Beispiel #1
0
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)
}
Beispiel #2
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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()
	}
}
Beispiel #3
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// 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)
}
Beispiel #4
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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)
	}
}
Beispiel #5
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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
}
Beispiel #6
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// 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
}
Beispiel #7
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// Here problem is already translated to epigraph format except original convex problem.
// We wrap it and create special CP epigraph kktsolver.
func cp_problem(F ConvexProg, c MatrixVariable, G MatrixVarG, h *matrix.FloatMatrix, A MatrixVarA,
	b MatrixVariable, dims *sets.DimensionSet, kktsolver KKTCpSolver,
	solopts *SolverOptions, x0 *matrix.FloatMatrix, mnl int) (sol *Solution, err error) {

	err = nil

	F_e := &cpProg{F}

	//mx0 := newEpigraph(x0, 0.0)
	cdim := dims.Sum("l", "q") + dims.SumSquared("s")
	ux := x0.Copy()
	uz := matrix.FloatZeros(mnl+cdim, 1)

	kktsolver_e := func(W *sets.FloatMatrixSet, xa MatrixVariable, znl *matrix.FloatMatrix) (KKTFuncVar, error) {
		x, x_ok := xa.(*epigraph)
		_ = x_ok
		We := W.Copy()
		// dnl is matrix
		dnl := W.At("dnl")[0]
		dnli := W.At("dnli")[0]
		We.Set("dnl", matrix.FloatVector(dnl.FloatArray()[1:]))
		We.Set("dnli", matrix.FloatVector(dnli.FloatArray()[1:]))
		g, err := kktsolver(We, x.m(), znl)
		_, Df, _ := F.F1(x.m())
		gradf0 := Df.GetRow(0, nil).Transpose()

		solve := func(xa, ya MatrixVariable, z *matrix.FloatMatrix) (err error) {
			x, x_ok := xa.(*epigraph)
			_ = x_ok // TODO: remove or use x_ok
			y := ya.Matrix()
			err = nil
			a := z.GetIndex(0)
			blas.Copy(x.m(), ux)
			blas.AxpyFloat(gradf0, ux, x.t())
			blas.Copy(z, uz, &la.IOpt{"offsetx", 1})
			err = g(ux, y, uz)
			z.SetIndex(0, -x.t()*dnl.GetIndex(0))
			blas.Copy(uz, z, &la.IOpt{"offsety", 1})
			blas.Copy(ux, x.m())
			val := blas.DotFloat(gradf0, x.m()) + dnl.GetIndex(0)*dnl.GetIndex(0)*x.t() - a
			x.set(val)
			return
		}
		return solve, err
	}
	return cpl_solver(F_e, c, G, h, A, b, dims, kktsolver_e, solopts, nil, mnl)
}
Beispiel #8
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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)
}
Beispiel #9
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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
}
Beispiel #10
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// 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()
	}
}
Beispiel #11
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// 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
}
Beispiel #12
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// 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
}
Beispiel #13
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// 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()
	}
}
Beispiel #14
0
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
}
Beispiel #15
0
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)
	}
}
Beispiel #16
0
// 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)
}
Beispiel #17
0
// 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
}
Beispiel #18
0
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()
	}
}
Beispiel #19
0
func TestMcSdp(t *testing.T) {
	dataref := []float64{
		0.13391860811867590, -0.08810099183143839, 1.67440840625377385,
		0.73364110729257948, 0.99752463160201232, -1.27750208100276641,
		-2.39671528273320744, -0.67928016472921104, -0.03909131843358723,
		0.89355554552081806, -0.01764779458978329, -1.29655690877732033,
		-0.66798050600863512, 0.18170877830799323, 0.83105079034069296,
		-0.54824847139682842, -0.63803150269339892, 0.00708853169542829,
		-0.66985369437758802, -0.82776233138694755, 0.61250003036052025,
		-0.36758841015776106, -0.28949340911906524, -0.91687467081464402,
		-0.58380545879822354, 0.06461529015869494, 0.04674385977341894,
		-0.75072697866800864, -0.14423268736469502, 1.47556079692186803,
		-0.20647897491996936, -0.26069727887528138, -0.13821797189084586,
		-2.54339721705159461, 0.57741853670968823, -0.03610986310676272,
		-0.22617256815925429, 0.71500221712914425, 1.57726688654131597,
		-0.39227386148741172, 1.18277085579223740, 1.79311368741147459,
		-0.73134596730591273, -0.62981768127016369, 2.07069457080308439,
		-0.90300600743388137, 1.73554553761132668, 1.38308886012672638,
		-0.37536663987692281, -0.30431019951136606, 1.25357124882638882,
		-0.27384517924722629, -0.04632938142394107, 0.79898247010790735,
		0.53995588924910287, 0.08585033894586751, 1.35529122074053832,
		0.01385086083133875, 0.75136369502320788, -1.57490917820568810,
		-0.23737825305680132, 0.33649059262790887, -0.07959967279201274,
		-0.54087224229887154, -1.38043394277880926, 1.71655186027439033,
		0.41811180400013570, 1.52495658744607199, 0.68647633127251673,
		-0.58855597085742983, 1.31674790938058739, -0.83298414482940919,
		0.77396029737830319, -0.80351540646387032, -0.08543027502251639,
		1.49619789524058855, 0.39776679351920241, 3.33842986686760090,
		1.80093502635427316, 1.75627861060973078, 0.66747365962131211,
		-1.05016810484723888, 0.32937053624566975, 0.45508103845913089,
		-2.34700732048193306, -0.61389923742031549, -2.16180058868422442,
		-1.00134312023216387, -0.46226762826081341, -0.81756106137679618,
		0.18439203820434547, -0.39491697602688680, -1.66955730897081911,
		-0.94514784362504667, 0.00356999348571242, 1.15573946640829073,
		1.87743135406613315, 0.11219893028802071, 0.18121323774935597,
		1.27830445029226136}

	xref := []float64{
		4.35021720027799841, 3.05093154317696458, 3.81967245723734372,
		4.46546919954538968, 1.84870016923725555, 1.98581804497831516,
		4.10446362983979540, 0.71734387468946914, 4.36803763631339592, 4.18804903479807233}

