func TestDaxpy(t *testing.T) {
fmt.Printf("* L1 * test axpy: Y = alpha * X + Y\n")
X := matrix.FloatVector([]float64{1, 1, 1})
Y := matrix.FloatVector([]float64{0, 0, 0})
fmt.Printf("before:\nX=\n%v\nY=\n%v\n", X, Y)
Axpy(X, Y, matrix.FScalar(5.0))
fmt.Printf("after:\nX=\n%v\nY=\n%v\n", X, Y)
}
func main() {
flag.Parse()
if len(spPath) > 0 {
checkpnt.Reset(spPath)
checkpnt.Activate()
checkpnt.Verbose(spVerbose)
checkpnt.Format("%.17f")
}
gdata := [][]float64{
[]float64{16., 7., 24., -8., 8., -1., 0., -1., 0., 0., 7.,
-5., 1., -5., 1., -7., 1., -7., -4.},
[]float64{-14., 2., 7., -13., -18., 3., 0., 0., -1., 0., 3.,
13., -6., 13., 12., -10., -6., -10., -28.},
[]float64{5., 0., -15., 12., -6., 17., 0., 0., 0., -1., 9.,
6., -6., 6., -7., -7., -6., -7., -11.}}
hdata := []float64{-3., 5., 12., -2., -14., -13., 10., 0., 0., 0., 68.,
-30., -19., -30., 99., 23., -19., 23., 10.}
c := matrix.FloatVector([]float64{-6., -4., -5.})
G := matrix.FloatMatrixFromTable(gdata)
h := matrix.FloatVector(hdata)
dims := sets.NewDimensionSet("l", "q", "s")
dims.Set("l", []int{2})
dims.Set("q", []int{4, 4})
dims.Set("s", []int{3})
var solopts cvx.SolverOptions
solopts.MaxIter = 30
solopts.ShowProgress = true
if maxIter > 0 {
solopts.MaxIter = maxIter
}
if len(solver) > 0 {
solopts.KKTSolverName = solver
}
sol, err := cvx.ConeLp(c, G, h, nil, nil, dims, &solopts, nil, 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)
} else {
fmt.Printf("status: %s\n", err)
}
}
func main() {
flag.Parse()
gdata0 := [][]float64{
[]float64{12., 13., 12.},
[]float64{6., -3., -12.},
[]float64{-5., -5., 6.}}
gdata1 := [][]float64{
[]float64{3., 3., -1., 1.},
[]float64{-6., -6., -9., 19.},
[]float64{10., -2., -2., -3.}}
c := matrix.FloatVector([]float64{-2.0, 1.0, 5.0})
g0 := matrix.FloatMatrixFromTable(gdata0, matrix.ColumnOrder)
g1 := matrix.FloatMatrixFromTable(gdata1, matrix.ColumnOrder)
Ghq := sets.FloatSetNew("Gq", "hq")
Ghq.Append("Gq", g0, g1)
h0 := matrix.FloatVector([]float64{-12.0, -3.0, -2.0})
h1 := matrix.FloatVector([]float64{27.0, 0.0, 3.0, -42.0})
Ghq.Append("hq", h0, h1)
var Gl, hl, A, b *matrix.FloatMatrix = nil, nil, nil, nil
var solopts cvx.SolverOptions
solopts.MaxIter = 30
solopts.ShowProgress = true
if maxIter > -1 {
solopts.MaxIter = maxIter
}
if len(solver) > 0 {
solopts.KKTSolverName = solver
}
sol, err := cvx.Socp(c, Gl, hl, A, b, Ghq, &solopts, nil, nil)
fmt.Printf("status: %v\n", err)
if sol != nil && sol.Status == cvx.Optimal {
x := sol.Result.At("x")[0]
fmt.Printf("x=\n%v\n", x.ToString("%.9f"))
for i, m := range sol.Result.At("sq") {
fmt.Printf("sq[%d]=\n%v\n", i, m.ToString("%.9f"))
}
for i, m := range sol.Result.At("zq") {
fmt.Printf("zq[%d]=\n%v\n", i, m.ToString("%.9f"))
}
sq0 := sol.Result.At("sq")[0]
sq1 := sol.Result.At("sq")[1]
zq0 := sol.Result.At("zq")[0]
zq1 := sol.Result.At("zq")[1]
check(x, sq0, sq1, zq0, zq1)
}
}
func TestAcent(t *testing.T) {
// matrix string in row order presentation
Adata := [][]float64{
[]float64{-7.44e-01, 1.11e-01, 1.29e+00, 2.62e+00, -1.82e+00},
[]float64{4.59e-01, 7.06e-01, 3.16e-01, -1.06e-01, 7.80e-01},
[]float64{-2.95e-02, -2.22e-01, -2.07e-01, -9.11e-01, -3.92e-01},
[]float64{-7.75e-01, 1.03e-01, -1.22e+00, -5.74e-01, -3.32e-01},
[]float64{-1.80e+00, 1.24e+00, -2.61e+00, -9.31e-01, -6.38e-01}}
bdata := []float64{
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}
// these are solution obtained from running cvxopt acent.py with above data
solData := []float64{-11.59728373909344512, -1.35196389161339936,
7.21894899350256303, -3.29159917142051528, 4.90454147385329176}
ntData := []float64{
1.5163484265903457, 1.2433928210771914, 1.0562922103520955, 0.8816246051011607,
0.7271128861543598, 0.42725003346248974, 0.0816777301914883, 0.0005458037072843131,
1.6259980735305693e-10}
b := matrix.FloatVector(bdata)
Al := matrix.FloatMatrixFromTable(Adata, matrix.RowOrder)
Au := matrix.Scale(Al, -1.0)
A := matrix.FloatZeros(2*Al.Rows(), Al.Cols())
A.SetSubMatrix(0, 0, Al)
A.SetSubMatrix(Al.Rows(), 0, Au)
x, nt, err := acent(A, b, 10)
if err != nil {
t.Logf("Acent error: %s", err)
t.Fail()
}
solref := matrix.FloatVector(solData)
ntref := matrix.FloatVector(ntData)
soldf := matrix.Minus(x, solref)
ntdf := matrix.Minus(matrix.FloatVector(nt), ntref)
solNrm := blas.Nrm2Float(soldf)
ntNrm := blas.Nrm2Float(ntdf)
t.Logf("x [diff=%.2e]:\n%v\n", solNrm, x)
t.Logf("nt [diff=%.2e]:\n%v\n", ntNrm, nt)
if solNrm > TOL {
t.Log("solution deviates too much from expected\n")
t.Fail()
}
}
func _TestRankSmall(t *testing.T) {
bM := 5
bN := 5
//bP := 5
Adata := [][]float64{
[]float64{1.0, 1.0, 1.0, 1.0, 1.0},
[]float64{2.0, 2.0, 2.0, 2.0, 2.0},
[]float64{3.0, 3.0, 3.0, 3.0, 3.0},
[]float64{4.0, 4.0, 4.0, 4.0, 4.0},
[]float64{5.0, 5.0, 5.0, 5.0, 5.0}}
A := matrix.FloatMatrixFromTable(Adata, matrix.RowOrder)
A0 := matrix.FloatMatrixFromTable(Adata, matrix.RowOrder)
X := matrix.FloatVector([]float64{1.0, 2.0, 3.0, 4.0, 5.0})
Y := matrix.FloatWithValue(bN, 1, 2.0)
Ar := A.FloatArray()
Xr := X.FloatArray()
Yr := Y.FloatArray()
blas.GerFloat(X, Y, A0, 1.0)
DRankMV(Ar, Xr, Yr, 1.0, A.LeadingIndex(), 1, 1, 0, bN, 0, bM, 4, 4)
ok := A0.AllClose(A)
t.Logf("A0 == A1: %v\n", ok)
if !ok {
t.Logf("blas ger:\n%v\n", A0)
t.Logf("A1: \n%v\n", A)
}
}
// Dscal: X = alpha * X
func TestDscal(t *testing.T) {
fmt.Printf("* L1 * test scal: X = alpha * X\n")
alpha := matrix.FScalar(2.0)
A := matrix.FloatVector([]float64{1.0, 1.0, 1.0, 1.0, 1.0, 1.0})
Scal(A, alpha)
fmt.Printf("Dscal 2.0 * A\n")
fmt.Printf("%s\n", A)
A = matrix.FloatVector([]float64{1.0, 1.0, 1.0, 1.0, 1.0, 1.0})
Scal(A, alpha, &linalg.IOpt{"offset", 3})
fmt.Printf("Dscal 2.0 * A[3:]\n")
fmt.Printf("%s\n", A)
A = matrix.FloatVector([]float64{1.0, 1.0, 1.0, 1.0, 1.0, 1.0})
fmt.Printf("Dscal 2.0* A[::2]\n")
Scal(A, alpha, &linalg.IOpt{"inc", 2})
fmt.Printf("%s\n", A)
}
// v = X.T * Y
func TestDdot(t *testing.T) {
fmt.Printf("* L1 * test dot: X.T*Y\n")
A := matrix.FloatVector([]float64{1.0, 1.0, 1.0, 1.0, 1.0, 1.0})
B := matrix.FloatVector([]float64{2.0, 2.0, 2.0, 2.0, 2.0, 2.0})
v1 := Dot(A, B)
v2 := Dot(A, B, &linalg.IOpt{"offset", 3})
v3 := Dot(A, B, &linalg.IOpt{"inc", 2})
fmt.Printf("Ddot: X.T * Y\n")
fmt.Printf("%.3f\n", v1.Float())
fmt.Printf("%.3f\n", v2.Float())
fmt.Printf("%.3f\n", v3.Float())
// Output:
// 12.000
// 6.000
// 6.000
}
func (gp *gpConvexProg) F1(x *matrix.FloatMatrix) (f, Df *matrix.FloatMatrix, err error) {
f = nil
Df = nil
err = nil
f = matrix.FloatZeros(gp.mnl+1, 1)
Df = matrix.FloatZeros(gp.mnl+1, gp.n)
y := gp.g.Copy()
blas.GemvFloat(gp.F, x, y, 1.0, 1.0)
for i, s := range gp.ind {
start := s[0]
stop := s[1]
// yi := exp(yi) = exp(Fi*x+gi)
ymax := maxvec(y.FloatArray()[start:stop])
// ynew = exp(y[start:stop] - ymax)
ynew := matrix.Exp(matrix.FloatVector(y.FloatArray()[start:stop]).Add(-ymax))
y.SetIndexesFromArray(ynew.FloatArray(), matrix.Indexes(start, stop)...)
