Golang FloatMatrix.Rows方法代码示例

func doScale(G *matrix.FloatMatrix, W *cvx.FloatMatrixSet) {
	g := matrix.FloatZeros(G.Rows(), 1)
	g.SetIndexes(matrix.MakeIndexSet(0, g.Rows(), 1), G.GetColumn(0, nil))
	fmt.Printf("** scaling g:\n%v\n", g)
	cvx.Scale(g, W, true, true)
	fmt.Printf("== scaled  g:\n%v\n", g)
}

// not really needed.
func createLdlSolver(G *matrix.FloatMatrix, dims *DimensionSet, A *matrix.FloatMatrix, mnl int) *kktLdlSolver {
	kkt := new(kktLdlSolver)

	kkt.p, kkt.n = A.Size()
	kkt.ldK = kkt.n + kkt.p + mnl + dims.Sum("l", "q") + dims.SumPacked("s")
	kkt.K = matrix.FloatZeros(kkt.ldK, kkt.ldK)
	kkt.ipiv = make([]int32, kkt.ldK)
	kkt.u = matrix.FloatZeros(kkt.ldK, 1)
	kkt.g = matrix.FloatZeros(kkt.mnl+G.Rows(), 1)
	kkt.G = G
	kkt.A = A
	kkt.dims = dims
	kkt.mnl = mnl
	return kkt
}

//    Solves a pair of primal and dual LPs
//
//        minimize    c'*x
//        subject to  G*x + s = h
//                    A*x = b
//                    s >= 0
//
//        maximize    -h'*z - b'*y
//        subject to  G'*z + A'*y + c = 0
//                    z >= 0.
//
func Lp(c, G, h, A, b *matrix.FloatMatrix, solopts *SolverOptions, primalstart, dualstart *FloatMatrixSet) (sol *Solution, err error) {

	if c == nil {
		err = errors.New("'c' must a column matrix")
		return
	}
	n := c.Rows()
	if n < 1 {
		err = errors.New("Number of variables must be at least 1")
		return
	}
	if G == nil || G.Cols() != n {
		err = errors.New(fmt.Sprintf("'G' must be matrix with %d columns", n))
		return
	}
	m := G.Rows()
	if h == nil || !h.SizeMatch(m, 1) {
		err = errors.New(fmt.Sprintf("'h' must be matrix of size (%d,1)", m))
		return
	}
	if A == nil {
		A = matrix.FloatZeros(0, n)
	}
	if A.Cols() != n {
		err = errors.New(fmt.Sprintf("'A' must be matrix with %d columns", n))
		return
	}
	p := A.Rows()
	if b == nil {
		b = matrix.FloatZeros(0, 1)
	}
	if !b.SizeMatch(p, 1) {
		err = errors.New(fmt.Sprintf("'b' must be matrix of size (%d,1)", p))
		return
	}
	dims := DSetNew("l", "q", "s")
	dims.Set("l", []int{m})

	return ConeLp(c, G, h, A, b, dims, solopts, primalstart, dualstart)
}

// Solution of KKT equations by a dense LDL factorization of the
// 3 x 3 system.
//
// Returns a function that (1) computes the LDL factorization of
//
// [ H           A'   GG'*W^{-1} ]
// [ A           0    0          ],
// [ W^{-T}*GG   0   -I          ]
//
// given H, Df, W, where GG = [Df; G], and (2) returns a function for
// solving
//
// [ H     A'   GG'   ]   [ ux ]   [ bx ]
// [ A     0    0     ] * [ uy ] = [ by ].
// [ GG    0   -W'*W  ]   [ uz ]   [ bz ]
//
// H is n x n,  A is p x n, Df is mnl x n, G is N x n where
// N = dims['l'] + sum(dims['q']) + sum( k**2 for k in dims['s'] ).
//
func kktLdl(G *matrix.FloatMatrix, dims *DimensionSet, A *matrix.FloatMatrix, mnl int) (kktFactor, error) {