	zref := []float64{
		1.00000000000000022, 0.85303260971097450, -0.85020234203223444,
		-0.64630863726711674, -0.66211228030949409, 0.12515757728046226,
		0.96725545921055622, -0.37698782755909727, 0.39233601196202428,
		0.43202712322170089, 0.85303260971097450, 0.99999999999999956,
		-0.99998464240308171, -0.94953899113609297, -0.17372147858022127,
		0.62451665675716073, 0.69265128272781040, -0.80493649049792482,
		0.81469110300198433, 0.83917543899780545, -0.85020234203223444,
		-0.99998464240308171, 1.00000000000000089, 0.95121850038903877,
		0.16840142730281188, -0.62872513058587987, -0.68874630140860726,
		0.80812857148627615, -0.81781005005790097, -0.84210005596350634,
		-0.64630863726711674, -0.94953899113609297, 0.95121850038903877,
		1.00000000000000000, -0.14392344681149352, -0.83796540396053343,
		-0.43147384025661650, 0.95042502132018603, -0.95546406276523821,
		-0.96741055070299309, -0.66211228030949409, -0.17372147858022127,
		0.16840142730281188, -0.14392344681149352, 1.00000000000000067,
		0.66064328265023031, -0.83063373455540346, -0.44450355615883147,
		0.42954780821049410, 0.38980770530806869, 0.12515757728046226,
		0.62451665675716073, -0.62872513058587987, -0.83796540396053343,
		0.66064328265023031, 0.99999999999999978, -0.13074907207216546,
		-0.96611746250486030, 0.96169189357427165, 0.94884018282016180,
		0.96725545921055622, 0.69265128272781040, -0.68874630140860726,
		-0.43147384025661650, -0.83063373455540346, -0.13074907207216546,
		1.00000000000000044, -0.12956585354159514, 0.14603486320665809,
		0.18898493355529175, -0.37698782755909727, -0.80493649049792482,
		0.80812857148627615, 0.95042502132018603, -0.44450355615883147,
		-0.96611746250486030, -0.12956585354159514, 1.00000000000000067,
		-0.99986146360473316, -0.99818853872682889, 0.39233601196202428,
		0.81469110300198433, -0.81781005005790097, -0.95546406276523821,
		0.42954780821049410, 0.96169189357427165, 0.14603486320665809,
		-0.99986146360473316, 1.00000000000000022, 0.99905052020024310,
		0.43202712322170089, 0.83917543899780545, -0.84210005596350634,
		-0.96741055070299309, 0.38980770530806869, 0.94884018282016180,
		0.18898493355529175, -0.99818853872682889, 0.99905052020024310, 0.99999999999999933}

	data := matrix.FloatNew(10, 10, dataref)

	sol, err := mcsdp(data)
	if sol != nil && sol.Status == Optimal {
		fail := false
		x := sol.Result.At("x")[0]
		z := sol.Result.At("z")[0]
		//matrix.Reshape(z, data.Rows(), data.Rows())
		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)
			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: %v\n", err)
		t.Fail()
	}
}
Beispiel #20
0
func updateScaling(W *sets.FloatMatrixSet, lmbda, s, z *matrix.FloatMatrix) (err error) {
	err = nil
	var stmp, ztmp *matrix.FloatMatrix
	/*
	   Nonlinear and 'l' blocks

	      d :=  d .* sqrt( s ./ z )
	      lmbda := lmbda .* sqrt(s) .* sqrt(z)
	*/
	mnl := 0
	dnlset := W.At("dnl")
	dnliset := W.At("dnli")
	dset := W.At("d")
	diset := W.At("di")
	beta := W.At("beta")[0]
	if dnlset != nil && dnlset[0].NumElements() > 0 {
		mnl = dnlset[0].NumElements()
	}
	ml := dset[0].NumElements()
	m := mnl + ml
	//fmt.Printf("ml=%d, mnl=%d, m=%d'n", ml, mnl, m)

	stmp = matrix.FloatVector(s.FloatArray()[:m])
	stmp.Apply(math.Sqrt)
	s.SetIndexesFromArray(stmp.FloatArray(), matrix.MakeIndexSet(0, m, 1)...)

	ztmp = matrix.FloatVector(z.FloatArray()[:m])
	ztmp.Apply(math.Sqrt)
	z.SetIndexesFromArray(ztmp.FloatArray(), matrix.MakeIndexSet(0, m, 1)...)

	// d := d .* s .* z
	if len(dnlset) > 0 {
		blas.TbmvFloat(s, dnlset[0], &la_.IOpt{"n", mnl}, &la_.IOpt{"k", 0}, &la_.IOpt{"lda", 1})
		blas.TbsvFloat(z, dnlset[0], &la_.IOpt{"n", mnl}, &la_.IOpt{"k", 0}, &la_.IOpt{"lda", 1})
		//dnliset[0].Apply(dnlset[0], func(a float64)float64 { return 1.0/a})
		//--dnliset[0] = matrix.Inv(dnlset[0])
		matrix.Set(dnliset[0], dnlset[0])
		dnliset[0].Inv()
	}
	blas.TbmvFloat(s, dset[0], &la_.IOpt{"n", ml},
		&la_.IOpt{"k", 0}, &la_.IOpt{"lda", 1}, &la_.IOpt{"offseta", mnl})
	blas.TbsvFloat(z, dset[0], &la_.IOpt{"n", ml},
		&la_.IOpt{"k", 0}, &la_.IOpt{"lda", 1}, &la_.IOpt{"offseta", mnl})
	//diset[0].Apply(dset[0], func(a float64)float64 { return 1.0/a})
	//--diset[0] = matrix.Inv(dset[0])
	matrix.Set(diset[0], dset[0])
	diset[0].Inv()

	// lmbda := s .* z
	blas.CopyFloat(s, lmbda, &la_.IOpt{"n", m})
	blas.TbmvFloat(z, lmbda, &la_.IOpt{"n", m}, &la_.IOpt{"k", 0}, &la_.IOpt{"lda", 1})

