Ejemplo n.º 1
0
func _TestBlkCHOLUpLo(t *testing.T) {
	N := 10
	nb := 4
	L := matrix.FloatUniformSymmetric(N, matrix.Lower)
	A := matrix.Times(L, L.Transpose())
	DecomposeCHOL(A, LOWER, nb)
	Ld := TriL(A)
	ok := L.AllClose(Ld)
	t.Logf("result L == Ld: %v\n", ok)
	if !ok {
		t.Logf("L:\n%v\n", L)
		t.Logf("Ld:\n%v\n", Ld)
	}

	U := matrix.FloatUniformSymmetric(N, matrix.Upper)
	A = matrix.Times(U.Transpose(), U)
	DecomposeCHOL(A, UPPER, nb)
	Ud := TriU(A)
	ok = U.AllClose(Ud)
	t.Logf("result U == Ud: %v\n", ok)

	//lapack.Potrf(A0)
	//t.Logf("lapack result:\n%v\n", A0)
	//t.Logf("A == A0: %v\n", A0.AllClose(A))
}
Ejemplo n.º 2
0
func (g *matrixFs) Gf(x, y *matrix.FloatMatrix, alpha, beta float64, trans linalg.Option) error {
	//
	minor := 0
	if !checkpnt.MinorEmpty() {
		minor = checkpnt.MinorTop()
	} else {
		loopg += 1
		minor = loopg
	}
	checkpnt.Check("00-Gfunc", minor)

	m, n := g.A.Size()
	y.Scale(beta)

	// x_n = x[:n]
	//x_n := matrix.FloatVector(x.FloatArray()[:n])
	x_n := x.SubMatrix(0, 0, n, 1).Copy()

	// x_n_2n = x[n:2*n]
	//x_n_2n := matrix.FloatVector(x.FloatArray()[n : 2*n])
	x_n_2n := x.SubMatrix(n, 0, n, 1).Copy()

	if linalg.Equal(trans, linalg.OptNoTrans) {
		// y += alpha * G * x

		// y[:n] += alpha * (x[:n] - x[n:2*n])
		y_n := matrix.Minus(x_n, x_n_2n).Scale(alpha)
		y.SubMatrix(0, 0, n, 1).Plus(y_n)
		//y.AddIndexes(matrix.Indexes(n), y_n.FloatArray())

		// y[n:2*n] += alpha * (-x[:n] - x[n:2*n]) = -alpha * (x[:n]+x[n:2*n])
		y_n = matrix.Plus(x_n, x_n_2n).Scale(-alpha)
		y.SubMatrix(n, 0, n, 1).Plus(y_n)
		//y.AddIndexes(matrix.Indexes(n, 2*n), y_n.FloatArray())

		// y[2*n+1:] += -alpha * A * x[:n]
		y_2n := matrix.Times(g.A, x_n).Scale(-alpha)
		//y.AddIndexes(matrix.Indexes(2*n+1, y.NumElements()), y_2n.FloatArray())
		y.SubMatrix(2*n+1, 0, y_2n.NumElements(), 1).Plus(y_2n)
	} else {
		// x_m = x[-m:]
		//x_m := matrix.FloatVector(x.FloatArray()[x.NumElements()-m:])
		x_m := x.SubMatrix(x.NumElements()-m, 0)

		// x_tmp = (x[:n] - x[n:2*n] - A.T * x[-m:])
		x_tmp := matrix.Minus(x_n, x_n_2n, matrix.Times(g.A.Transpose(), x_m))