// fi = log sum yi = log sum exp(Fi*x+gi)
ysum := blas.AsumFloat(y, &la.IOpt{"n", stop - start}, &la.IOpt{"offset", start})
f.SetIndex(i, ymax+math.Log(ysum))
blas.ScalFloat(y, 1.0/ysum, &la.IOpt{"n", stop - start}, &la.IOpt{"offset", start})
blas.GemvFloat(gp.F, y, Df, 1.0, 0.0, la.OptTrans, &la.IOpt{"m", stop - start},
&la.IOpt{"incy", gp.mnl + 1}, &la.IOpt{"offseta", start},
&la.IOpt{"offsetx", start}, &la.IOpt{"offsety", i})
}
return
}
func main() {
var A, b *matrix.FloatMatrix = nil, nil
m, n := 20, 20
blas.PanicOnError(true)
matrix.PanicOnError(true)
flag.Parse()
if len(spPath) > 0 {
checkpnt.Reset(spPath)
checkpnt.Activate()
checkpnt.Verbose(spVerbose)
checkpnt.Format("%.17f")
}
if len(AVal) > 0 {
A, _ = matrix.FloatParse(AVal)
if A == nil {
fmt.Printf("could not parse:\n%s\n", AVal)
return
}
} else {
A = matrix.FloatNormal(m, n)
}
if len(bVal) > 0 {
b, _ = matrix.FloatParse(bVal)
if b == nil {
fmt.Printf("could not parse:\n%s\n", bVal)
return
}
} else {
b = matrix.FloatNormal(m, 1)
}
sol, err := qcl1(A, b)
if sol != nil {
r := sol.Result.At("x")[0]
x := matrix.FloatVector(r.FloatArray()[:A.Cols()])
r = sol.Result.At("z")[0]
z := matrix.FloatVector(r.FloatArray()[r.NumElements()-A.Rows():])
fmt.Printf("x=\n%v\n", x.ToString("%.9f"))
fmt.Printf("z=\n%v\n", z.ToString("%.9f"))
check(x, z)
} else {
fmt.Printf("status: %v\n", err)
}
}
func TestDgemv(t *testing.T) {
fmt.Printf("* L2 * test gemv: Y = alpha * A * X + beta * Y\n")
A := matrix.FloatNew(3, 2, []float64{1, 1, 1, 2, 2, 2})
X := matrix.FloatVector([]float64{1, 1})
Y := matrix.FloatVector([]float64{0, 0, 0})
alpha := matrix.FScalar(1.0)
beta := matrix.FScalar(0.0)
fmt.Printf("before: alpha=1.0, beta=0.0\nA=\n%v\nX=\n%v\nY=\n%v\n", A, X, Y)
err := Gemv(A, X, Y, alpha, beta)
fmt.Printf("after:\nA=\n%v\nX=\n%v\nY=\n%v\n", A, X, Y)
fmt.Printf("* L2 * test gemv: X = alpha * A.T * Y + beta * X\n")
err = Gemv(A, Y, X, alpha, beta, linalg.OptTrans)
if err != nil {
fmt.Printf("error: %s\n", err)
}
fmt.Printf("after:\nA=\n%v\nX=\n%v\nY=\n%v\n", A, X, Y)
}
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 (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 := matrix.Div(p.Amin, matrix.Pow(x17, 3.0))
tmp = matrix.Mul(z, tmp).Scale(2.0)
diag := matrix.FloatDiagonal(5, tmp.FloatArray()...)
H = matrix.FloatZeros(22, 22)
H.SetSubMatrix(17, 17, diag)
return
}
func main() {
flag.Parse()
x := floorplan(matrix.FloatWithValue(5, 1, 100.0))
if x != nil {
W := x.GetIndex(0)
H := x.GetIndex(1)
xs := matrix.FloatVector(x.FloatArray()[2:7])
ys := matrix.FloatVector(x.FloatArray()[7:12])
ws := matrix.FloatVector(x.FloatArray()[12:17])
hs := matrix.FloatVector(x.FloatArray()[17:])
fmt.Printf("W = %.5f, H = %.5f\n", W, H)
fmt.Printf("x = \n%v\n", xs.ToString("%.5f"))
fmt.Printf("y = \n%v\n", ys.ToString("%.5f"))
fmt.Printf("w = \n%v\n", ws.ToString("%.5f"))
fmt.Printf("h = \n%v\n", hs.ToString("%.5f"))
check(x)
}
}
func sinv(x, y *matrix.FloatMatrix, dims *sets.DimensionSet, mnl int) (err error) {
/*DEBUGGED*/
err = nil
// For the nonlinear and 'l' blocks:
//
// yk o\ xk = yk .\ xk.
ind := mnl + dims.At("l")[0]
blas.Tbsv(y, x, &la_.IOpt{"n", ind}, &la_.IOpt{"k", 0}, &la_.IOpt{"ldA", 1})
// For the 'q' blocks:
//
// [ l0 -l1' ]
// yk o\ xk = 1/a^2 * [ ] * xk
// [ -l1 (a*I + l1*l1')/l0 ]
//
// where yk = (l0, l1) and a = l0^2 - l1'*l1.
for _, m := range dims.At("q") {
aa := blas.Nrm2Float(y, &la_.IOpt{"n", m - 1}, &la_.IOpt{"offset", ind + 1})
ee := y.GetIndex(ind)
aa = (ee + aa) * (ee - aa)
cc := x.GetIndex(ind)
dd := blas.DotFloat(x, y, &la_.IOpt{"n", m - 1}, &la_.IOpt{"offsetx", ind + 1},
&la_.IOpt{"offsety", ind + 1})
x.SetIndex(ind, cc*ee-dd)
blas.ScalFloat(x, aa/ee, &la_.IOpt{"n", m - 1}, &la_.IOpt{"offset", ind + 1})
blas.AxpyFloat(y, x, dd/ee-cc, &la_.IOpt{"n", m - 1},
&la_.IOpt{"offsetx", ind + 1}, &la_.IOpt{"offsety", ind + 1})
blas.ScalFloat(x, 1.0/aa, &la_.IOpt{"n", m}, &la_.IOpt{"offset", ind})
ind += m
}
// For the 's' blocks:
//
// yk o\ xk = xk ./ gamma
//
// where gammaij = .5 * (yk_i + yk_j).
ind2 := ind
for _, m := range dims.At("s") {
for j := 0; j < m; j++ {
u := matrix.FloatVector(y.FloatArray()[ind2+j : ind2+m])
u.Add(y.GetIndex(ind2 + j))
u.Scale(0.5)
blas.Tbsv(u, x, &la_.IOpt{"n", m - j}, &la_.IOpt{"k", 0}, &la_.IOpt{"lda", 1},
&la_.IOpt{"offsetx", ind + j*(m+1)})
}
ind += m * m
ind2 += m
}
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 := matrix.Div(p.Amin, matrix.Mul(x17, x17)).Scale(-1.0)
dk2.SetIndexesFromArray(diag.FloatArray(), matrix.MakeDiagonalSet(5)...)