	// 'q' blocks.
	// Let st and zt be the new variables in the old scaling:
	//
	//     st = s_k,   zt = z_k
	//
	// and a = sqrt(st' * J * st),  b = sqrt(zt' * J * zt).
	//
	// 1. Compute the hyperbolic Householder transformation 2*q*q' - J
	//    that maps st/a to zt/b.
	//
	//        c = sqrt( (1 + st'*zt/(a*b)) / 2 )
	//        q = (st/a + J*zt/b) / (2*c).
	//
	//    The new scaling point is
	//
	//        wk := betak * sqrt(a/b) * (2*v[k]*v[k]' - J) * q
	//
	//    with betak = W['beta'][k].
	//
	// 3. The scaled variable:
	//
	//        lambda_k0 = sqrt(a*b) * c
	//        lambda_k1 = sqrt(a*b) * ( (2vk*vk' - J) * (-d*q + u/2) )_1
	//
	//    where
	//
	//        u = st/a - J*zt/b
	//        d = ( vk0 * (vk'*u) + u0/2 ) / (2*vk0 *(vk'*q) - q0 + 1).
	//
	// 4. Update scaling
	//
	//        v[k] := wk^1/2
	//              = 1 / sqrt(2*(wk0 + 1)) * (wk + e).
	//        beta[k] *=  sqrt(a/b)

	ind := m
	for k, v := range W.At("v") {
		m = v.NumElements()

		// ln = sqrt( lambda_k' * J * lambda_k ) !! NOT USED!!
		jnrm2(lmbda, m, ind) // ?? NOT USED ??

		// a = sqrt( sk' * J * sk ) = sqrt( st' * J * st )
		// s := s / a = st / a
		aa := jnrm2(s, m, ind)
		blas.ScalFloat(s, 1.0/aa, &la_.IOpt{"n", m}, &la_.IOpt{"offset", ind})

		// b = sqrt( zk' * J * zk ) = sqrt( zt' * J * zt )
		// z := z / a = zt / b
		bb := jnrm2(z, m, ind)
		blas.ScalFloat(z, 1.0/bb, &la_.IOpt{"n", m}, &la_.IOpt{"offset", ind})

		// c = sqrt( ( 1 + (st'*zt) / (a*b) ) / 2 )
		cc := blas.DotFloat(s, z, &la_.IOpt{"offsetx", ind}, &la_.IOpt{"offsety", ind},
			&la_.IOpt{"n", m})
		cc = math.Sqrt((1.0 + cc) / 2.0)

		// vs = v' * st / a
		vs := blas.DotFloat(v, s, &la_.IOpt{"offsety", ind}, &la_.IOpt{"n", m})

		// vz = v' * J *zt / b
		vz := jdot(v, z, m, 0, ind)

		// vq = v' * q where q = (st/a + J * zt/b) / (2 * c)
		vq := (vs + vz) / 2.0 / cc

		// vq = v' * q where q = (st/a + J * zt/b) / (2 * c)
		vu := vs - vz
		// lambda_k0 = c
		lmbda.SetIndex(ind, cc)

		// wk0 = 2 * vk0 * (vk' * q) - q0
		wk0 := 2.0*v.GetIndex(0)*vq - (s.GetIndex(ind)+z.GetIndex(ind))/2.0/cc

		// d = (v[0] * (vk' * u) - u0/2) / (wk0 + 1)
		dd := (v.GetIndex(0)*vu - s.GetIndex(ind)/2.0 + z.GetIndex(ind)/2.0) / (wk0 + 1.0)

		// lambda_k1 = 2 * v_k1 * vk' * (-d*q + u/2) - d*q1 + u1/2
		blas.CopyFloat(v, lmbda, &la_.IOpt{"offsetx", 1}, &la_.IOpt{"offsety", ind + 1},
			&la_.IOpt{"n", m - 1})
		blas.ScalFloat(lmbda, (2.0 * (-dd*vq + 0.5*vu)),
			&la_.IOpt{"offsetx", ind + 1}, &la_.IOpt{"offsety", ind + 1}, &la_.IOpt{"n", m - 1})
		blas.AxpyFloat(s, lmbda, 0.5*(1.0-dd/cc),
			&la_.IOpt{"offsetx", ind + 1}, &la_.IOpt{"offsety", ind + 1}, &la_.IOpt{"n", m - 1})
		blas.AxpyFloat(z, lmbda, 0.5*(1.0+dd/cc),
			&la_.IOpt{"offsetx", ind + 1}, &la_.IOpt{"offsety", ind + 1}, &la_.IOpt{"n", m - 1})

		// Scale so that sqrt(lambda_k' * J * lambda_k) = sqrt(aa*bb).
		blas.ScalFloat(lmbda, math.Sqrt(aa*bb), &la_.IOpt{"offset", ind}, &la_.IOpt{"n", m})

		// v := (2*v*v' - J) * q
		//    = 2 * (v'*q) * v' - (J* st/a + zt/b) / (2*c)
		blas.ScalFloat(v, 2.0*vq)
		v.SetIndex(0, v.GetIndex(0)-(s.GetIndex(ind)/2.0/cc))
		blas.AxpyFloat(s, v, 0.5/cc, &la_.IOpt{"offsetx", ind + 1}, &la_.IOpt{"offsety", 1},
			&la_.IOpt{"n", m - 1})
		blas.AxpyFloat(z, v, -0.5/cc, &la_.IOpt{"offsetx", ind}, &la_.IOpt{"n", m})

		// v := v^{1/2} = 1/sqrt(2 * (v0 + 1)) * (v + e)
		v0 := v.GetIndex(0) + 1.0
		v.SetIndex(0, v0)
		blas.ScalFloat(v, 1.0/math.Sqrt(2.0*v0))

		// beta[k] *= ( aa / bb )**1/2
		bk := beta.GetIndex(k)
		beta.SetIndex(k, bk*math.Sqrt(aa/bb))

		ind += m
	}
	//fmt.Printf("-- end of q:\nz=\n%v\nlmbda=\n%v\n", z.ConvertToString(), lmbda.ConvertToString())
	//fmt.Printf("beta=\n%v\n", beta.ConvertToString())