		// y[:n] += alpha * (x[:n] - x[n:2*n] - A.T * x[-m:])
		//y.AddIndexes(matrix.Indexes(n), x_tmp.Scale(alpha).FloatArray())
		y.SubMatrix(0, 0, n, 1).Plus(x_tmp.Scale(alpha))

		x_tmp = matrix.Plus(x_n, x_n_2n).Scale(-alpha)
		//y.AddIndexes(matrix.Indexes(n, y.NumElements()), x_tmp.FloatArray())
		y.SubMatrix(n, 0).Plus(x_tmp)
	}
	checkpnt.Check("10-Gfunc", minor)
	return nil
}
Ejemplo n.º 3
0
func _TestCHOLUpLo(t *testing.T) {
	N := 10
	L := matrix.FloatUniformSymmetric(N, matrix.Lower)
	A := matrix.Times(L, L.Transpose())
	DecomposeCHOL(A, LOWER, 0)
	Ld := TriL(A)
	t.Logf("result L == Ld: %v\n", L.AllClose(Ld))

	U := matrix.FloatUniformSymmetric(N, matrix.Upper)
	A = matrix.Times(U.Transpose(), U)
	DecomposeCHOL(A, UPPER, 0)
	Ud := TriU(A)
	t.Logf("result U == Ud: %v\n", U.AllClose(Ud))
}
Ejemplo n.º 4
0
func _TestUnblkLUnoPiv(t *testing.T) {
	N := 6
	L := matrix.FloatUniformSymmetric(N, matrix.Lower)
	U := matrix.FloatUniformSymmetric(N, matrix.Upper)
	// Set L diagonal to 1.0
	L.Diag().SetIndexes(1.0)

	A := matrix.Times(L, U)
	t.Logf("A\n%v\n", A)
	DecomposeBlockSize(0)
	R, _ := DecomposeLUnoPiv(A.Copy(), 0)
	Ld := TriLU(R.Copy())
	Ud := TriU(R)
	t.Logf("A == L*U: %v\n", A.AllClose(matrix.Times(Ld, Ud)))
}
Ejemplo n.º 5
0
func _TestLU3x3Piv(t *testing.T) {
	Adata2 := [][]float64{
		[]float64{3.0, 2.0, 2.0},
		[]float64{6.0, 4.0, 1.0},
		[]float64{4.0, 6.0, 3.0},
	}
	A := matrix.FloatMatrixFromTable(Adata2, matrix.RowOrder)
	piv := make([]int, A.Rows())
	piv0 := make([]int32, A.Rows())
	A0 := A.Copy()
	t.Logf("start A\n%v\n", A)
	DecomposeBlockSize(0)
	DecomposeLU(A, piv, 0)
	Ld := TriLU(A.Copy())
	Ud := TriU(A.Copy())
	t.Logf("A\n%v\n", A)
	t.Logf("Ld:\n%v\n", Ld)
	t.Logf("Ud:\n%v\n", Ud)
	t.Logf("piv: %v\n", piv)
	t.Logf("result:\n%v\n", matrix.Times(Ld, Ud))
	//t.Logf("A == L*U: %v\n", A0.AllClose(matrix.Times(Ld, Ud)))
	lapack.Getrf(A0, piv0)
	t.Logf("lapack result: piv0 %v\n%v\n", piv0, A0)
	t.Logf("A == A0: %v\n", A0.AllClose(A))
}
Ejemplo n.º 6
0
func _TestLU2x2NoPiv(t *testing.T) {
	Adata2 := [][]float64{
		[]float64{4.0, 3.0},
		[]float64{6.0, 3.0}}

	A := matrix.FloatMatrixFromTable(Adata2, matrix.RowOrder)
	DecomposeBlockSize(0)
	DecomposeLUnoPiv(A, 0)
	t.Logf("A\n%v\n", A)
	Ld := TriLU(A.Copy())
	Ud := TriU(A)
	t.Logf("L*U\n%v\n", matrix.Times(Ld, Ud))
}
Ejemplo n.º 7
0
func _TestCHOL3x3(t *testing.T) {
	Ldata2 := [][]float64{
		[]float64{3.0, 0.0, 0.0},
		[]float64{6.0, 4.0, 0.0},
		[]float64{4.0, 6.0, 3.0},
	}
	L := matrix.FloatMatrixFromTable(Ldata2, matrix.RowOrder)
	A := matrix.Times(L, L.Transpose())
	DecomposeBlockSize(0)
	DecomposeCHOL(A, LOWER, 0)
	Ld := TriL(A.Copy())
	t.Logf("Ld:\n%v\n", Ld)
	t.Logf("result L == Ld: %v\n", L.AllClose(Ld))
}
Ejemplo n.º 8
0
func _TestLU3x3NoPiv(t *testing.T) {
	Adata2 := [][]float64{
		[]float64{4.0, 2.0, 2.0},
		[]float64{6.0, 4.0, 2.0},
		[]float64{4.0, 6.0, 1.0},
	}