	p, n := A.Size()
	ldK := n + p + mnl + dims.At("l")[0] + dims.Sum("q") + dims.SumPacked("s")
	K := matrix.FloatZeros(ldK, ldK)
	ipiv := make([]int32, ldK)
	u := matrix.FloatZeros(ldK, 1)
	g := matrix.FloatZeros(mnl+G.Rows(), 1)

	factor := func(W *FloatMatrixSet, H, Df *matrix.FloatMatrix) (kktFunc, error) {
		var err error = nil
		// Zero K for each call.
		blas.ScalFloat(K, 0.0)
		if H != nil {
			K.SetSubMatrix(0, 0, H)
		}
		K.SetSubMatrix(n, 0, A)
		//fmt.Printf("G=\n%v\n", G)
		for k := 0; k < n; k++ {
			// g is (mnl + G.Rows(), 1) matrix, Df is (mnl, n), G is (N, n)
			if mnl > 0 {
				// set values g[0:mnl] = Df[,k]
				g.SetIndexes(matrix.MakeIndexSet(0, mnl, 1), Df.GetColumnArray(k, nil))
			}
			// set values g[mnl:] = G[,k]
			g.SetIndexes(matrix.MakeIndexSet(mnl, mnl+g.Rows(), 1), G.GetColumnArray(k, nil))
			scale(g, W, true, true)
			if err != nil {
				fmt.Printf("scale error: %s\n", err)
			}
			pack(g, K, dims, &la_.IOpt{"mnl", mnl}, &la_.IOpt{"offsety", k*ldK + n + p})
		}
		setDiagonal(K, n+p, n+n, ldK, ldK, -1.0)
		//fmt.Printf("K=\n%v\n", K)
		err = lapack.Sytrf(K, ipiv)
		//fmt.Printf("sytrf: K=\n%v\n", K)
		if err != nil {
			return nil, err
		}

		solve := func(x, y, z *matrix.FloatMatrix) (err error) {
			// Solve
			//
			//     [ H          A'   GG'*W^{-1} ]   [ ux   ]   [ bx        ]
			//     [ A          0    0          ] * [ uy   [ = [ by        ]
			//     [ W^{-T}*GG  0   -I          ]   [ W*uz ]   [ W^{-T}*bz ]
			//
			// and return ux, uy, W*uz.
			//
			// On entry, x, y, z contain bx, by, bz.  On exit, they contain
			// the solution ux, uy, W*uz.
			//fmt.Printf("** start solve **\n")
			//fmt.Printf("x=\n%v\n", x.ConvertToString())
			//fmt.Printf("z=\n%v\n", z.ConvertToString())
			err = nil
			blas.Copy(x, u)
			blas.Copy(y, u, &la_.IOpt{"offsety", n})
			//fmt.Printf("solving: u=\n%v\n", u.ConvertToString())
			//W.Print()
			err = scale(z, W, true, true)
			//fmt.Printf("solving: post-scale z=\n%v\n", z.ConvertToString())
			if err != nil {
				return
			}
			err = pack(z, u, dims, &la_.IOpt{"mnl", mnl}, &la_.IOpt{"offsety", n + p})
			//fmt.Printf("solve: post-Pack {mnl=%d, n=%d, p=%d} u=\n%v\n",
			//	mnl, n, p, u.ConvertToString())
			if err != nil {
				return
			}

			err = lapack.Sytrs(K, u, ipiv)
			if err != nil {
				return
			}

			blas.Copy(u, x, &la_.IOpt{"n", n})
			blas.Copy(u, y, &la_.IOpt{"n", p}, &la_.IOpt{"offsetx", n})
			err = unpack(u, z, dims, &la_.IOpt{"mnl", mnl}, &la_.IOpt{"offsetx", n + p})
			//fmt.Printf("** end solve **\n")
			//fmt.Printf("x=\n%v\n", x.ConvertToString())
//.........这里部分代码省略.........