	// 's' blocks
	//
	// Let st, zt be the updated variables in the old scaling:
	//
	//     st = Ls * Ls', zt = Lz * Lz'.
	//
	// where Ls and Lz are the 's' components of s, z.
	//
	// 1.  SVD Lz'*Ls = Uk * lambda_k^+ * Vk'.
	//
	// 2.  New scaling is
	//
	//         r[k] := r[k] * Ls * Vk * diag(lambda_k^+)^{-1/2}
	//         rti[k] := r[k] * Lz * Uk * diag(lambda_k^+)^{-1/2}.
	//

	maxr := 0
	for _, m := range W.At("r") {
		if m.Rows() > maxr {
			maxr = m.Rows()
		}
	}
	work := matrix.FloatZeros(maxr*maxr, 1)
	vlensum := 0
	for _, m := range W.At("v") {
		vlensum += m.NumElements()
	}
	ind = mnl + ml + vlensum
	ind2 := ind
	ind3 := 0
	rset := W.At("r")
	rtiset := W.At("rti")

	for k, _ := range rset {
		r := rset[k]
		rti := rtiset[k]
		m = r.Rows()
		//fmt.Printf("m=%d, r=\n%v\nrti=\n%v\n", m, r.ConvertToString(), rti.ConvertToString())

		// r := r*sk = r*Ls
		blas.GemmFloat(r, s, work, 1.0, 0.0, &la_.IOpt{"m", m}, &la_.IOpt{"n", m},
			&la_.IOpt{"k", m}, &la_.IOpt{"ldb", m}, &la_.IOpt{"ldc", m},
			&la_.IOpt{"offsetb", ind2})
		//fmt.Printf("1 work=\n%v\n", work.ConvertToString())
		blas.CopyFloat(work, r, &la_.IOpt{"n", m * m})

		// rti := rti*zk = rti*Lz
		blas.GemmFloat(rti, z, work, 1.0, 0.0, &la_.IOpt{"m", m}, &la_.IOpt{"n", m},
			&la_.IOpt{"k", m}, &la_.IOpt{"ldb", m}, &la_.IOpt{"ldc", m},
			&la_.IOpt{"offsetb", ind2})
		//fmt.Printf("2 work=\n%v\n", work.ConvertToString())
		blas.CopyFloat(work, rti, &la_.IOpt{"n", m * m})

		// SVD Lz'*Ls = U * lmbds^+ * V'; store U in sk and V' in zk. '
		blas.GemmFloat(z, s, work, 1.0, 0.0, la_.OptTransA, &la_.IOpt{"m", m},
			&la_.IOpt{"n", m}, &la_.IOpt{"k", m}, &la_.IOpt{"lda", m}, &la_.IOpt{"ldb", m},
			&la_.IOpt{"ldc", m}, &la_.IOpt{"offseta", ind2}, &la_.IOpt{"offsetb", ind2})
		//fmt.Printf("3 work=\n%v\n", work.ConvertToString())

		// U = s, Vt = z
		lapack.GesvdFloat(work, lmbda, s, z, la_.OptJobuAll, la_.OptJobvtAll,
			&la_.IOpt{"m", m}, &la_.IOpt{"n", m}, &la_.IOpt{"lda", m}, &la_.IOpt{"ldu", m},
			&la_.IOpt{"ldvt", m}, &la_.IOpt{"offsets", ind}, &la_.IOpt{"offsetu", ind2},
			&la_.IOpt{"offsetvt", ind2})

		// r := r*V
		blas.GemmFloat(r, z, work, 1.0, 0.0, la_.OptTransB, &la_.IOpt{"m", m},
			&la_.IOpt{"n", m}, &la_.IOpt{"k", m}, &la_.IOpt{"ldb", m}, &la_.IOpt{"ldc", m},
			&la_.IOpt{"offsetb", ind2})
		//fmt.Printf("4 work=\n%v\n", work.ConvertToString())
		blas.CopyFloat(work, r, &la_.IOpt{"n", m * m})

		// rti := rti*U
		blas.GemmFloat(rti, s, work, 1.0, 0.0, &la_.IOpt{"m", m}, &la_.IOpt{"n", m},
			&la_.IOpt{"k", m}, &la_.IOpt{"ldb", m}, &la_.IOpt{"ldc", m},
			&la_.IOpt{"offsetb", ind2})
		//fmt.Printf("5 work=\n%v\n", work.ConvertToString())
		blas.CopyFloat(work, rti, &la_.IOpt{"n", m * m})

		for i := 0; i < m; i++ {
			a := 1.0 / math.Sqrt(lmbda.GetIndex(ind+i))
			blas.ScalFloat(r, a, &la_.IOpt{"n", m}, &la_.IOpt{"offset", m * i})
			blas.ScalFloat(rti, a, &la_.IOpt{"n", m}, &la_.IOpt{"offset", m * i})
		}
		ind += m
		ind2 += m * m
		ind3 += m // !!NOT USED: ind3!!
	}

	//fmt.Printf("-- end of s:\nz=\n%v\nlmbda=\n%v\n", z.ConvertToString(), lmbda.ConvertToString())

	return

}
Beispiel #21
0
/*
   Evaluates

       x := H(lambda^{1/2}) * x   (inverse is 'N')
       x := H(lambda^{-1/2}) * x  (inverse is 'I').

   H is the Hessian of the logarithmic barrier.