	A := matrix.FloatMatrixFromTable(Adata2, matrix.RowOrder)
	A0 := A.Copy()
	DecomposeBlockSize(0)
	DecomposeLUnoPiv(A, 0)
	t.Logf("A\n%v\n", A)
	Ld := TriLU(A.Copy())
	Ud := TriU(A.Copy())
	t.Logf("A == L*U: %v\n", A0.AllClose(matrix.Times(Ld, Ud)))
}
Ejemplo n.º 9
0
func _TestBlkLUPiv(t *testing.T) {
	N := 10
	nb := 4
	L := matrix.FloatUniformSymmetric(N, matrix.Lower)
	U := matrix.FloatUniformSymmetric(N, matrix.Upper)
	// Set L diagonal to 1.0
	L.Diag().SetIndexes(1.0)

	A := matrix.Times(L, U)
	A0 := A.Copy()
	piv := make([]int, N, N)
	DecomposeBlockSize(nb)
	R, _ := DecomposeLU(A.Copy(), piv, 0)
	t.Logf("piv: %v\n", piv)

	piv0 := make([]int32, N, N)
	lapack.Getrf(A0, piv0)
	t.Logf("lapack result: piv0 %v\n", piv0)
	t.Logf("R == A0: %v\n", A0.AllClose(R))
}
Ejemplo n.º 10
0
func TestLowerCHOL(t *testing.T) {
	N := 60
	K := 30
	nb := 16
	Z := matrix.FloatUniform(N, N)
	A := matrix.Times(Z, Z.Transpose())
	B := matrix.FloatUniform(N, K)
	X := B.Copy()

	// R = chol(A) = L*L.T
	R, _ := DecomposeCHOL(A.Copy(), LOWER, nb)

	// X = A.-1*B = L.-T*(L.-1*B)
	SolveCHOL(X, R, LOWER)

	// B = B - A*X
	Mult(B, A, X, -1.0, 1.0, NONE)

	nrm := NormP(B, NORM_ONE)
	t.Logf("||B - A*X||_1: %e\n", nrm)
}
Ejemplo n.º 11
0
func TestUpperCHOL(t *testing.T) {
	N := 60
	K := 30
	nb := 16
	Z := matrix.FloatUniform(N, N)
	A := matrix.Times(Z, Z.Transpose())
	B := matrix.FloatUniform(N, K)
	X := B.Copy()

	// R = chol(A) = U.T*U
	R, _ := DecomposeCHOL(TriU(A.Copy()), UPPER, nb)

	// X = A.-1*B = U.-1*(U.-T*B)
	SolveCHOL(X, R, UPPER)

	// B = B - A*X
	Mult(B, A, X, -1.0, 1.0, NONE)