//    Solves a pair of primal and dual cone programs
//
//        minimize    c'*x
//        subject to  G*x + s = h
//                    A*x = b
//                    s >= 0
//
//        maximize    -h'*z - b'*y
//        subject to  G'*z + A'*y + c = 0
//                    z >= 0.
//
//    The inequalities are with respect to a cone C defined as the Cartesian
//    product of N + M + 1 cones:
//
//        C = C_0 x C_1 x .... x C_N x C_{N+1} x ... x C_{N+M}.
//
//    The first cone C_0 is the nonnegative orthant of dimension ml.
//    The next N cones are second order cones of dimension mq[0], ...,
//    mq[N-1].  The second order cone of dimension m is defined as
//
//        { (u0, u1) in R x R^{m-1} | u0 >= ||u1||_2 }.
//
//    The next M cones are positive semidefinite cones of order ms[0], ...,
//    ms[M-1] >= 0.
//
func ConeLp(c, G, h, A, b *matrix.FloatMatrix, dims *DimensionSet, solopts *SolverOptions, primalstart, dualstart *FloatMatrixSet) (sol *Solution, err error) {

	err = nil
	const EXPON = 3
	const STEP = 0.99

	sol = &Solution{Unknown,
		nil, nil, nil, nil, nil,
		0.0, 0.0, 0.0, 0.0, 0.0,
		0.0, 0.0, 0.0, 0.0, 0.0, 0}

	//var primalstart *FloatMatrixSet = nil
	//var dualstart *FloatMatrixSet = nil
	var refinement int

	if solopts.Refinement > 0 {
		refinement = solopts.Refinement
	} else {
		refinement = 0
		if len(dims.At("q")) > 0 || len(dims.At("s")) > 0 {
			refinement = 1
		}
	}
	feasTolerance := FEASTOL
	absTolerance := ABSTOL
	relTolerance := RELTOL
	if solopts.FeasTol > 0.0 {
		feasTolerance = solopts.FeasTol
	}
	if solopts.AbsTol > 0.0 {
		absTolerance = solopts.AbsTol
	}
	if solopts.RelTol > 0.0 {
		relTolerance = solopts.RelTol
	}

	solvername := solopts.KKTSolverName
	if len(solvername) == 0 {
		if dims != nil && (len(dims.At("q")) > 0 || len(dims.At("s")) > 0) {
			solvername = "qr"
		} else {
			solvername = "chol2"
		}
	}

	if c == nil || c.Cols() > 1 {
		err = errors.New("'c' must be matrix with 1 column")
		return
	}
	if h == nil || h.Cols() > 1 {
		err = errors.New("'h' must be matrix with 1 column")
		return
	}

	if dims == nil {
		dims = DSetNew("l", "q", "s")
		dims.Set("l", []int{h.Rows()})
	}
	if err = checkConeLpDimensions(dims); err != nil {
		return
	}

	cdim := dims.Sum("l", "q") + dims.SumSquared("s")
	cdim_diag := dims.Sum("l", "q", "s")

	if h.Rows() != cdim {
		err = errors.New(fmt.Sprintf("'h' must be float matrix of size (%d,1)", cdim))
		return
	}

	// Data for kth 'q' constraint are found in rows indq[k]:indq[k+1] of G.
	indq := make([]int, 0, 100)
	indq = append(indq, dims.At("l")[0])
	for _, k := range dims.At("q") {
		indq = append(indq, indq[len(indq)-1]+k)
//.........这里部分代码省略.........

/*
   Applies Nesterov-Todd scaling or its inverse.

   Computes

        x := W*x        (trans is false 'N', inverse = false 'N')
        x := W^T*x      (trans is true  'T', inverse = false 'N')
        x := W^{-1}*x   (trans is false 'N', inverse = true  'T')
        x := W^{-T}*x   (trans is true  'T', inverse = true  'T').

   x is a dense float matrix.

