t_)
y.Scal(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 = sets.NewFloatSet("x", "y", "s", "x")
sol.Result.Append("x", x.Matrix())
sol.Result.Append("y", y.Matrix())
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)
checkpnt.AddMatrixVar("th", th)
}
blas.Copy(h, th)
scale(th, W, true, true)
f6_no_ir := func(x, y MatrixVariable, 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) ]
minor := checkpnt.MinorTop()
err = nil
// y := -y = -by
y.Scal(-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)
checkpnt.Check("prescale", minor+5)
checkpnt.MinorPush(minor + 5)
err = scale(ws3, W, true, false)
checkpnt.MinorPop()
if err != nil {
fmt.Printf("scale error: %s\n", err)
}
blas.AxpyFloat(ws3, z, 1.0)
blas.ScalFloat(z, -1.0)
checkpnt.Check("f3-call", minor+20)
checkpnt.MinorPush(minor + 20)
err = f3(x, y, z)
checkpnt.MinorPop()
checkpnt.Check("f3-return", minor+40)
// 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_ += c.Dot(x)
tau_ += b.Dot(y)
tau_ += sdot(th, z, dims, 0)
tau_ = dgi * tau_ / (1.0 + sdot(z1, z1, dims, 0))
tau.SetValue(tau_)
x1.Axpy(x, tau_)
y1.Axpy(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)
checkpnt.AddVerifiable("wx", WS.wx)
checkpnt.AddMatrixVar("wz", WS.wz)
checkpnt.AddMatrixVar("ws", WS.ws)
}
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)
checkpnt.AddVerifiable("wx2", WS.wx2)
checkpnt.AddMatrixVar("wz2", WS.wz2)
checkpnt.AddMatrixVar("ws2", WS.ws2)
}
}
f6 := func(x, y MatrixVariable, z, tau, s, kappa *matrix.FloatMatrix) error {
var err error = nil
minor := checkpnt.MinorTop()
checkpnt.Check("startf6", minor+100)
if refinement > 0 || solopts.Debug {
mCopy(x, WS.wx)
mCopy(y, WS.wy)
blas.Copy(z, WS.wz)
blas.Copy(s, WS.ws)
WS.wtau.SetValue(tau.Float())
WS.wkappa.SetValue(kappa.Float())
}
checkpnt.Check("pref6_no_ir", minor+200)
err = f6_no_ir(x, y, z, tau, s, kappa)
checkpnt.Check("postf6_no_ir", minor+399)
for i := 0; i < refinement; i++ {
mCopy(WS.wx, WS.wx2)
mCopy(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())
checkpnt.Check("res-call", minor+400)
checkpnt.MinorPush(minor + 400)
err = res(x, y, z, tau, s, kappa, WS.wx2, WS.wy2, WS.wz2, WS.wtau2, WS.ws2, WS.wkappa2, W, dg, lmbda)
checkpnt.MinorPop()
checkpnt.Check("refine_pref6_no_ir", minor+500)
checkpnt.MinorPush(minor + 500)
err = f6_no_ir(WS.wx2, WS.wy2, WS.wz2, WS.wtau2, WS.ws2, WS.wkappa2)
checkpnt.MinorPop()
checkpnt.Check("refine_postf6_no_ir", minor+600)
WS.wx2.Axpy(x, 1.0)
WS.wy2.Axpy(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 {
checkpnt.MinorPush(minor + 700)
res(x, y, z, tau, s, kappa, WS.wx, WS.wy, WS.wz, WS.wtau, WS.ws, WS.wkappa, W, dg, lmbda)
checkpnt.MinorPop()
fmt.Printf("KKT residuals\n")
fmt.Printf(" 'x' : %.6e\n", math.Sqrt(WS.wx.Dot(WS.wx)))
fmt.Printf(" 'y' : %.6e\n", math.Sqrt(WS.wy.Dot(WS.wy)))
fmt.Printf(" 'z' : %.6e\n", snrm2(WS.wz, dims, 0))
fmt.Printf(" 'tau' : %.6e\n", math.Abs(WS.wtau.Float()))
fmt.Printf(" 's' : %.6e\n", snrm2(WS.ws, dims, 0))
fmt.Printf(" 'kappa': %.6e\n", math.Abs(WS.wkappa.Float()))
}
return err
}
var nrm float64 = blas.Nrm2Float(lmbda)
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)
// indexes: [:dims['l']]
if dims.At("l")[0] > 0 {
is = append(is, matrix.MakeIndexSet(0, dims.At("l")[0], 1)...)
}
// ...[indq[:-1]]
if len(indq) > 1 {
is = append(is, indq[:len(indq)-1]...)
}
// ...[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
}
//ds.Add(-sigma*mu, is...)
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)
mCopy(rx, dx)
dx.Scal(1.0 - sigma)
mCopy(ry, dy)
dy.Scal(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)
checkpnt.Check("pref6", (1+i)*1000)
checkpnt.MinorPush((1 + i) * 1000)
err = f6(dx, dy, dz, dtau, ds, dkappa)
checkpnt.MinorPop()
checkpnt.Check("postf6", (1+i)*1000+800)
// 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
checkpnt.MinorPush((1+i)*1000 + 900)
scale2(lmbda, ds, dims, 0, false)
scale2(lmbda, dz, dims, 0, false)
checkpnt.MinorPop()
checkpnt.Check("post-scale2", (1+i)*1000+990)
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)
checkpnt.Check("update-xy", 7000)
// Update x, y
dx.Axpy(x, step)
dy.Axpy(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))
}
checkpnt.Check("update-dsdz", 7500)
// 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}
checkpnt.MinorPush(7500)
scale2(lmbda, ds, dims, 0, true)
scale2(lmbda, dz, dims, 0, true)
checkpnt.MinorPop()
// 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
}
checkpnt.Check("pre-update-scaling", 7700)
err = updateScaling(W, lmbda, ds, dz)
checkpnt.Check("post-update-scaling", 7800)
// 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
checkpnt.Check("end-of-loop", 8000)
//fmt.Printf(" ** kappa=%.10f, tau=%.10f, gap=%.10f\n", kappa.Float(), tau.Float(), gap)
}
return
}