	// ||B - A*X||_1
	nrm := NormP(B, NORM_ONE)
	t.Logf("||B - A*X||_1: %e\n", nrm)
}
Ejemplo n.º 12
0
// Computes analytic center of A*x <= b with A m by n of rank n.
// We assume that b > 0 and the feasible set is bounded.
func Acent(A, b *matrix.FloatMatrix, niters int) (*matrix.FloatMatrix, []float64) {

	if niters <= 0 {
		niters = MAXITERS
	}
	ntdecrs := make([]float64, 0, niters)

	if A.Rows() != b.Rows() {
		return nil, nil
	}

	m, n := A.Size()
	x := matrix.FloatZeros(n, 1)
	H := matrix.FloatZeros(n, n)
	// Helper m*n matrix
	Dmn := matrix.FloatZeros(m, n)

	for i := 0; i < niters; i++ {

		// Gradient is g = A^T * (1.0/(b - A*x)). d = 1.0/(b - A*x)
		// d is m*1 matrix, g is n*1 matrix
		d := matrix.Minus(b, matrix.Times(A, x)).Inv()
		g := matrix.Times(A.Transpose(), d)

		// Hessian is H = A^T * diag(1./(b-A*x))^2 * A.
		// in the original python code expression d[:,n*[0]] creates
		// a m*n matrix where each column is copy of column 0.
		// We do it here manually.
		for i := 0; i < n; i++ {
			Dmn.SetColumn(i, d)
		}

		// Function mul creates element wise product of matrices.
		Asc := matrix.Mul(Dmn, A)
		blas.SyrkFloat(Asc, H, 1.0, 0.0, linalg.OptTrans)

		// Newton step is v = H^-1 * g.
		v := g.Copy().Scale(-1.0)
		lapack.PosvFloat(H, v)

		// Directional derivative and Newton decrement.
		lam := blas.DotFloat(g, v)
		ntdecrs = append(ntdecrs, math.Sqrt(-lam))
		if ntdecrs[len(ntdecrs)-1] < TOL {
			fmt.Printf("last Newton decrement < TOL(%v)\n", TOL)
			return x, ntdecrs
		}

		// Backtracking line search.
		// y = d .* A*v
		y := d.Mul(A.Times(v))
		step := 1.0
		for 1-step*y.Max() < 0 {
			step *= BETA
		}

	search:
		for {
			// t = -step*y
			t := y.Copy().Scale(-step)
			// t = (1 + t) [e.g. t = 1 - step*y]
			t.Add(1.0)

			// ts = sum(log(1-step*y))
			ts := t.Log().Sum()
			if -ts < ALPHA*step*lam {
				break search
			}
			step *= BETA
		}
		v.Scale(step)
		x = x.Plus(v)
	}
	// no solution !!
	fmt.Printf("Iteration %d exhausted\n", niters)
	return x, ntdecrs
}
Ejemplo n.º 13
0
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())
}
Ejemplo n.º 14
0
func qcl1(A, b *matrix.FloatMatrix) (*cvx.Solution, error) {

	// Returns the solution u, z of
	//
	//   (primal)  minimize    || u ||_1
	//             subject to  || A * u - b ||_2  <= 1
	//
	//   (dual)    maximize    b^T z - ||z||_2
	//             subject to  || A'*z ||_inf <= 1.
	//
	// Exploits structure, assuming A is m by n with m >= n.

	m, n := A.Size()
	Fkkt := func(W *sets.FloatMatrixSet) (f cvx.KKTFunc, err error) {

		minor := 0
		if !checkpnt.MinorEmpty() {
			minor = checkpnt.MinorTop()
		}

		err = nil
		f = nil
		beta := W.At("beta")[0].GetIndex(0)
		v := W.At("v")[0]

		// As = 2 * v *(v[1:].T * A)
		//v_1 := matrix.FloatNew(1, v.NumElements()-1, v.FloatArray()[1:])
		v_1 := v.SubMatrix(1, 0).Transpose()

		As := matrix.Times(v, matrix.Times(v_1, A)).Scale(2.0)

		//As_1 := As.GetSubMatrix(1, 0, m, n)
		//As_1.Scale(-1.0)
		//As.SetSubMatrix(1, 0, matrix.Minus(As_1, A))
		As_1 := As.SubMatrix(1, 0, m, n)
		As_1.Scale(-1.0)
		As_1.Minus(A)
		As.Scale(1.0 / beta)