   W is a MatrixSet with entries:

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

   The 'dnl' and 'dnli' entries are optional, and only present when the
   function is called from the nonlinear solver.
*/
func scale(x *matrix.FloatMatrix, W *FloatMatrixSet, trans, inverse bool) (err error) {
	/*DEBUGGED*/
	var wl []*matrix.FloatMatrix
	var w *matrix.FloatMatrix
	ind := 0
	err = nil

	// Scaling for nonlinear component xk is xk := dnl .* xk; inverse
	// scaling is xk ./ dnl = dnli .* xk, where dnl = W['dnl'],
	// dnli = W['dnli'].

	if wl = W.At("dnl"); wl != nil {
		if inverse {
			w = W.At("dnli")[0]
		} else {
			w = W.At("dnl")[0]
		}
		for k := 0; k < x.Cols(); k++ {
			err = blas.TbmvFloat(w, x, &la_.IOpt{"n", w.Rows()}, &la_.IOpt{"k", 0},
				&la_.IOpt{"lda", 1}, &la_.IOpt{"offsetx", k * x.Rows()})
			if err != nil {
				return
			}
		}
		ind += w.Rows()
	}

	// Scaling for linear 'l' component xk is xk := d .* xk; inverse
	// scaling is xk ./ d = di .* xk, where d = W['d'], di = W['di'].

	if inverse {
		w = W.At("di")[0]
	} else {
		w = W.At("d")[0]
	}

	for k := 0; k < x.Cols(); k++ {
		err = blas.TbmvFloat(w, x, &la_.IOpt{"n", w.Rows()}, &la_.IOpt{"k", 0},
			&la_.IOpt{"lda", 1}, &la_.IOpt{"offsetx", k*x.Rows() + ind})
		if err != nil {
			return
		}
	}
	ind += w.Rows()

	// Scaling for 'q' component is
	//
	//    xk := beta * (2*v*v' - J) * xk
	//        = beta * (2*v*(xk'*v)' - J*xk)
	//
	// where beta = W['beta'][k], v = W['v'][k], J = [1, 0; 0, -I].
	//
	//Inverse scaling is
	//
	//    xk := 1/beta * (2*J*v*v'*J - J) * xk
	//        = 1/beta * (-J) * (2*v*((-J*xk)'*v)' + xk).
	//wf := matrix.FloatZeros(x.Cols(), 1)
	w = matrix.FloatZeros(x.Cols(), 1)
	for k, v := range W.At("v") {
		m := v.Rows()
		if inverse {
			blas.ScalFloat(x, -1.0, &la_.IOpt{"offset", ind}, &la_.IOpt{"inc", x.Rows()})
		}
		err = blas.GemvFloat(x, v, w, 1.0, 0.0, la_.OptTrans, &la_.IOpt{"m", m},
			&la_.IOpt{"n", x.Cols()}, &la_.IOpt{"offsetA", ind},
			&la_.IOpt{"lda", x.Rows()})
		if err != nil {
			return
		}

		err = blas.ScalFloat(x, -1.0, &la_.IOpt{"offset", ind}, &la_.IOpt{"inc", x.Rows()})
		if err != nil {
			return
//.........这里部分代码省略.........

// Computes analytic center of A*x <= b with A m by n of rank n.
// We assume that b > 0 and the feasible set is bounded.
func Acent(A, b *matrix.FloatMatrix, niters int) (*matrix.FloatMatrix, []float64) {

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

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

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

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

		// Gradient is g = A^T * (1.0/(b - A*x)). d = 1.0/(b - A*x)
		// d is m*1 matrix, g is n*1 matrix
		d := b.Minus(A.Times(x))
		d.Apply(d, func(a float64) float64 { return 1.0 / a })
		g := A.Transpose().Times(d)

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

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

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

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

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

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

			// ts = sum(log(1-step*y))
			ts := t.Log().Sum()
			if -ts < ALPHA*step*lam {
				break search
			}
			step *= BETA
		}
		v.Scale(step)
		x = x.Plus(v)
	}
	// no solution !!
	fmt.Printf("Iteration %d exhausted\n", niters)
	return x, ntdecrs
}