*/
func scale2(lmbda, x *matrix.FloatMatrix, dims *sets.DimensionSet, mnl int, inverse bool) (err error) {
	err = nil

	//var minor int = 0
	//if ! checkpnt.MinorEmpty() {
	//	minor = checkpnt.MinorTop()
	//}

	//fmt.Printf("\n%d.%04d scale2 x=\n%v\nlmbda=\n%v\n", checkpnt.Major(), minor,
	//	x.ToString("%.17f"), lmbda.ToString("%.17f"))

	//if ! checkpnt.MinorEmpty() {
	//	checkpnt.Check("000scale2", minor)
	//}

	// For the nonlinear and 'l' blocks,
	//
	//     xk := xk ./ l   (inverse is 'N')
	//     xk := xk .* l   (inverse is 'I')
	//
	// where l is lmbda[:mnl+dims['l']].
	ind := mnl + dims.Sum("l")
	if !inverse {
		blas.TbsvFloat(lmbda, x, &la_.IOpt{"n", ind}, &la_.IOpt{"k", 0}, &la_.IOpt{"lda", 1})
	} else {
		blas.TbmvFloat(lmbda, x, &la_.IOpt{"n", ind}, &la_.IOpt{"k", 0}, &la_.IOpt{"lda", 1})
	}

	//if ! checkpnt.MinorEmpty() {
	//	checkpnt.Check("010scale2", minor)
	//}

	// For 'q' blocks, if inverse is 'N',
	//
	//     xk := 1/a * [ l'*J*xk;
	//         xk[1:] - (xk[0] + l'*J*xk) / (l[0] + 1) * l[1:] ].
	//
	// If inverse is 'I',
	//
	//     xk := a * [ l'*xk;
	//         xk[1:] + (xk[0] + l'*xk) / (l[0] + 1) * l[1:] ].
	//
	// a = sqrt(lambda_k' * J * lambda_k), l = lambda_k / a.
	for _, m := range dims.At("q") {
		var lx, a, c, x0 float64
		a = jnrm2(lmbda, m, ind) //&la_.IOpt{"n", m}, &la_.IOpt{"offset", ind})
		if !inverse {
			lx = jdot(lmbda, x, m, ind, ind) //&la_.IOpt{"n", m}, &la_.IOpt{"offsetx", ind},
			//&la_.IOpt{"offsety", ind})
			lx /= a
		} else {
			lx = blas.DotFloat(lmbda, x, &la_.IOpt{"n", m}, &la_.IOpt{"offsetx", ind},
				&la_.IOpt{"offsety", ind})
			lx /= a
		}
		x0 = x.GetIndex(ind)
		x.SetIndex(ind, lx)
		c = (lx + x0) / (lmbda.GetIndex(ind)/a + 1.0) / a
		if !inverse {
			c *= -1.0
		}
		blas.AxpyFloat(lmbda, x, c, &la_.IOpt{"n", m - 1}, &la_.IOpt{"offsetx", ind + 1},
			&la_.IOpt{"offsety", ind + 1})
		if !inverse {
			a = 1.0 / a
		}
		blas.ScalFloat(x, a, &la_.IOpt{"offset", ind}, &la_.IOpt{"n", m})
		ind += m
	}

	//if ! checkpnt.MinorEmpty() {
	//	checkpnt.Check("020scale2", minor)
	//}

	// For the 's' blocks, if inverse is 'N',
	//
	//     xk := vec( diag(l)^{-1/2} * mat(xk) * diag(k)^{-1/2}).
	//
	// If inverse is true,
	//
	//     xk := vec( diag(l)^{1/2} * mat(xk) * diag(k)^{1/2}).
	//
	// where l is kth block of lambda.
	//
	// We scale upper and lower triangular part of mat(xk) because the
	// inverse operation will be applied to nonsymmetric matrices.
	ind2 := ind
	sdims := dims.At("s")
	for k := 0; k < len(sdims); k++ {
		m := sdims[k]
		scaleF := func(v, x float64) float64 {
			return math.Sqrt(v) * math.Sqrt(x)
		}
		for j := 0; j < m; j++ {
			c := matrix.FloatVector(lmbda.FloatArray()[ind2 : ind2+m])
			c.ApplyConst(lmbda.GetIndex(ind2+j), scaleF)
			if !inverse {
				blas.Tbsv(c, x, &la_.IOpt{"n", m}, &la_.IOpt{"k", 0}, &la_.IOpt{"lda", 1},
					&la_.IOpt{"offsetx", ind + j*m})
			} else {
				blas.Tbmv(c, x, &la_.IOpt{"n", m}, &la_.IOpt{"k", 0}, &la_.IOpt{"lda", 1},
					&la_.IOpt{"offsetx", ind + j*m})
			}
		}
		ind += m * m
		ind2 += m
	}

	//if ! checkpnt.MinorEmpty() {
	//	checkpnt.Check("030scale2", minor)
	//}
	return
}
Beispiel #22
0
// The product x := (y o x).  If diag is 'D', the 's' part of y is
// diagonal and only the diagonal is stored.
func sprod(x, y *matrix.FloatMatrix, dims *sets.DimensionSet, mnl int, opts ...la_.Option) (err error) {

	err = nil
	diag := la_.GetStringOpt("diag", "N", opts...)
	// For the nonlinear and 'l' blocks:
	//
	//     yk o xk = yk .* xk.
	ind := mnl + dims.At("l")[0]
	err = blas.Tbmv(y, x, &la_.IOpt{"n", ind}, &la_.IOpt{"k", 0}, &la_.IOpt{"lda", 1})
	if err != nil {
		return
	}
	//fmt.Printf("Sprod l:x=\n%v\n", x)

	// For 'q' blocks:
	//
	//               [ l0   l1'  ]
	//     yk o xk = [           ] * xk
	//               [ l1   l0*I ]
	//
	// where yk = (l0, l1).
	for _, m := range dims.At("q") {
		dd := blas.DotFloat(x, y, &la_.IOpt{"offsetx", ind}, &la_.IOpt{"offsety", ind},
			&la_.IOpt{"n", m})
		//fmt.Printf("dd=%v\n", dd)
		alpha := y.GetIndex(ind)
		//fmt.Printf("scal=%v\n", alpha)
		blas.ScalFloat(x, alpha, &la_.IOpt{"offset", ind + 1}, &la_.IOpt{"n", m - 1})
		alpha = x.GetIndex(ind)
		//fmt.Printf("axpy=%v\n", alpha)
		blas.AxpyFloat(y, x, alpha, &la_.IOpt{"offsetx", ind + 1}, &la_.IOpt{"offsety", ind + 1},
			&la_.IOpt{"n", m - 1})
		x.SetIndex(ind, dd)
		ind += m
	}
	//fmt.Printf("Sprod q :x=\n%v\n", x)

	// For the 's' blocks:
	//
	//    yk o sk = .5 * ( Yk * mat(xk) + mat(xk) * Yk )
	//
	// where Yk = mat(yk) if diag is 'N' and Yk = diag(yk) if diag is 'D'.