		S := matrix.Times(As.Transpose(), As)
		checkpnt.AddMatrixVar("S", S)

		d1 := W.At("d")[0].SubMatrix(0, 0, n, 1).Copy()
		d2 := W.At("d")[0].SubMatrix(n, 0).Copy()

		// D = 4.0 * (d1**2 + d2**2)**-1
		d := matrix.Plus(matrix.Mul(d1, d1), matrix.Mul(d2, d2)).Inv().Scale(4.0)
		// S[::n+1] += d
		S.Diag().Plus(d.Transpose())

		err = lapack.Potrf(S)
		checkpnt.Check("00-Fkkt", minor)
		if err != nil {
			return
		}

		f = func(x, y, z *matrix.FloatMatrix) (err error) {

			minor := 0
			if !checkpnt.MinorEmpty() {
				minor = checkpnt.MinorTop()
			} else {
				loopf += 1
				minor = loopf
			}
			checkpnt.Check("00-f", minor)

			// -- z := - W**-T * z
			// z[:n] = -div( z[:n], d1 )
			z_val := z.SubMatrix(0, 0, n, 1)
			z_res := matrix.Div(z_val, d1).Scale(-1.0)
			z.SubMatrix(0, 0, n, 1).Set(z_res)

			// z[n:2*n] = -div( z[n:2*n], d2 )
			z_val = z.SubMatrix(n, 0, n, 1)
			z_res = matrix.Div(z_val, d2).Scale(-1.0)
			z.SubMatrix(n, 0, n, 1).Set(z_res)

			// z[2*n:] -= 2.0*v*( v[0]*z[2*n] - blas.dot(v[1:], z[2*n+1:]) )
			v0_z2n := v.GetIndex(0) * z.GetIndex(2*n)
			v1_z2n := blas.DotFloat(v, z, &linalg.IOpt{"offsetx", 1}, &linalg.IOpt{"offsety", 2*n + 1})
			z_res = matrix.Scale(v, -2.0*(v0_z2n-v1_z2n))
			z.SubMatrix(2*n, 0, z_res.NumElements(), 1).Plus(z_res)

			// z[2*n+1:] *= -1.0
			z.SubMatrix(2*n+1, 0).Scale(-1.0)

			// z[2*n:] /= beta
			z.SubMatrix(2*n, 0).Scale(1.0 / beta)

			// -- x := x - G' * W**-1 * z

			// z_n = z[:n], z_2n = z[n:2*n], z_m = z[-(m+1):],
			z_n := z.SubMatrix(0, 0, n, 1)
			z_2n := z.SubMatrix(n, 0, n, 1)
			z_m := z.SubMatrix(z.NumElements()-(m+1), 0)

			// x[:n] -= div(z[:n], d1) - div(z[n:2*n], d2) + As.T * z[-(m+1):]
			z_res = matrix.Minus(matrix.Div(z_n, d1), matrix.Div(z_2n, d2))
			a_res := matrix.Times(As.Transpose(), z_m)
			z_res.Plus(a_res).Scale(-1.0)
			x.SubMatrix(0, 0, n, 1).Plus(z_res)

			// x[n:] += div(z[:n], d1) + div(z[n:2*n], d2)
			z_res = matrix.Plus(matrix.Div(z_n, d1), matrix.Div(z_2n, d2))
			x.SubMatrix(n, 0, z_res.NumElements(), 1).Plus(z_res)
			checkpnt.Check("15-f", minor)

			// Solve for x[:n]:
			//
			//    S*x[:n] = x[:n] - (W1**2 - W2**2)(W1**2 + W2**2)^-1 * x[n:]

			// w1 = (d1**2 - d2**2), w2 = (d1**2 + d2**2)
			w1 := matrix.Minus(matrix.Mul(d1, d1), matrix.Mul(d2, d2))
			w2 := matrix.Plus(matrix.Mul(d1, d1), matrix.Mul(d2, d2))