//    Solves a quadratic program
//
//        minimize    (1/2)*x'*P*x + q'*x
//        subject to  G*x <= h
//                    A*x = b.
//
//
//    Input arguments.
//
//        P is a n x n float matrix with the lower triangular part of P stored
//        in the lower triangle.  Must be positive semidefinite.
//
//        q is an n x 1 matrix.
//
//        G is an m x n matrix or nil.
//
//        h is an m x 1 matrix or nil.
//
//        A is a p x n matrix or nil.
//
//        b is a p x 1 matrix or nil.
//
//        The default values for G, h, A and b are empty matrices with zero rows.
//
//
func Qp(P, q, G, h, A, b *matrix.FloatMatrix, solopts *SolverOptions, initvals *FloatMatrixSet) (sol *Solution, err error) {

	sol = nil
	if P == nil || P.Rows() != P.Cols() {
		err = errors.New("'P' must a non-nil square matrix")
		return
	}
	if q == nil {
		err = errors.New("'q' must a non-nil matrix")
		return
	}
	if q.Rows() != P.Rows() || q.Cols() > 1 {
		err = errors.New(fmt.Sprintf("'q' must be matrix of size (%d,1)", P.Rows()))
		return
	}
	if G == nil {
		G = matrix.FloatZeros(0, P.Rows())
	}
	if G.Cols() != P.Rows() {
		err = errors.New(fmt.Sprintf("'G' must be matrix of %d columns", P.Rows()))
		return
	}
	if h == nil {
		h = matrix.FloatZeros(G.Rows(), 1)
	}
	if h.Rows() != G.Rows() || h.Cols() > 1 {
		err = errors.New(fmt.Sprintf("'h' must be matrix of size (%d,1)", G.Rows()))
		return
	}
	if A == nil {
		A = matrix.FloatZeros(0, P.Rows())
	}
	if A.Cols() != P.Rows() {
		err = errors.New(fmt.Sprintf("'A' must be matrix of %d columns", P.Rows()))
		return
	}
	if b == nil {
		b = matrix.FloatZeros(A.Rows(), 1)
	}
	if b.Rows() != A.Rows() {
		err = errors.New(fmt.Sprintf("'b' must be matrix of size (%d,1)", A.Rows()))
		return
	}
	return ConeQp(P, q, G, h, A, b, nil, solopts, initvals)
}

//    Solves a pair of primal and dual SDPs
//
//        minimize    c'*x
//        subject to  Gl*x + sl = hl
//                    mat(Gs[k]*x) + ss[k] = hs[k], k = 0, ..., N-1
//                    A*x = b
//                    sl >= 0,  ss[k] >= 0, k = 0, ..., N-1
//
//        maximize    -hl'*z - sum_k trace(hs[k]*zs[k]) - b'*y
//        subject to  Gl'*zl + sum_k Gs[k]'*vec(zs[k]) + A'*y + c = 0
//                    zl >= 0,  zs[k] >= 0, k = 0, ..., N-1.
//
//    The inequalities sl >= 0 and zl >= 0 are elementwise vector
//    inequalities.  The inequalities ss[k] >= 0, zs[k] >= 0 are matrix
//    inequalities, i.e., the symmetric matrices ss[k] and zs[k] must be
//    positive semidefinite.  mat(Gs[k]*x) is the symmetric matrix X with
//    X[:] = Gs[k]*x.  For a symmetric matrix, zs[k], vec(zs[k]) is the
//    vector zs[k][:].
//
func Sdp(c, Gl, hl, A, b *matrix.FloatMatrix, Ghs *FloatMatrixSet, solopts *SolverOptions, primalstart, dualstart *FloatMatrixSet) (sol *Solution, err error) {
	if c == nil {
		err = errors.New("'c' must a column matrix")
		return
	}
	n := c.Rows()
	if n < 1 {
		err = errors.New("Number of variables must be at least 1")
		return
	}
	if Gl == nil {
		Gl = matrix.FloatZeros(0, n)
	}
	if Gl.Cols() != n {
		err = errors.New(fmt.Sprintf("'G' must be matrix with %d columns", n))
		return
	}
	ml := Gl.Rows()
	if hl == nil {
		hl = matrix.FloatZeros(0, 1)
	}
	if !hl.SizeMatch(ml, 1) {
		err = errors.New(fmt.Sprintf("'hl' must be matrix of size (%d,1)", ml))
		return
	}
	Gsset := Ghs.At("Gs")
	ms := make([]int, 0)
	for i, Gs := range Gsset {
		if Gs.Cols() != n {
			err = errors.New(fmt.Sprintf("'Gs' must be list of matrices with %d columns", n))
			return
		}
		sz := int(math.Sqrt(float64(Gs.Rows())))
		if Gs.Rows() != sz*sz {
			err = errors.New(fmt.Sprintf("the squareroot of the number of rows of 'Gq[%d]' is not an integer", i))
			return
		}
		ms = append(ms, sz)
	}