	if diag[0] == 'N' {
		// DEBUGGED
		maxm := maxdim(dims.At("s"))
		A := matrix.FloatZeros(maxm, maxm)
		for _, m := range dims.At("s") {
			blas.Copy(x, A, &la_.IOpt{"offsetx", ind}, &la_.IOpt{"n", m * m})
			for i := 0; i < m-1; i++ { // i < m-1 --> i < m
				symm(A, m, 0)
				symm(y, m, ind)
			}
			err = blas.Syr2kFloat(A, y, x, 0.5, 0.0, &la_.IOpt{"n", m}, &la_.IOpt{"k", m},
				&la_.IOpt{"lda", m}, &la_.IOpt{"ldb", m}, &la_.IOpt{"ldc", m},
				&la_.IOpt{"offsetb", ind}, &la_.IOpt{"offsetc", ind})
			if err != nil {
				return
			}
			ind += m * m
		}
		//fmt.Printf("Sprod diag=N s:x=\n%v\n", x)

	} else {
		ind2 := ind
		for _, m := range dims.At("s") {
			for i := 0; i < m; i++ {
				// original: u = 0.5 * ( y[ind2+i:ind2+m] + y[ind2+i] )
				// creates matrix of elements: [ind2+i ... ind2+m] then
				// element wisely adds y[ind2+i] and scales by 0.5
				iset := matrix.MakeIndexSet(ind2+i, ind2+m, 1)
				u := matrix.FloatVector(y.GetIndexes(iset...))
				u.Add(y.GetIndex(ind2 + i))
				u.Scale(0.5)
				err = blas.Tbmv(u, x, &la_.IOpt{"n", m - i}, &la_.IOpt{"k", 0}, &la_.IOpt{"lda", 1},
					&la_.IOpt{"offsetx", ind + i*(m+1)})
				if err != nil {
					return
				}
			}
			ind += m * m
			ind2 += m
		}
		//fmt.Printf("Sprod diag=T s:x=\n%v\n", x)
	}
	return
}
Beispiel #23
0
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()
		}
	}

}
Beispiel #24
0
// Solves a pair of primal and dual SOCPs
//
//     minimize    c'*x
//     subject to  Gl*x + sl = hl
//                 Gq[k]*x + sq[k] = hq[k],  k = 0, ..., N-1
//                 A*x = b
//                 sl >= 0,
//                 sq[k] >= 0, k = 0, ..., N-1
//
//     maximize   -hl'*z - sum_k hq[k]'*zq[k] - b'*y
//     subject to  Gl'*zl + sum_k Gq[k]'*zq[k] + A'*y + c = 0
//                 zl >= 0,  zq[k] >= 0, k = 0, ..., N-1.
//
// The inequalities sl >= 0 and zl >= 0 are elementwise vector
// inequalities.  The inequalities sq[k] >= 0, zq[k] >= 0 are second
// order cone inequalities, i.e., equivalent to
//
//     sq[k][0] >= || sq[k][1:] ||_2,  zq[k][0] >= || zq[k][1:] ||_2.
//
func Socp(c, Gl, hl, A, b *matrix.FloatMatrix, Ghq *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
	}
	Gqset := Ghq.At("Gq")
	mq := make([]int, 0)
	for i, Gq := range Gqset {
		if Gq.Cols() != n {
			err = errors.New(fmt.Sprintf("'Gq' must be list of matrices with %d columns", n))
			return
		}
		if Gq.Rows() == 0 {
			err = errors.New(fmt.Sprintf("the number of rows of 'Gq[%d]' is zero", i))
			return
		}
		mq = append(mq, Gq.Rows())
	}
	hqset := Ghq.At("hq")
	if len(Gqset) != len(hqset) {
		err = errors.New(fmt.Sprintf("'hq' must be a list of %d matrices", len(Gqset)))
		return
	}
	for i, hq := range hqset {
		if !hq.SizeMatch(Gqset[i].Rows(), 1) {
			s := fmt.Sprintf("hq[%d] has size (%d,%d). Expected size is (%d,1)",
				i, hq.Rows(), hq.Cols(), Gqset[i].Rows())
			err = errors.New(s)
			return
		}
	}
	if A == nil {
		A = matrix.FloatZeros(0, n)
	}
	if A.Cols() != n {
		err = errors.New(fmt.Sprintf("'A' must be matrix with %d columns", n))
		return
	}
	p := A.Rows()
	if b == nil {
		b = matrix.FloatZeros(0, 1)
	}
	if !b.SizeMatch(p, 1) {
		err = errors.New(fmt.Sprintf("'b' must be matrix of size (%d,1)", p))
		return
	}
	dims := sets.NewDimensionSet("l", "q", "s")
	dims.Set("l", []int{ml})
	dims.Set("q", mq)
	//N := dims.Sum("l", "q")

	hargs := make([]*matrix.FloatMatrix, 0, len(hqset)+1)
	hargs = append(hargs, hl)
	hargs = append(hargs, hqset...)
	h, indh := matrix.FloatMatrixStacked(matrix.StackDown, hargs...)

	Gargs := make([]*matrix.FloatMatrix, 0, len(Gqset)+1)
	Gargs = append(Gargs, Gl)
	Gargs = append(Gargs, Gqset...)
	G, indg := 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)
	}

	sol, err = ConeLp(c, G, h, A, b, dims, solopts, pstart, dstart)
	// unpack sol.Result
	if err == nil {
		s := sol.Result.At("s")[0]
		sl := matrix.FloatVector(s.FloatArray()[:ml])
		sol.Result.Append("sl", sl)
		ind := ml
		for _, k := range indh[1:] {
			sk := matrix.FloatVector(s.FloatArray()[ind : ind+k])
			sol.Result.Append("sq", sk)
			ind += k
		}

		z := sol.Result.At("z")[0]
		zl := matrix.FloatVector(z.FloatArray()[:ml])
		sol.Result.Append("zl", zl)
		ind = ml
		for _, k := range indg[1:] {
			zk := matrix.FloatVector(z.FloatArray()[ind : ind+k])
			sol.Result.Append("zq", zk)
			ind += k
		}
	}
	sol.Result.Remove("s")
	sol.Result.Remove("z")

	return
}
Beispiel #25
0
// 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

}
Beispiel #26
0
/*
   Returns the Nesterov-Todd scaling W at points s and z, and stores the
   scaled variable in lmbda.