Df, _ = matrix.FloatMatrixStacked(matrix.StackRight, zeros, dk1, dk2)
x12 := matrix.FloatVector(x.FloatArray()[12:17])
// f = -x[12:17] + div(Amin, x[17:]) == div(Amin, x[17:]) - x[12:17]
f = matrix.Minus(matrix.Div(p.Amin, x17), x12)
return
}
func main() {
flag.Parse()
if len(spPath) > 0 {
checkpnt.Reset(spPath)
checkpnt.Activate()
checkpnt.Verbose(spVerbose)
checkpnt.Format("%.17f")
}
gdata := [][]float64{
[]float64{2.0, 1.0, -1.0, 0.0},
[]float64{1.0, 2.0, 0.0, -1.0}}
c := matrix.FloatVector([]float64{-4.0, -5.0})
G := matrix.FloatMatrixFromTable(gdata, matrix.ColumnOrder)
h := matrix.FloatVector([]float64{3.0, 3.0, 0.0, 0.0})
var solopts cvx.SolverOptions
solopts.MaxIter = 30
solopts.ShowProgress = true
if maxIter > -1 {
solopts.MaxIter = maxIter
}
if len(solver) > 0 {
solopts.KKTSolverName = solver
}
sol, err := cvx.Lp(c, G, h, nil, nil, &solopts, nil, nil)
if sol != nil && sol.Status == cvx.Optimal {
x := sol.Result.At("x")[0]
s := sol.Result.At("s")[0]
z := sol.Result.At("z")[0]
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)
} else {
fmt.Printf("status: %v\n", err)
}
}
// The analytic centering with cone constraints example of section 9.1
// (Problems with nonlinear objectives).
func TestCp(t *testing.T) {
xref := []float64{0.41132359189354400, 0.55884774432611484, -0.72007090016957931}
F := &acenterProg{3, 1}
gdata := [][]float64{
[]float64{0., -1., 0., 0., -21., -11., 0., -11., 10., 8., 0., 8., 5.},
[]float64{0., 0., -1., 0., 0., 10., 16., 10., -10., -10., 16., -10., 3.},
[]float64{0., 0., 0., -1., -5., 2., -17., 2., -6., 8., -17., -7., 6.}}
G := matrix.FloatMatrixFromTable(gdata)
h := matrix.FloatVector(
[]float64{1.0, 0.0, 0.0, 0.0, 20., 10., 40., 10., 80., 10., 40., 10., 15.})
var solopts SolverOptions
solopts.MaxIter = 40
solopts.ShowProgress = false
dims := sets.NewDimensionSet("l", "q", "s")
dims.Set("l", []int{0})
dims.Set("q", []int{4})
dims.Set("s", []int{3})
sol, err := Cp(F, G, h, nil, nil, dims, &solopts)
if err == nil && sol.Status == Optimal {
x := sol.Result.At("x")[0]
t.Logf("x = \n%v\n", x.ToString("%.9f"))
xe, _ := nrmError(matrix.FloatVector(xref), x)
if xe > TOL {
t.Logf("x differs [%.3e] from exepted too much.", xe)
t.Fail()
}
} else {
t.Logf("result: %v\n", err)
t.Fail()
}
}
// a = sum(X)
func TestDasum(t *testing.T) {
fmt.Printf("* L1 * test sum: sum(X)\n")
A := matrix.FloatVector([]float64{1.0, 1.0, 1.0, 1.0, 1.0, 1.0})
v1 := Asum(A, &linalg.IOpt{"offset", 0})
v2 := Asum(A, &linalg.IOpt{"offset", 3})
v3 := Asum(A, &linalg.IOpt{"inc", 2})
fmt.Printf("Dasum\n")
fmt.Printf("%.3f\n", v1.Float())
fmt.Printf("%.3f\n", v2.Float())
fmt.Printf("%.3f\n", v3.Float())
// Output:
// 6.000
// 3.000
// 3.000
}
// a = norm2(A)
func TestDnrm2(t *testing.T) {
fmt.Printf("* L1 * test sum: nrm2(X)\n")
A := matrix.FloatVector([]float64{1.0, 1.0, 1.0, 1.0, 1.0, 1.0})
v1 := Nrm2(A, &linalg.IOpt{"offset", 0})
v2 := Nrm2(A, &linalg.IOpt{"offset", 3})
v3 := Nrm2(A, &linalg.IOpt{"inc", 2})
fmt.Printf("Ddnrm2\n")
fmt.Printf("%.3f\n", v1.Float())
fmt.Printf("%.3f\n", v2.Float())
fmt.Printf("%.3f\n", v3.Float())
// Output:
// 2.499
// 1.732
// 1.732
}
// The small GP of section 9.3 (Geometric programming).
func TestGp(t *testing.T) {
xref := []float64{1.06032641296944741, 1.75347359157296845, 2.44603683900611868}
aflr := 1000.0
awall := 100.0
alpha := 0.5
beta := 2.0
gamma := 0.5
delta := 2.0
fdata := [][]float64{
[]float64{-1.0, 1.0, 1.0, 0.0, -1.0, 1.0, 0.0, 0.0},
[]float64{-1.0, 1.0, 0.0, 1.0, 1.0, -1.0, 1.0, -1.0},
[]float64{-1.0, 0.0, 1.0, 1.0, 0.0, 0.0, -1.0, 1.0}}
gdata := []float64{1.0, 2.0 / awall, 2.0 / awall, 1.0 / aflr, alpha, 1.0 / beta, gamma, 1.0 / delta}
g := matrix.FloatNew(8, 1, gdata).Log()
F := matrix.FloatMatrixFromTable(fdata)
K := []int{1, 2, 1, 1, 1, 1, 1}
var solopts SolverOptions
solopts.MaxIter = 40
solopts.ShowProgress = false
solopts.KKTSolverName = "ldl"
sol, err := Gp(K, F, g, nil, nil, nil, nil, &solopts)
if sol != nil && sol.Status == Optimal {
x := sol.Result.At("x")[0]
r := matrix.Exp(x)
h := r.GetIndex(0)
w := r.GetIndex(1)
d := r.GetIndex(2)
t.Logf("x=\n%v\n", x.ToString("%.9f"))
t.Logf("h = %f, w = %f, d = %f.\n", h, w, d)
xe, _ := nrmError(matrix.FloatVector(xref), x)
if xe > TOL {
t.Logf("x differs [%.3e] from exepted too much.", xe)
t.Fail()
}
} else {
t.Logf("status: %v\n", err)
t.Fail()
}
}
func (gp *gpConvexProg) F2(x, z *matrix.FloatMatrix) (f, Df, H *matrix.FloatMatrix, err error) {
err = nil
f = matrix.FloatZeros(gp.mnl+1, 1)
Df = matrix.FloatZeros(gp.mnl+1, gp.n)
H = matrix.FloatZeros(gp.n, gp.n)
y := gp.g.Copy()
Fsc := matrix.FloatZeros(gp.maxK, gp.n)
blas.GemvFloat(gp.F, x, y, 1.0, 1.0)
//fmt.Printf("y=\n%v\n", y.ToString("%.3f"))
for i, s := range gp.ind {
start := s[0]
stop := s[1]
// yi := exp(yi) = exp(Fi*x+gi)
ymax := maxvec(y.FloatArray()[start:stop])
ynew := matrix.Exp(matrix.FloatVector(y.FloatArray()[start:stop]).Add(-ymax))
y.SetIndexesFromArray(ynew.FloatArray(), matrix.Indexes(start, stop)...)
// fi = log sum yi = log sum exp(Fi*x+gi)
ysum := blas.AsumFloat(y, &la.IOpt{"n", stop - start}, &la.IOpt{"offset", start})
f.SetIndex(i, ymax+math.Log(ysum))
blas.ScalFloat(y, 1.0/ysum, &la.IOpt{"n", stop - start}, &la.IOpt{"offset", start})
blas.GemvFloat(gp.F, y, Df, 1.0, 0.0, la.OptTrans, &la.IOpt{"m", stop - start},
&la.IOpt{"incy", gp.mnl + 1}, &la.IOpt{"offseta", start},
&la.IOpt{"offsetx", start}, &la.IOpt{"offsety", i})
Fsc.SetSubMatrix(0, 0, gp.F.GetSubMatrix(start, 0, stop-start))
for k := start; k < stop; k++ {
blas.AxpyFloat(Df, Fsc, -1.0, &la.IOpt{"n", gp.n},
&la.IOpt{"incx", gp.mnl + 1}, &la.IOpt{"incy", Fsc.Rows()},
&la.IOpt{"offsetx", i}, &la.IOpt{"offsety", k - start})
blas.ScalFloat(Fsc, math.Sqrt(y.GetIndex(k)),
&la.IOpt{"inc", Fsc.Rows()}, &la.IOpt{"offset", k - start})
}
// H += z[i]*Hi = z[i] *Fisc' * Fisc
blas.SyrkFloat(Fsc, H, z.GetIndex(i), 1.0, la.OptTrans,
&la.IOpt{"k", stop - start})
}
return
}
func acenter() *matrix.FloatMatrix {
F := &acenterProg{3, 1}
gdata := [][]float64{
[]float64{0., -1., 0., 0., -21., -11., 0., -11., 10., 8., 0., 8., 5.},
[]float64{0., 0., -1., 0., 0., 10., 16., 10., -10., -10., 16., -10., 3.},
[]float64{0., 0., 0., -1., -5., 2., -17., 2., -6., 8., -17., -7., 6.}}
G := matrix.FloatMatrixFromTable(gdata)
h := matrix.FloatVector(
[]float64{1.0, 0.0, 0.0, 0.0, 20., 10., 40., 10., 80., 10., 40., 10., 15.})
var solopts cvx.SolverOptions
solopts.MaxIter = 40
solopts.ShowProgress = true
if maxIter > -1 {
solopts.MaxIter = maxIter
}
if len(solver) > 0 {
solopts.KKTSolverName = solver
}
dims := sets.NewDimensionSet("l", "q", "s")
dims.Set("l", []int{0})
dims.Set("q", []int{4})
dims.Set("s", []int{3})
var err error
var sol *cvx.Solution
sol, err = cvx.Cp(F, 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
}
func _TestMultMVSmall(t *testing.T) {
bM := 5
bN := 4
A := matrix.FloatNormal(bM, bN)
X := matrix.FloatVector([]float64{1.0, 2.0, 3.0, 4.0})
Y1 := matrix.FloatZeros(bM, 1)
Y0 := matrix.FloatZeros(bM, 1)
Ar := A.FloatArray()
Xr := X.FloatArray()
Y1r := Y1.FloatArray()
blas.GemvFloat(A, X, Y0, 1.0, 1.0)
DMultMV(Y1r, Ar, Xr, 1.0, 1.0, NOTRANS, 1, A.LeadingIndex(), 1, 0, bN, 0, bM, 4, 4)
ok := Y0.AllClose(Y1)
t.Logf("Y0 == Y1: %v\n", ok)
if !ok {
t.Logf("blas: Y=A*X\n%v\n", Y0)
t.Logf("Y1: Y1 = A*X\n%v\n", Y1)
}
}
// X <--> Y
func TestDswap(t *testing.T) {
fmt.Printf("* L1 * test swap: X <--> Y\n")
A := matrix.FloatVector([]float64{1.0, 1.0, 1.0, 1.0, 1.0, 1.0})
B := matrix.FloatVector([]float64{2.0, 2.0, 2.0, 2.0, 2.0, 2.0})
Swap(A, B)
fmt.Printf("Dswap A, B\n")
fmt.Printf("%s\n", A)
A = matrix.FloatVector([]float64{1.0, 1.0, 1.0, 1.0, 1.0, 1.0})
B = matrix.FloatVector([]float64{2.0, 2.0, 2.0, 2.0, 2.0, 2.0})
Swap(A, B, &linalg.IOpt{"offset", 3})
fmt.Printf("Dswap A[3:], B[3:]\n")
fmt.Printf("%s\n", A)
A = matrix.FloatVector([]float64{1.0, 1.0, 1.0, 1.0, 1.0, 1.0})
B = matrix.FloatVector([]float64{2.0, 2.0, 2.0, 2.0, 2.0, 2.0})
fmt.Printf("Dswap A[::2], B[::2]\n")
Swap(A, B, &linalg.IOpt{"inc", 2})
fmt.Printf("%s\n", A)
}
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.FloatMatrixFromTable(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 := matrix.Scale(S, mu)
pbarNeg := matrix.Scale(pbar, -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)
}
}
// 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 *sets.FloatMatrixSet, solopts *SolverOptions,
primalstart, dualstart *sets.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 := sets.NewDimensionSet("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.SetIndexesFromArray(hl.FloatArray()[:ml], matrix.MakeIndexSet(0, ml, 1)...)