			// x[:n] += -mul( div(w1, w2), x[n:])
			x_n := x.SubMatrix(n, 0)
			x_val := matrix.Mul(matrix.Div(w1, w2), x_n).Scale(-1.0)
			x.SubMatrix(0, 0, n, 1).Plus(x_val)
			checkpnt.Check("25-f", minor)

			// Solve for x[n:]:
			//
			//    (d1**-2 + d2**-2) * x[n:] = x[n:] + (d1**-2 - d2**-2)*x[:n]

			err = lapack.Potrs(S, x)
			if err != nil {
				fmt.Printf("Potrs error: %s\n", err)
			}
			checkpnt.Check("30-f", minor)

			// Solve for x[n:]:
			//
			//    (d1**-2 + d2**-2) * x[n:] = x[n:] + (d1**-2 - d2**-2)*x[:n]

			// w1 = (d1**-2 - d2**-2), w2 = (d1**-2 + d2**-2)
			w1 = matrix.Minus(matrix.Mul(d1, d1).Inv(), matrix.Mul(d2, d2).Inv())
			w2 = matrix.Plus(matrix.Mul(d1, d1).Inv(), matrix.Mul(d2, d2).Inv())
			x_n = x.SubMatrix(0, 0, n, 1)

			// x[n:] += mul( d1**-2 - d2**-2, x[:n])
			x_val = matrix.Mul(w1, x_n)
			x.SubMatrix(n, 0, x_val.NumElements(), 1).Plus(x_val)
			checkpnt.Check("35-f", minor)

			// x[n:] = div( x[n:], d1**-2 + d2**-2)
			x_n = x.SubMatrix(n, 0)
			x_val = matrix.Div(x_n, w2)
			x.SubMatrix(n, 0, x_val.NumElements(), 1).Set(x_val)
			checkpnt.Check("40-f", minor)

			// x_n = x[:n], x-2n = x[n:2*n]
			x_n = x.SubMatrix(0, 0, n, 1)
			x_2n := x.SubMatrix(n, 0, n, 1)

			// z := z + W^-T * G*x
			// z[:n] += div( x[:n] - x[n:2*n], d1)
			x_val = matrix.Div(matrix.Minus(x_n, x_2n), d1)
			z.SubMatrix(0, 0, n, 1).Plus(x_val)
			checkpnt.Check("44-f", minor)

			// z[n:2*n] += div( -x[:n] - x[n:2*n], d2)
			x_val = matrix.Div(matrix.Plus(x_n, x_2n).Scale(-1.0), d2)
			z.SubMatrix(n, 0, n, 1).Plus(x_val)
			checkpnt.Check("48-f", minor)

			// z[2*n:] += As*x[:n]
			x_val = matrix.Times(As, x_n)
			z.SubMatrix(2*n, 0, x_val.NumElements(), 1).Plus(x_val)

			checkpnt.Check("50-f", minor)

			return nil
		}
		return
	}

	// matrix(n*[0.0] + n*[1.0])
	c := matrix.FloatZeros(2*n, 1)
	c.SubMatrix(n, 0).SetIndexes(1.0)

	h := matrix.FloatZeros(2*n+m+1, 1)
	h.SetIndexes(1.0, 2*n)
	// h[2*n+1:] = -b
	h.SubMatrix(2*n+1, 0).Plus(b).Scale(-1.0)
	G := &matrixFs{A}

	dims := sets.DSetNew("l", "q", "s")
	dims.Set("l", []int{2 * n})
	dims.Set("q", []int{m + 1})

	var solopts cvx.SolverOptions
	solopts.ShowProgress = true
	if maxIter > 0 {
		solopts.MaxIter = maxIter
	}
	if len(solver) > 0 {
		solopts.KKTSolverName = solver
	}
	return cvx.ConeLpCustomMatrix(c, G, h, nil, nil, dims, Fkkt, &solopts, nil, nil)
}
Ejemplo n.º 15
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()
		}
	}

}