       W * z = W^{-T} * s = lmbda.

   W is a MatrixSet with entries:

   - W['dnl']: positive vector
   - W['dnli']: componentwise inverse of W['dnl']
   - W['d']: positive vector
   - W['di']: componentwise inverse of W['d']
   - W['v']: lists of 2nd order cone vectors with unit hyperbolic norms
   - W['beta']: list of positive numbers
   - W['r']: list of square matrices
   - W['rti']: list of square matrices.  rti[k] is the inverse transpose
     of r[k].

*/
func computeScaling(s, z, lmbda *matrix.FloatMatrix, dims *sets.DimensionSet, mnl int) (W *sets.FloatMatrixSet, err error) {
	/*DEBUGGED*/
	err = nil
	W = sets.NewFloatSet("dnl", "dnli", "d", "di", "v", "beta", "r", "rti")

	// For the nonlinear block:
	//
	//     W['dnl'] = sqrt( s[:mnl] ./ z[:mnl] )
	//     W['dnli'] = sqrt( z[:mnl] ./ s[:mnl] )
	//     lambda[:mnl] = sqrt( s[:mnl] .* z[:mnl] )

	var stmp, ztmp, lmd *matrix.FloatMatrix
	if mnl > 0 {
		stmp = matrix.FloatVector(s.FloatArray()[:mnl])
		ztmp = matrix.FloatVector(z.FloatArray()[:mnl])
		//dnl := stmp.Div(ztmp)
		//dnl.Apply(dnl, math.Sqrt)
		dnl := matrix.Sqrt(matrix.Div(stmp, ztmp))
		//dnli := dnl.Copy()
		//dnli.Apply(dnli, func(a float64)float64 { return 1.0/a })
		dnli := matrix.Inv(dnl)
		W.Set("dnl", dnl)
		W.Set("dnli", dnli)
		//lmd = stmp.Mul(ztmp)
		//lmd.Apply(lmd, math.Sqrt)
		lmd = matrix.Sqrt(matrix.Mul(stmp, ztmp))
		lmbda.SetIndexesFromArray(lmd.FloatArray(), matrix.MakeIndexSet(0, mnl, 1)...)
	} else {
		// set for empty matrices
		//W.Set("dnl", matrix.FloatZeros(0, 1))
		//W.Set("dnli", matrix.FloatZeros(0, 1))
		mnl = 0
	}

	// For the 'l' block:
	//
	//     W['d'] = sqrt( sk ./ zk )
	//     W['di'] = sqrt( zk ./ sk )
	//     lambdak = sqrt( sk .* zk )
	//
	// where sk and zk are the first dims['l'] entries of s and z.
	// lambda_k is stored in the first dims['l'] positions of lmbda.

	m := dims.At("l")[0]
	//td := s.FloatArray()
	stmp = matrix.FloatVector(s.FloatArray()[mnl : mnl+m])
	//zd := z.FloatArray()
	ztmp = matrix.FloatVector(z.FloatArray()[mnl : mnl+m])
	//fmt.Printf(".Sqrt()=\n%v\n", matrix.Div(stmp, ztmp).Sqrt().ToString("%.17f"))
	//d := stmp.Div(ztmp)
	//d.Apply(d, math.Sqrt)
	d := matrix.Div(stmp, ztmp).Sqrt()
	//di := d.Copy()
	//di.Apply(di, func(a float64)float64 { return 1.0/a })
	di := matrix.Inv(d)
	//fmt.Printf("d:\n%v\n", d)
	//fmt.Printf("di:\n%v\n", di)
	W.Set("d", d)
	W.Set("di", di)
	//lmd = stmp.Mul(ztmp)
	//lmd.Apply(lmd, math.Sqrt)
	lmd = matrix.Mul(stmp, ztmp).Sqrt()
	// lmd has indexes mnl:mnl+m and length of m
	lmbda.SetIndexesFromArray(lmd.FloatArray(), matrix.MakeIndexSet(mnl, mnl+m, 1)...)
	//fmt.Printf("after l:\n%v\n", lmbda)

	/*
	   For the 'q' blocks, compute lists 'v', 'beta'.

	   The vector v[k] has unit hyperbolic norm:

	       (sqrt( v[k]' * J * v[k] ) = 1 with J = [1, 0; 0, -I]).

	   beta[k] is a positive scalar.

	   The hyperbolic Householder matrix H = 2*v[k]*v[k]' - J
	   defined by v[k] satisfies

	       (beta[k] * H) * zk  = (beta[k] * H) \ sk = lambda_k

	   where sk = s[indq[k]:indq[k+1]], zk = z[indq[k]:indq[k+1]].

	   lambda_k is stored in lmbda[indq[k]:indq[k+1]].
	*/
	ind := mnl + dims.At("l")[0]
	var beta *matrix.FloatMatrix

	for _, k := range dims.At("q") {
		W.Append("v", matrix.FloatZeros(k, 1))
	}
	beta = matrix.FloatZeros(len(dims.At("q")), 1)
	W.Set("beta", beta)
	vset := W.At("v")
	for k, m := range dims.At("q") {
		v := vset[k]
		// a = sqrt( sk' * J * sk )  where J = [1, 0; 0, -I]
		aa := jnrm2(s, m, ind)
		// b = sqrt( zk' * J * zk )
		bb := jnrm2(z, m, ind)
		// beta[k] = ( a / b )**1/2
		beta.SetIndex(k, math.Sqrt(aa/bb))
		// c = sqrt( (sk/a)' * (zk/b) + 1 ) / sqrt(2)
		c0 := blas.DotFloat(s, z, &la_.IOpt{"n", m},
			&la_.IOpt{"offsetx", ind}, &la_.IOpt{"offsety", ind})
		cc := math.Sqrt((c0/aa/bb + 1.0) / 2.0)