ind := ml
for k, hs := range hsset {
h.SetIndexesFromArray(hs.FloatArray(), matrix.MakeIndexSet(ind, ind+ms[k]*ms[k], 1)...)
ind += ms[k] * ms[k]
}
Gargs := make([]*matrix.FloatMatrix, 0)
Gargs = append(Gargs, Gl)
Gargs = append(Gargs, Gsset...)
G, sizeg := matrix.FloatMatrixStacked(matrix.StackDown, Gargs...)
var pstart, dstart *sets.FloatMatrixSet = nil, nil
if primalstart != nil {
pstart = sets.NewFloatSet("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.FloatMatrixStacked(matrix.StackDown, margs...)
pstart.Set("s", sl)
}
if dualstart != nil {
dstart = sets.NewFloatSet("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.FloatMatrixStacked(matrix.StackDown, margs...)
dstart.Set("z", zl)
}
//fmt.Printf("h=\n%v\n", h.ToString("%.3f"))
//fmt.Printf("G=\n%v\n", G.ToString("%.3f"))
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
}
func main() {
flag.Parse()
if len(spPath) > 0 {
checkpnt.Reset(spPath)
checkpnt.Activate()
checkpnt.Verbose(spVerbose)
checkpnt.Format("%.17f")
}
gdata0 := [][]float64{
[]float64{-7., -11., -11., 3.},
[]float64{7., -18., -18., 8.},
[]float64{-2., -8., -8., 1.}}
gdata1 := [][]float64{
[]float64{-21., -11., 0., -11., 10., 8., 0., 8., 5.},
[]float64{0., 10., 16., 10., -10., -10., 16., -10., 3.},
[]float64{-5., 2., -17., 2., -6., 8., -17., -7., 6.}}
hdata0 := [][]float64{
[]float64{33., -9.},
[]float64{-9., 26.}}
hdata1 := [][]float64{
[]float64{14., 9., 40.},
[]float64{9., 91., 10.},
[]float64{40., 10., 15.}}
g0 := matrix.FloatMatrixFromTable(gdata0, matrix.ColumnOrder)
g1 := matrix.FloatMatrixFromTable(gdata1, matrix.ColumnOrder)
Ghs := sets.FloatSetNew("Gs", "hs")
Ghs.Append("Gs", g0, g1)
h0 := matrix.FloatMatrixFromTable(hdata0, matrix.ColumnOrder)
h1 := matrix.FloatMatrixFromTable(hdata1, matrix.ColumnOrder)
Ghs.Append("hs", h0, h1)
c := matrix.FloatVector([]float64{1.0, -1.0, 1.0})
var Gs, hs, A, b *matrix.FloatMatrix = nil, nil, nil, nil
var solopts cvx.SolverOptions
solopts.MaxIter = 30
solopts.ShowProgress = true
if maxIter > -1 {
solopts.MaxIter = maxIter
}
if len(solver) > 0 {
solopts.KKTSolverName = solver
}
sol, err := cvx.Sdp(c, Gs, hs, A, b, Ghs, &solopts, nil, nil)
if sol != nil && sol.Status == cvx.Optimal {
x := sol.Result.At("x")[0]
fmt.Printf("x=\n%v\n", x.ToString("%.9f"))
for i, m := range sol.Result.At("zs") {
fmt.Printf("zs[%d]=\n%v\n", i, m.ToString("%.9f"))
}
ss0 := sol.Result.At("ss")[0]
ss1 := sol.Result.At("ss")[1]
zs0 := sol.Result.At("zs")[0]
zs1 := sol.Result.At("zs")[1]
check(x, ss0, ss1, zs0, zs1)
} else {
fmt.Printf("status: %v\n", err)
}
checkpnt.Report()
}
func TestConeLp(t *testing.T) {
gdata := [][]float64{
[]float64{16., 7., 24., -8., 8., -1., 0., -1., 0., 0., 7.,
-5., 1., -5., 1., -7., 1., -7., -4.},
[]float64{-14., 2., 7., -13., -18., 3., 0., 0., -1., 0., 3.,
13., -6., 13., 12., -10., -6., -10., -28.},
[]float64{5., 0., -15., 12., -6., 17., 0., 0., 0., -1., 9.,
6., -6., 6., -7., -7., -6., -7., -11.}}
hdata := []float64{-3., 5., 12., -2., -14., -13., 10., 0., 0., 0., 68.,
-30., -19., -30., 99., 23., -19., 23., 10.}
// these reference values obtained from running cvxopt conelp.py example
xref := []float64{-1.22091525026262993, 0.09663323966626469, 3.57750155386611057}
sref := []float64{
0.00000172588537019, 13.35314040819201864,
94.28805677232460880, -53.44110853283719109,
18.97172963929198275, -75.32834138499130461,
10.00000013568614321, -1.22091525026262993,
0.09663323966626476, 3.57750155386611146,
44.05899318373081286, -58.82581769017131990,
4.26572401145687596, -58.82581769017131990,
124.10382738701650851, 40.46243652188705653,
4.26572401145687596, 40.46243652188705653,
47.17458693781828316}
zref := []float64{
0.09299833991484617, 0.00000001060210894,
0.23532251654806322, 0.13337937743566930,
-0.04734875722474355, 0.18800192060450249,
0.00000001245876667, 0.00000000007816348,
-0.00000000039584268, -0.00000000183463577,
0.12558704894101563, 0.08777794737598217,
-0.08664401207348003, 0.08777794737598217,
0.06135161787371416, -0.06055906182304811,
-0.08664401207348003, -0.06055906182304811,
0.05977675078191153}
c := matrix.FloatVector([]float64{-6., -4., -5.})
G := matrix.FloatMatrixFromTable(gdata)
h := matrix.FloatVector(hdata)
dims := sets.DSetNew("l", "q", "s")
dims.Set("l", []int{2})
dims.Set("q", []int{4, 4})
dims.Set("s", []int{3})
var solopts SolverOptions
solopts.MaxIter = 30
solopts.ShowProgress = false
sol, err := ConeLp(c, G, h, nil, nil, dims, &solopts, nil, 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()
}
} else {
t.Logf("status: %s\n", err)
t.Fail()
}
}
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())
}
// Internal CPL solver for CP and CLP problems. Everything is wrapped to proper interfaces
func cpl_solver(F ConvexVarProg, c MatrixVariable, G MatrixVarG, h *matrix.FloatMatrix,
A MatrixVarA, b MatrixVariable, dims *sets.DimensionSet, kktsolver KKTCpSolverVar,
solopts *SolverOptions, x0 MatrixVariable, mnl int) (sol *Solution, err error) {
const (
STEP = 0.99
BETA = 0.5
ALPHA = 0.01
EXPON = 3
MAX_RELAXED_ITERS = 8
)
var refinement int
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}
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.Refinement > 0 {
refinement = solopts.Refinement
} else {
refinement = 1
}
if solopts.MaxIter > 0 {
maxIter = solopts.MaxIter
}
if x0 == nil {
mnl, x0, err = F.F0()
if err != nil {
return
}
}
if c == nil {
err = errors.New("Must define objective.")
return
}
if h == nil {
h = matrix.FloatZeros(0, 1)
}
if dims == nil {
err = errors.New("Problem dimensions not defined.")
return
}
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
}
if G == nil {
err = errors.New("'G' must be non-nil MatrixG interface.")
return
}
fG := func(x, y MatrixVariable, alpha, beta float64, trans la.Option) error {
return G.Gf(x, y, alpha, beta, trans)
}
// Check A and set defaults if it is nil
if A == nil {
err = errors.New("'A' must be non-nil MatrixA interface.")
return
}
fA := func(x, y MatrixVariable, alpha, beta float64, trans la.Option) error {
return A.Af(x, y, alpha, beta, trans)
}
if b == nil {
err = errors.New("'b' must be non-nil MatrixVariable interface.")
return
}
if kktsolver == nil {
err = errors.New("nil kktsolver not allowed.")
return
}
x := x0.Copy()
y := b.Copy()
y.Scal(0.0)
z := matrix.FloatZeros(mnl+cdim, 1)
s := matrix.FloatZeros(mnl+cdim, 1)
ind := mnl + dims.At("l")[0]
z.SetIndexes(1.0, matrix.MakeIndexSet(0, ind, 1)...)
s.SetIndexes(1.0, matrix.MakeIndexSet(0, ind, 1)...)
for _, m := range dims.At("q") {
z.SetIndexes(1.0, ind)
s.SetIndexes(1.0, ind)
ind += m
}
for _, m := range dims.At("s") {
iset := matrix.MakeIndexSet(ind, ind+m*m, m+1)
z.SetIndexes(1.0, iset...)
s.SetIndexes(1.0, iset...)
ind += m * m
}
rx := x0.Copy()
ry := b.Copy()
dx := x.Copy()
dy := y.Copy()
rznl := matrix.FloatZeros(mnl, 1)
rzl := matrix.FloatZeros(cdim, 1)
dz := matrix.FloatZeros(mnl+cdim, 1)
ds := matrix.FloatZeros(mnl+cdim, 1)
lmbda := matrix.FloatZeros(mnl+cdim_diag, 1)
lmbdasq := matrix.FloatZeros(mnl+cdim_diag, 1)
sigs := matrix.FloatZeros(dims.Sum("s"), 1)
sigz := matrix.FloatZeros(dims.Sum("s"), 1)
dz2 := matrix.FloatZeros(mnl+cdim, 1)
ds2 := matrix.FloatZeros(mnl+cdim, 1)
newx := x.Copy()
newy := y.Copy()
newrx := x0.Copy()
newz := matrix.FloatZeros(mnl+cdim, 1)
news := matrix.FloatZeros(mnl+cdim, 1)
newrznl := matrix.FloatZeros(mnl, 1)
rx0 := rx.Copy()
ry0 := ry.Copy()
rznl0 := matrix.FloatZeros(mnl, 1)
rzl0 := matrix.FloatZeros(cdim, 1)
x0, dx0 := x.Copy(), dx.Copy()
y0, dy0 := y.Copy(), dy.Copy()
z0 := matrix.FloatZeros(mnl+cdim, 1)
dz0 := matrix.FloatZeros(mnl+cdim, 1)
dz20 := matrix.FloatZeros(mnl+cdim, 1)
s0 := matrix.FloatZeros(mnl+cdim, 1)
ds0 := matrix.FloatZeros(mnl+cdim, 1)
ds20 := matrix.FloatZeros(mnl+cdim, 1)
checkpnt.AddMatrixVar("z", z)
checkpnt.AddMatrixVar("s", s)
checkpnt.AddMatrixVar("dz", dz)
checkpnt.AddMatrixVar("ds", ds)
checkpnt.AddMatrixVar("rznl", rznl)
checkpnt.AddMatrixVar("rzl", rzl)
checkpnt.AddMatrixVar("lmbda", lmbda)
checkpnt.AddMatrixVar("lmbdasq", lmbdasq)
checkpnt.AddMatrixVar("z0", z0)
checkpnt.AddMatrixVar("dz0", dz0)
checkpnt.AddVerifiable("c", c)
checkpnt.AddVerifiable("x", x)
checkpnt.AddVerifiable("rx", rx)
checkpnt.AddVerifiable("dx", dx)
checkpnt.AddVerifiable("newrx", newrx)
checkpnt.AddVerifiable("newx", newx)
checkpnt.AddVerifiable("x0", x0)
checkpnt.AddVerifiable("dx0", dx0)
checkpnt.AddVerifiable("rx0", rx0)
checkpnt.AddVerifiable("y", y)
checkpnt.AddVerifiable("dy", dy)
W0 := sets.NewFloatSet("d", "di", "dnl", "dnli", "v", "r", "rti", "beta")
W0.Set("dnl", matrix.FloatZeros(mnl, 1))
W0.Set("dnli", matrix.FloatZeros(mnl, 1))
W0.Set("d", matrix.FloatZeros(dims.At("l")[0], 1))
W0.Set("di", matrix.FloatZeros(dims.At("l")[0], 1))
W0.Set("beta", matrix.FloatZeros(len(dims.At("q")), 1))
for _, n := range dims.At("q") {
W0.Append("v", matrix.FloatZeros(n, 1))
}
for _, n := range dims.At("s") {
W0.Append("r", matrix.FloatZeros(n, n))
W0.Append("rti", matrix.FloatZeros(n, n))
}
lmbda0 := matrix.FloatZeros(mnl+dims.Sum("l", "q", "s"), 1)
lmbdasq0 := matrix.FloatZeros(mnl+dims.Sum("l", "q", "s"), 1)
var f MatrixVariable = nil
var Df MatrixVarDf = nil
var H MatrixVarH = nil
var ws3, wz3, wz2l, wz2nl *matrix.FloatMatrix
var ws, wz, wz2, ws2 *matrix.FloatMatrix
var wx, wx2, wy, wy2 MatrixVariable
var gap, gap0, theta1, theta2, theta3, ts, tz, phi, phi0, mu, sigma, eta float64
var resx, resy, reszl, resznl, pcost, dcost, dres, pres, relgap float64
var resx0, resznl0, dres0, pres0 float64
var dsdz, dsdz0, step, step0, dphi, dphi0, sigma0, eta0 float64
var newresx, newresznl, newgap, newphi float64
var W *sets.FloatMatrixSet
var f3 KKTFuncVar
checkpnt.AddFloatVar("gap", &gap)
checkpnt.AddFloatVar("pcost", &pcost)
checkpnt.AddFloatVar("dcost", &dcost)
checkpnt.AddFloatVar("pres", &pres)
checkpnt.AddFloatVar("dres", &dres)
checkpnt.AddFloatVar("relgap", &relgap)
checkpnt.AddFloatVar("step", &step)
checkpnt.AddFloatVar("dsdz", &dsdz)
checkpnt.AddFloatVar("resx", &resx)
checkpnt.AddFloatVar("resy", &resy)
checkpnt.AddFloatVar("reszl", &reszl)
checkpnt.AddFloatVar("resznl", &resznl)
// Declare fDf and fH here, they bind to Df and H as they are already declared.
// ??really??