		// vk = 1/(2*c) * ( (sk/a) + J * (zk/b) )
		blas.CopyFloat(z, v, &la_.IOpt{"offsetx", ind}, &la_.IOpt{"n", m})
		blas.ScalFloat(v, -1.0/bb)
		v.SetIndex(0, -1.0*v.GetIndex(0))
		blas.AxpyFloat(s, v, 1.0/aa, &la_.IOpt{"offsetx", ind}, &la_.IOpt{"n", m})
		blas.ScalFloat(v, 1.0/2.0/cc)

		// v[k] = 1/sqrt(2*(vk0 + 1)) * ( vk + e ),  e = [1; 0]
		v.SetIndex(0, v.GetIndex(0)+1.0)
		blas.ScalFloat(v, (1.0 / math.Sqrt(2.0*v.GetIndex(0))))
		/*
		   To get the scaled variable lambda_k

		       d =  sk0/a + zk0/b + 2*c
		       lambda_k = [ c;
		                    (c + zk0/b)/d * sk1/a + (c + sk0/a)/d * zk1/b ]
		       lambda_k *= sqrt(a * b)
		*/
		lmbda.SetIndex(ind, cc)
		dd := 2*cc + s.GetIndex(ind)/aa + z.GetIndex(ind)/bb
		blas.CopyFloat(s, lmbda, &la_.IOpt{"offsetx", ind + 1}, &la_.IOpt{"offsety", ind + 1},
			&la_.IOpt{"n", m - 1})
		zz := (cc + z.GetIndex(ind)/bb) / dd / aa
		ss := (cc + s.GetIndex(ind)/aa) / dd / bb
		blas.ScalFloat(lmbda, zz, &la_.IOpt{"offset", ind + 1}, &la_.IOpt{"n", m - 1})
		blas.AxpyFloat(z, lmbda, ss, &la_.IOpt{"offsetx", ind + 1},
			&la_.IOpt{"offsety", ind + 1}, &la_.IOpt{"n", m - 1})
		blas.ScalFloat(lmbda, math.Sqrt(aa*bb), &la_.IOpt{"offset", ind}, &la_.IOpt{"n", m})

		ind += m
		//fmt.Printf("after q[%d]:\n%v\n", k, lmbda)
	}
	/*
	   For the 's' blocks: compute two lists 'r' and 'rti'.

	       r[k]' * sk^{-1} * r[k] = diag(lambda_k)^{-1}
	       r[k]' * zk * r[k] = diag(lambda_k)

	   where sk and zk are the entries inds[k] : inds[k+1] of
	   s and z, reshaped into symmetric matrices.

	   rti[k] is the inverse of r[k]', so

	       rti[k]' * sk * rti[k] = diag(lambda_k)^{-1}
	       rti[k]' * zk^{-1} * rti[k] = diag(lambda_k).

	   The vectors lambda_k are stored in

	       lmbda[ dims['l'] + sum(dims['q']) : -1 ]
	*/
	for _, k := range dims.At("s") {
		W.Append("r", matrix.FloatZeros(k, k))
		W.Append("rti", matrix.FloatZeros(k, k))
	}
	maxs := maxdim(dims.At("s"))
	work := matrix.FloatZeros(maxs*maxs, 1)
	Ls := matrix.FloatZeros(maxs*maxs, 1)
	Lz := matrix.FloatZeros(maxs*maxs, 1)
	ind2 := ind
	for k, m := range dims.At("s") {
		r := W.At("r")[k]
		rti := W.At("rti")[k]

		// Factor sk = Ls*Ls'; store Ls in ds[inds[k]:inds[k+1]].
		blas.CopyFloat(s, Ls, &la_.IOpt{"offsetx", ind2}, &la_.IOpt{"n", m * m})
		lapack.PotrfFloat(Ls, &la_.IOpt{"n", m}, &la_.IOpt{"lda", m})

		// Factor zs[k] = Lz*Lz'; store Lz in dz[inds[k]:inds[k+1]].
		blas.CopyFloat(z, Lz, &la_.IOpt{"offsetx", ind2}, &la_.IOpt{"n", m * m})
		lapack.PotrfFloat(Lz, &la_.IOpt{"n", m}, &la_.IOpt{"lda", m})

		// SVD Lz'*Ls = U*diag(lambda_k)*V'.  Keep U in work.
		for i := 0; i < m; i++ {
			blas.ScalFloat(Ls, 0.0, &la_.IOpt{"offset", i * m}, &la_.IOpt{"n", i})
		}
		blas.CopyFloat(Ls, work, &la_.IOpt{"n", m * m})
		blas.TrmmFloat(Lz, work, 1.0, la_.OptTransA, &la_.IOpt{"lda", m}, &la_.IOpt{"ldb", m},
			&la_.IOpt{"n", m}, &la_.IOpt{"m", m})
		lapack.GesvdFloat(work, lmbda, nil, nil,
			la_.OptJobuO, &la_.IOpt{"lda", m}, &la_.IOpt{"offsetS", ind},
			&la_.IOpt{"n", m}, &la_.IOpt{"m", m})

		// r = Lz^{-T} * U
		blas.CopyFloat(work, r, &la_.IOpt{"n", m * m})
		blas.TrsmFloat(Lz, r, 1.0, la_.OptTransA,
			&la_.IOpt{"lda", m}, &la_.IOpt{"n", m}, &la_.IOpt{"m", m})

		// rti = Lz * U
		blas.CopyFloat(work, rti, &la_.IOpt{"n", m * m})
		blas.TrmmFloat(Lz, rti, 1.0,
			&la_.IOpt{"lda", m}, &la_.IOpt{"n", m}, &la_.IOpt{"m", m})

		// r := r * diag(sqrt(lambda_k))
		// rti := rti * diag(1 ./ sqrt(lambda_k))
		for i := 0; i < m; i++ {
			a := math.Sqrt(lmbda.GetIndex(ind + i))
			blas.ScalFloat(r, a, &la_.IOpt{"offset", m * i}, &la_.IOpt{"n", m})
			blas.ScalFloat(rti, 1.0/a, &la_.IOpt{"offset", m * i}, &la_.IOpt{"n", m})
		}
		ind += m
		ind2 += m * m
	}
	return
}