var fDf func(u, v MatrixVariable, alpha, beta float64, trans la.Option) error = nil
var fH func(u, v MatrixVariable, alpha, beta float64) error = nil
relaxed_iters := 0
for iters := 0; iters <= maxIter+1; iters++ {
checkpnt.MajorNext()
checkpnt.Check("loopstart", 10)
checkpnt.MinorPush(10)
if refinement != 0 || solopts.Debug {
f, Df, H, err = F.F2(x, matrix.FloatVector(z.FloatArray()[:mnl]))
fDf = func(u, v MatrixVariable, alpha, beta float64, trans la.Option) error {
return Df.Df(u, v, alpha, beta, trans)
}
fH = func(u, v MatrixVariable, alpha, beta float64) error {
return H.Hf(u, v, alpha, beta)
}
} else {
f, Df, err = F.F1(x)
fDf = func(u, v MatrixVariable, alpha, beta float64, trans la.Option) error {
return Df.Df(u, v, alpha, beta, trans)
}
}
checkpnt.MinorPop()
gap = sdot(s, z, dims, mnl)
// these are helpers, copies of parts of z,s
z_mnl := matrix.FloatVector(z.FloatArray()[:mnl])
z_mnl2 := matrix.FloatVector(z.FloatArray()[mnl:])
s_mnl := matrix.FloatVector(s.FloatArray()[:mnl])
s_mnl2 := matrix.FloatVector(s.FloatArray()[mnl:])
// rx = c + A'*y + Df'*z[:mnl] + G'*z[mnl:]
// -- y, rx MatrixArg
mCopy(c, rx)
fA(y, rx, 1.0, 1.0, la.OptTrans)
fDf(&matrixVar{z_mnl}, rx, 1.0, 1.0, la.OptTrans)
fG(&matrixVar{z_mnl2}, rx, 1.0, 1.0, la.OptTrans)
resx = math.Sqrt(rx.Dot(rx))
// rznl = s[:mnl] + f
blas.Copy(s_mnl, rznl)
blas.AxpyFloat(f.Matrix(), rznl, 1.0)
resznl = blas.Nrm2Float(rznl)
// rzl = s[mnl:] + G*x - h
blas.Copy(s_mnl2, rzl)
blas.AxpyFloat(h, rzl, -1.0)
fG(x, &matrixVar{rzl}, 1.0, 1.0, la.OptNoTrans)
reszl = snrm2(rzl, dims, 0)
// Statistics for stopping criteria
// pcost = c'*x
// dcost = c'*x + y'*(A*x-b) + znl'*f(x) + zl'*(G*x-h)
// = c'*x + y'*(A*x-b) + znl'*(f(x)+snl) + zl'*(G*x-h+sl)
// - z'*s
// = c'*x + y'*ry + znl'*rznl + zl'*rzl - gap
//pcost = blas.DotFloat(c, x)
pcost = c.Dot(x)
dcost = pcost + blas.DotFloat(y.Matrix(), ry.Matrix()) + blas.DotFloat(z_mnl, rznl)
dcost += sdot(z_mnl2, rzl, dims, 0) - gap
if pcost < 0.0 {
relgap = gap / -pcost
} else if dcost > 0.0 {
relgap = gap / dcost
} else {
relgap = math.NaN()
}
pres = math.Sqrt(resy*resy + resznl*resznl + reszl*reszl)
dres = resx
if iters == 0 {
resx0 = math.Max(1.0, resx)
resznl0 = math.Max(1.0, resznl)
pres0 = math.Max(1.0, pres)
dres0 = math.Max(1.0, dres)
gap0 = gap
theta1 = 1.0 / gap0
theta2 = 1.0 / resx0
theta3 = 1.0 / resznl0
}
phi = theta1*gap + theta2*resx + theta3*resznl
pres = pres / pres0
dres = dres / dres0
if solopts.ShowProgress {
if iters == 0 {
// some headers
fmt.Printf("% 10s% 12s% 10s% 8s% 7s\n",
"pcost", "dcost", "gap", "pres", "dres")
}
fmt.Printf("%2d: % 8.4e % 8.4e % 4.0e% 7.0e% 7.0e\n",
iters, pcost, dcost, gap, pres, dres)
}
checkpnt.Check("checkgap", 50)
// Stopping criteria
if (pres <= feasTolerance && dres <= feasTolerance &&
(gap <= absTolerance || (!math.IsNaN(relgap) && relgap <= relTolerance))) ||
iters == maxIter {
if iters == maxIter {
s := "Terminated (maximum number of iterations reached)"
if solopts.ShowProgress {
fmt.Printf(s + "\n")
}
err = errors.New(s)
sol.Status = Unknown
} else {
err = nil
sol.Status = Optimal
}
sol.Result = sets.NewFloatSet("x", "y", "znl", "zl", "snl", "sl")
sol.Result.Set("x", x.Matrix())
sol.Result.Set("y", y.Matrix())
sol.Result.Set("znl", matrix.FloatVector(z.FloatArray()[:mnl]))
sol.Result.Set("zl", matrix.FloatVector(z.FloatArray()[mnl:]))
sol.Result.Set("sl", matrix.FloatVector(s.FloatArray()[mnl:]))
sol.Result.Set("snl", matrix.FloatVector(s.FloatArray()[:mnl]))
sol.Gap = gap
sol.RelativeGap = relgap
sol.PrimalObjective = pcost
sol.DualObjective = dcost
sol.PrimalInfeasibility = pres
sol.DualInfeasibility = dres
sol.PrimalSlack = -ts
sol.DualSlack = -tz
return
}
// Compute initial scaling W:
//
// W * z = W^{-T} * s = lambda.
//
// lmbdasq = lambda o lambda
if iters == 0 {
W, _ = computeScaling(s, z, lmbda, dims, mnl)
checkpnt.AddScaleVar(W)
}
ssqr(lmbdasq, lmbda, dims, mnl)
checkpnt.Check("lmbdasq", 90)
// f3(x, y, z) solves
//
// [ H A' GG'*W^{-1} ] [ ux ] [ bx ]
// [ A 0 0 ] [ uy ] = [ by ].
// [ GG 0 -W' ] [ uz ] [ bz ]
//
// On entry, x, y, z contain bx, by, bz.
// On exit, they contain ux, uy, uz.
checkpnt.MinorPush(95)
f3, err = kktsolver(W, x, z_mnl)
checkpnt.MinorPop()
checkpnt.Check("f3", 100)
if err != nil {
// ?? z_mnl is really copy of z[:mnl] ... should we copy here back to z??
singular_kkt_matrix := false
if iters == 0 {
err = errors.New("Rank(A) < p or Rank([H(x); A; Df(x); G] < n")
return
} else if relaxed_iters > 0 && relaxed_iters < MAX_RELAXED_ITERS {
// The arithmetic error may be caused by a relaxed line
// search in the previous iteration. Therefore we restore
// the last saved state and require a standard line search.
phi, gap = phi0, gap0
mu = gap / float64(mnl+dims.Sum("l", "s")+len(dims.At("q")))
blas.Copy(W0.At("dnl")[0], W.At("dnl")[0])
blas.Copy(W0.At("dnli")[0], W.At("dnli")[0])
blas.Copy(W0.At("d")[0], W.At("d")[0])
blas.Copy(W0.At("di")[0], W.At("di")[0])
blas.Copy(W0.At("beta")[0], W.At("beta")[0])
for k, _ := range dims.At("q") {
blas.Copy(W0.At("v")[k], W.At("v")[k])
}
for k, _ := range dims.At("s") {
blas.Copy(W0.At("r")[k], W.At("r")[k])
blas.Copy(W0.At("rti")[k], W.At("rti")[k])
}
//blas.Copy(x0, x)
//x0.CopyTo(x)
mCopy(x0, x)
//blas.Copy(y0, y)
mCopy(y0, y)
blas.Copy(s0, s)
blas.Copy(z0, z)
blas.Copy(lmbda0, lmbda)
blas.Copy(lmbdasq0, lmbdasq) // ???
//blas.Copy(rx0, rx)
//rx0.CopyTo(rx)
mCopy(rx0, rx)
//blas.Copy(ry0, ry)
mCopy(ry0, ry)
//resx = math.Sqrt(blas.DotFloat(rx, rx))
resx = math.Sqrt(rx.Dot(rx))
blas.Copy(rznl0, rznl)
blas.Copy(rzl0, rzl)
resznl = blas.Nrm2Float(rznl)
relaxed_iters = -1
// How about z_mnl here???
checkpnt.MinorPush(120)
f3, err = kktsolver(W, x, z_mnl)
checkpnt.MinorPop()
if err != nil {
singular_kkt_matrix = true
}
} else {
singular_kkt_matrix = true
}
if singular_kkt_matrix {
msg := "Terminated (singular KKT matrix)."
if solopts.ShowProgress {
fmt.Printf(msg + "\n")
}
zl := matrix.FloatVector(z.FloatArray()[mnl:])
sl := matrix.FloatVector(s.FloatArray()[mnl:])
ind := dims.Sum("l", "q")
for _, m := range dims.At("s") {
symm(sl, m, ind)
symm(zl, m, ind)
ind += m * m
}
ts, _ = maxStep(s, dims, mnl, nil)
tz, _ = maxStep(z, dims, mnl, nil)
err = errors.New(msg)
sol.Status = Unknown
sol.Result = sets.NewFloatSet("x", "y", "znl", "zl", "snl", "sl")
sol.Result.Set("x", x.Matrix())
sol.Result.Set("y", y.Matrix())
sol.Result.Set("znl", matrix.FloatVector(z.FloatArray()[:mnl]))
sol.Result.Set("zl", zl)
sol.Result.Set("sl", sl)
sol.Result.Set("snl", matrix.FloatVector(s.FloatArray()[:mnl]))
sol.Gap = gap
sol.RelativeGap = relgap
sol.PrimalObjective = pcost
sol.DualObjective = dcost
sol.PrimalInfeasibility = pres
sol.DualInfeasibility = dres
sol.PrimalSlack = -ts
sol.DualSlack = -tz
return
}
}
// f4_no_ir(x, y, z, s) solves
//
// [ 0 ] [ H A' GG' ] [ ux ] [ bx ]
// [ 0 ] + [ A 0 0 ] [ uy ] = [ by ]
// [ W'*us ] [ GG 0 0 ] [ W^{-1}*uz ] [ bz ]
//
// lmbda o (uz + us) = bs.
//
// On entry, x, y, z, x, contain bx, by, bz, bs.
// On exit, they contain ux, uy, uz, us.
if iters == 0 {
ws3 = matrix.FloatZeros(mnl+cdim, 1)
wz3 = matrix.FloatZeros(mnl+cdim, 1)
checkpnt.AddMatrixVar("ws3", ws3)
checkpnt.AddMatrixVar("wz3", wz3)
}
f4_no_ir := func(x, y MatrixVariable, z, s *matrix.FloatMatrix) (err error) {
// Solve
//
// [ H A' GG' ] [ ux ] [ bx ]
// [ A 0 0 ] [ uy ] = [ by ]
// [ GG 0 -W'*W ] [ W^{-1}*uz ] [ bz - W'*(lmbda o\ bs) ]
//
// us = lmbda o\ bs - uz.
err = nil
// s := lmbda o\ s
// = lmbda o\ bs
sinv(s, lmbda, dims, mnl)
// z := z - W'*s
// = bz - W' * (lambda o\ bs)
blas.Copy(s, ws3)
scale(ws3, W, true, false)
blas.AxpyFloat(ws3, z, -1.0)
// Solve for ux, uy, uz
err = f3(x, y, z)
// s := s - z
// = lambda o\ bs - z.
blas.AxpyFloat(z, s, -1.0)
return
}
if iters == 0 {
wz2nl = matrix.FloatZeros(mnl, 1)
wz2l = matrix.FloatZeros(cdim, 1)
checkpnt.AddMatrixVar("wz2nl", wz2nl)
checkpnt.AddMatrixVar("wz2l", wz2l)
}
res := func(ux, uy MatrixVariable, uz, us *matrix.FloatMatrix, vx, vy MatrixVariable, vz, vs *matrix.FloatMatrix) (err error) {
// Evaluates residuals in Newton equations:
//
// [ vx ] [ 0 ] [ H A' GG' ] [ ux ]
// [ vy ] -= [ 0 ] + [ A 0 0 ] [ uy ]
// [ vz ] [ W'*us ] [ GG 0 0 ] [ W^{-1}*uz ]
//
// vs -= lmbda o (uz + us).
err = nil
minor := checkpnt.MinorTop()
// vx := vx - H*ux - A'*uy - GG'*W^{-1}*uz
fH(ux, vx, -1.0, 1.0)
fA(uy, vx, -1.0, 1.0, la.OptTrans)
blas.Copy(uz, wz3)
scale(wz3, W, false, true)
wz3_nl := matrix.FloatVector(wz3.FloatArray()[:mnl])
wz3_l := matrix.FloatVector(wz3.FloatArray()[mnl:])
fDf(&matrixVar{wz3_nl}, vx, -1.0, 1.0, la.OptTrans)
fG(&matrixVar{wz3_l}, vx, -1.0, 1.0, la.OptTrans)
checkpnt.Check("10res", minor+10)
// vy := vy - A*ux
fA(ux, vy, -1.0, 1.0, la.OptNoTrans)
// vz := vz - W'*us - GG*ux
err = fDf(ux, &matrixVar{wz2nl}, 1.0, 0.0, la.OptNoTrans)
checkpnt.Check("15res", minor+10)
blas.AxpyFloat(wz2nl, vz, -1.0)
fG(ux, &matrixVar{wz2l}, 1.0, 0.0, la.OptNoTrans)
checkpnt.Check("20res", minor+10)
blas.AxpyFloat(wz2l, vz, -1.0, &la.IOpt{"offsety", mnl})
blas.Copy(us, ws3)
scale(ws3, W, true, false)
blas.AxpyFloat(ws3, vz, -1.0)
checkpnt.Check("30res", minor+10)
// vs -= lmbda o (uz + us)
blas.Copy(us, ws3)
blas.AxpyFloat(uz, ws3, 1.0)
sprod(ws3, lmbda, dims, mnl, &la.SOpt{"diag", "D"})
blas.AxpyFloat(ws3, vs, -1.0)
checkpnt.Check("90res", minor+10)
return
}
// f4(x, y, z, s) solves the same system as f4_no_ir, but applies
// iterative refinement.
if iters == 0 {
if refinement > 0 || solopts.Debug {
wx = c.Copy()
wy = b.Copy()
wz = z.Copy()
ws = s.Copy()
checkpnt.AddVerifiable("wx", wx)
checkpnt.AddMatrixVar("ws", ws)
checkpnt.AddMatrixVar("wz", wz)
}
if refinement > 0 {
wx2 = c.Copy()
wy2 = b.Copy()
wz2 = matrix.FloatZeros(mnl+cdim, 1)
ws2 = matrix.FloatZeros(mnl+cdim, 1)
checkpnt.AddVerifiable("wx2", wx2)
checkpnt.AddMatrixVar("ws2", ws2)
checkpnt.AddMatrixVar("wz2", wz2)
}
}
f4 := func(x, y MatrixVariable, z, s *matrix.FloatMatrix) (err error) {
if refinement > 0 || solopts.Debug {
mCopy(x, wx)
mCopy(y, wy)
blas.Copy(z, wz)
blas.Copy(s, ws)
}
minor := checkpnt.MinorTop()
checkpnt.Check("0_f4", minor+100)
checkpnt.MinorPush(minor + 100)
err = f4_no_ir(x, y, z, s)
checkpnt.MinorPop()
checkpnt.Check("1_f4", minor+200)
for i := 0; i < refinement; i++ {
mCopy(wx, wx2)
mCopy(wy, wy2)
blas.Copy(wz, wz2)
blas.Copy(ws, ws2)
checkpnt.Check("2_f4", minor+(1+i)*200)
checkpnt.MinorPush(minor + (1+i)*200)
res(x, y, z, s, wx2, wy2, wz2, ws2)
checkpnt.MinorPop()
checkpnt.Check("3_f4", minor+(1+i)*200+100)
err = f4_no_ir(wx2, wy2, wz2, ws2)
checkpnt.MinorPop()
checkpnt.Check("4_f4", minor+(1+i)*200+199)
wx2.Axpy(x, 1.0)
wy2.Axpy(y, 1.0)
blas.AxpyFloat(wz2, z, 1.0)
blas.AxpyFloat(ws2, s, 1.0)
}
if solopts.Debug {
res(x, y, z, s, wx, wy, wz, ws)
fmt.Printf("KKT residuals:\n")
}
return
}
sigma, eta = 0.0, 0.0
for i := 0; i < 2; i++ {
minor := (i + 2) * 1000
checkpnt.MinorPush(minor)
checkpnt.Check("loop01", minor)
// Solve
//
// [ 0 ] [ H A' GG' ] [ dx ]
// [ 0 ] + [ A 0 0 ] [ dy ] = -(1 - eta)*r
// [ W'*ds ] [ GG 0 0 ] [ W^{-1}*dz ]
//
// lmbda o (dz + ds) = -lmbda o lmbda + sigma*mu*e.
//
mu = gap / float64(mnl+dims.Sum("l", "s")+len(dims.At("q")))
blas.ScalFloat(ds, 0.0)
blas.AxpyFloat(lmbdasq, ds, -1.0, &la.IOpt{"n", mnl + dims.Sum("l", "q")})
ind = mnl + dims.At("l")[0]
iset := matrix.MakeIndexSet(0, ind, 1)
ds.Add(sigma*mu, iset...)
for _, m := range dims.At("q") {
ds.Add(sigma*mu, ind)
ind += m
}
ind2 := ind
for _, m := range dims.At("s") {
blas.AxpyFloat(lmbdasq, ds, -1.0, &la.IOpt{"n", m}, &la.IOpt{"offsetx", ind2},
&la.IOpt{"offsety", ind}, &la.IOpt{"incy", m + 1})
ds.Add(sigma*mu, matrix.MakeIndexSet(ind, ind+m*m, m+1)...)
ind += m * m
ind2 += m
}
dx.Scal(0.0)
rx.Axpy(dx, -1.0+eta)
dy.Scal(0.0)
ry.Axpy(dy, -1.0+eta)
dz.Scale(0.0)
blas.AxpyFloat(rznl, dz, -1.0+eta)
blas.AxpyFloat(rzl, dz, -1.0+eta, &la.IOpt{"offsety", mnl})
//fmt.Printf("dx=\n%v\n", dx)
//fmt.Printf("dz=\n%v\n", dz.ToString("%.7f"))
//fmt.Printf("ds=\n%v\n", ds.ToString("%.7f"))
checkpnt.Check("pref4", minor)
checkpnt.MinorPush(minor)
err = f4(dx, dy, dz, ds)
if err != nil {
if iters == 0 {
s := fmt.Sprintf("Rank(A) < p or Rank([H(x); A; Df(x); G] < n (%s)", err)
err = errors.New(s)
return
}
msg := "Terminated (singular KKT matrix)."
if solopts.ShowProgress {
fmt.Printf(msg + "\n")
}
zl := matrix.FloatVector(z.FloatArray()[mnl:])
sl := matrix.FloatVector(s.FloatArray()[mnl:])
ind := dims.Sum("l", "q")
for _, m := range dims.At("s") {
symm(sl, m, ind)
symm(zl, m, ind)
ind += m * m
}
ts, _ = maxStep(s, dims, mnl, nil)
tz, _ = maxStep(z, dims, mnl, nil)
err = errors.New(msg)
sol.Status = Unknown
sol.Result = sets.NewFloatSet("x", "y", "znl", "zl", "snl", "sl")
sol.Result.Set("x", x.Matrix())
sol.Result.Set("y", y.Matrix())
sol.Result.Set("znl", matrix.FloatVector(z.FloatArray()[:mnl]))
sol.Result.Set("zl", zl)
sol.Result.Set("sl", sl)
sol.Result.Set("snl", matrix.FloatVector(s.FloatArray()[:mnl]))
sol.Gap = gap
sol.RelativeGap = relgap
sol.PrimalObjective = pcost
sol.DualObjective = dcost
sol.PrimalInfeasibility = pres
sol.DualInfeasibility = dres
sol.PrimalSlack = -ts
sol.DualSlack = -tz
return
}
checkpnt.MinorPop()
checkpnt.Check("postf4", minor+400)
// Inner product ds'*dz and unscaled steps are needed in the
// line search.
dsdz = sdot(ds, dz, dims, mnl)
blas.Copy(dz, dz2)
scale(dz2, W, false, true)
blas.Copy(ds, ds2)
scale(ds2, W, true, false)
checkpnt.Check("dsdz", minor+400)
// Maximum steps to boundary.
//
// Also compute the eigenvalue decomposition of 's' blocks in
// ds, dz. The eigenvectors Qs, Qz are stored in ds, dz.
// The eigenvalues are stored in sigs, sigz.
scale2(lmbda, ds, dims, mnl, false)
ts, _ = maxStep(ds, dims, mnl, sigs)
scale2(lmbda, dz, dims, mnl, false)
tz, _ = maxStep(dz, dims, mnl, sigz)
t := maxvec([]float64{0.0, ts, tz})
if t == 0 {
step = 1.0
} else {
step = math.Min(1.0, STEP/t)
}
checkpnt.Check("maxstep", minor+400)
var newDf MatrixVarDf = nil
var newf MatrixVariable = nil
// Backtrack until newx is in domain of f.
backtrack := true
for backtrack {
mCopy(x, newx)
dx.Axpy(newx, step)
newf, newDf, err = F.F1(newx)
if newf != nil {
backtrack = false
} else {
step *= BETA
}
}
// Merit function
//
// phi = theta1 * gap + theta2 * norm(rx) +
// theta3 * norm(rznl)
//
// and its directional derivative dphi.
phi = theta1*gap + theta2*resx + theta3*resznl
if i == 0 {
dphi = -phi
} else {
dphi = -theta1*(1-sigma)*gap - theta2*(1-eta)*resx - theta3*(1-eta)*resznl
}
var newfDf func(x, y MatrixVariable, a, b float64, trans la.Option) error
// Line search
backtrack = true
for backtrack {
mCopy(x, newx)
dx.Axpy(newx, step)
mCopy(y, newy)
dy.Axpy(newy, step)
blas.Copy(z, newz)
blas.AxpyFloat(dz2, newz, step)
blas.Copy(s, news)
blas.AxpyFloat(ds2, news, step)
newf, newDf, err = F.F1(newx)
newfDf = func(u, v MatrixVariable, a, b float64, trans la.Option) error {
return newDf.Df(u, v, a, b, trans)
}
// newrx = c + A'*newy + newDf'*newz[:mnl] + G'*newz[mnl:]
newz_mnl := matrix.FloatVector(newz.FloatArray()[:mnl])
newz_ml := matrix.FloatVector(newz.FloatArray()[mnl:])
//blas.Copy(c, newrx)
//c.CopyTo(newrx)
mCopy(c, newrx)
fA(newy, newrx, 1.0, 1.0, la.OptTrans)
newfDf(&matrixVar{newz_mnl}, newrx, 1.0, 1.0, la.OptTrans)
fG(&matrixVar{newz_ml}, newrx, 1.0, 1.0, la.OptTrans)
newresx = math.Sqrt(newrx.Dot(newrx))
// newrznl = news[:mnl] + newf
news_mnl := matrix.FloatVector(news.FloatArray()[:mnl])
//news_ml := matrix.FloatVector(news.FloatArray()[mnl:])
blas.Copy(news_mnl, newrznl)
blas.AxpyFloat(newf.Matrix(), newrznl, 1.0)
newresznl = blas.Nrm2Float(newrznl)
newgap = (1.0-(1.0-sigma)*step)*gap + step*step*dsdz
newphi = theta1*newgap + theta2*newresx + theta3*newresznl
if i == 0 {
if newgap <= (1.0-ALPHA*step)*gap &&
(relaxed_iters > 0 && relaxed_iters < MAX_RELAXED_ITERS ||
newphi <= phi+ALPHA*step*dphi) {
backtrack = false
sigma = math.Min(newgap/gap, math.Pow((newgap/gap), EXPON))
//fmt.Printf("break 1: sigma=%.7f\n", sigma)
eta = 0.0
} else {
step *= BETA
}
} else {
if relaxed_iters == -1 || (relaxed_iters == 0 && MAX_RELAXED_ITERS == 0) {
// Do a standard line search.
if newphi <= phi+ALPHA*step*dphi {
relaxed_iters = 0
backtrack = false
//fmt.Printf("break 2 : newphi=%.7f\n", newphi)
} else {
step *= BETA
}
} else if relaxed_iters == 0 && relaxed_iters < MAX_RELAXED_ITERS {
if newphi <= phi+ALPHA*step*dphi {
// Relaxed l.s. gives sufficient decrease.
relaxed_iters = 0
} else {
// Save state.
phi0, dphi0, gap0 = phi, dphi, gap
step0 = step
blas.Copy(W.At("dnl")[0], W0.At("dnl")[0])
blas.Copy(W.At("dnli")[0], W0.At("dnli")[0])
blas.Copy(W.At("d")[0], W0.At("d")[0])
blas.Copy(W.At("di")[0], W0.At("di")[0])
blas.Copy(W.At("beta")[0], W0.At("beta")[0])
for k, _ := range dims.At("q") {
blas.Copy(W.At("v")[k], W0.At("v")[k])
}
for k, _ := range dims.At("s") {
blas.Copy(W.At("r")[k], W0.At("r")[k])
blas.Copy(W.At("rti")[k], W0.At("rti")[k])
}
mCopy(x, x0)
mCopy(y, y0)
mCopy(dx, dx0)
mCopy(dy, dy0)
blas.Copy(s, s0)
blas.Copy(z, z0)
blas.Copy(ds, ds0)
blas.Copy(dz, dz0)
blas.Copy(ds2, ds20)
blas.Copy(dz2, dz20)
blas.Copy(lmbda, lmbda0)
blas.Copy(lmbdasq, lmbdasq0) // ???
mCopy(rx, rx0)
mCopy(ry, ry0)
blas.Copy(rznl, rznl0)
blas.Copy(rzl, rzl0)
dsdz0 = dsdz
sigma0, eta0 = sigma, eta
relaxed_iters = 1
}
backtrack = false
//fmt.Printf("break 3 : newphi=%.7f\n", newphi)
} else if relaxed_iters >= 0 && relaxed_iters < MAX_RELAXED_ITERS &&
MAX_RELAXED_ITERS > 0 {
if newphi <= phi0+ALPHA*step0*dphi0 {
// Relaxed l.s. gives sufficient decrease.
relaxed_iters = 0
} else {
// Relaxed line search
relaxed_iters += 1
}
backtrack = false
//fmt.Printf("break 4 : newphi=%.7f\n", newphi)
} else if relaxed_iters == MAX_RELAXED_ITERS && MAX_RELAXED_ITERS > 0 {
if newphi <= phi0+ALPHA*step0*dphi0 {
// Series of relaxed line searches ends
// with sufficient decrease w.r.t. phi0.
backtrack = false
relaxed_iters = 0
//fmt.Printf("break 5 : newphi=%.7f\n", newphi)
} else if newphi >= phi0 {
// Resume last saved line search
phi, dphi, gap = phi0, dphi0, gap0
step = step0
blas.Copy(W0.At("dnl")[0], W.At("dnl")[0])
blas.Copy(W0.At("dnli")[0], W.At("dnli")[0])
blas.Copy(W0.At("d")[0], W.At("d")[0])
blas.Copy(W0.At("di")[0], W.At("di")[0])
blas.Copy(W0.At("beta")[0], W.At("beta")[0])
for k, _ := range dims.At("q") {
blas.Copy(W0.At("v")[k], W.At("v")[k])
}
for k, _ := range dims.At("s") {
blas.Copy(W0.At("r")[k], W.At("r")[k])
blas.Copy(W0.At("rti")[k], W.At("rti")[k])
}
mCopy(x, x0)
mCopy(y, y0)
mCopy(dx, dx0)
mCopy(dy, dy0)
blas.Copy(s, s0)
blas.Copy(z, z0)
blas.Copy(ds2, ds20)
blas.Copy(dz2, dz20)
blas.Copy(lmbda, lmbda0)
blas.Copy(lmbdasq, lmbdasq0) // ???
mCopy(rx, rx0)
mCopy(ry, ry0)
blas.Copy(rznl, rznl0)
blas.Copy(rzl, rzl0)
dsdz = dsdz0
sigma, eta = sigma0, eta0
relaxed_iters = -1
} else if newphi <= phi+ALPHA*step*dphi {
// Series of relaxed line searches ends
// with sufficient decrease w.r.t. phi0.
backtrack = false
relaxed_iters = -1
//fmt.Printf("break 6 : newphi=%.7f\n", newphi)
}
}
}
} // end of line search
checkpnt.Check("eol", minor+900)
} // end for [0,1]
// Update x, y
dx.Axpy(x, step)
dy.Axpy(y, step)
checkpnt.Check("updatexy", 5000)
// Replace nonlinear, '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 nonlinear, 'l' and 'q' blocks.
// dz := e + step*dz for nonlinear, 'l' and 'q' blocks.
blas.ScalFloat(ds, step, &la.IOpt{"n", mnl + dims.Sum("l", "q")})
blas.ScalFloat(dz, step, &la.IOpt{"n", mnl + dims.Sum("l", "q")})
ind := mnl + 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
}
checkpnt.Check("updatedsdz", 5100)
// 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, mnl, true)
scale2(lmbda, dz, dims, mnl, true)
checkpnt.Check("scale2", 5200)
// 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", mnl + qdimsum})
blas.TbsvFloat(lmbda, sigz, &la.IOpt{"n", sdimsum}, &la.IOpt{"k", 0},
&la.IOpt{"lda", 1}, &la.IOpt{"offseta", mnl + qdimsum})
checkpnt.Check("sigs", 5300)
ind2 := mnl + 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("scaling", 5400)
err = updateScaling(W, lmbda, ds, dz)
checkpnt.Check("postscaling", 5500)
// Unscale s, z, tau, kappa (unscaled variables are used only to
// compute feasibility residuals).
ind = mnl + 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)
checkpnt.Check("unscale_s", 5600)
ind = mnl + 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)
checkpnt.Check("unscale_z", 5700)
gap = blas.DotFloat(lmbda, lmbda)
}
return
}