	hsset := Ghs.At("hs")
	if len(Gsset) != len(hsset) {
		err = errors.New(fmt.Sprintf("'hs' must be a list of %d matrices", len(Gsset)))
		return
	}
	for i, hs := range hsset {
		if !hs.SizeMatch(ms[i], ms[i]) {
			s := fmt.Sprintf("hq[%d] has size (%d,%d). Expected size is (%d,%d)",
				i, hs.Rows(), hs.Cols(), ms[i], ms[i])
			err = errors.New(s)
			return
		}
	}
	if A == nil {
		A = matrix.FloatZeros(0, n)
	}
	if A.Cols() != n {
		err = errors.New(fmt.Sprintf("'A' must be matrix with %d columns", n))
		return
	}
	p := A.Rows()
	if b == nil {
		b = matrix.FloatZeros(0, 1)
	}
	if !b.SizeMatch(p, 1) {
		err = errors.New(fmt.Sprintf("'b' must be matrix of size (%d,1)", p))
		return
	}
	dims := DSetNew("l", "q", "s")
	dims.Set("l", []int{ml})
	dims.Set("s", ms)
	N := dims.Sum("l") + dims.SumSquared("s")

	// Map hs matrices to h vector
	h := matrix.FloatZeros(N, 1)
	h.SetIndexes(matrix.MakeIndexSet(0, ml, 1), hl.FloatArray()[:ml])
	ind := ml
	for k, hs := range hsset {
		h.SetIndexes(matrix.MakeIndexSet(ind, ind+ms[k]*ms[k], 1), hs.FloatArray())
		ind += ms[k] * ms[k]
	}
//.........这里部分代码省略.........

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

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

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

//.........这里部分代码省略.........

//    Solves a pair of primal and dual convex quadratic cone programs
//
//        minimize    (1/2)*x'*P*x + q'*x
//        subject to  G*x + s = h
//                    A*x = b
//                    s >= 0
//
//        maximize    -(1/2)*(q + G'*z + A'*y)' * pinv(P) * (q + G'*z + A'*y)
//                    - h'*z - b'*y
//        subject to  q + G'*z + A'*y in range(P)
//                    z >= 0.
//
//    The inequalities are with respect to a cone C defined as the Cartesian
//    product of N + M + 1 cones:
//
//        C = C_0 x C_1 x .... x C_N x C_{N+1} x ... x C_{N+M}.
//
//    The first cone C_0 is the nonnegative orthant of dimension ml.
//    The next N cones are 2nd order cones of dimension mq[0], ..., mq[N-1].
//    The second order cone of dimension m is defined as
//
//        { (u0, u1) in R x R^{m-1} | u0 >= ||u1||_2 }.
//
//    The next M cones are positive semidefinite cones of order ms[0], ...,
//    ms[M-1] >= 0.
//
func ConeQp(P, q, G, h, A, b *matrix.FloatMatrix, dims *DimensionSet, solopts *SolverOptions, initvals *FloatMatrixSet) (sol *Solution, err error) {

	err = nil
	EXPON := 3
	STEP := 0.99

	sol = &Solution{Unknown,
		nil, nil, nil, nil, nil,
		0.0, 0.0, 0.0, 0.0, 0.0,
		0.0, 0.0, 0.0, 0.0, 0.0, 0}

	var kktsolver func(*FloatMatrixSet) (kktFunc, error) = nil
	var refinement int
	var correction bool = true

	feasTolerance := FEASTOL
	absTolerance := ABSTOL
	relTolerance := RELTOL
	if solopts.FeasTol > 0.0 {
		feasTolerance = solopts.FeasTol
	}
	if solopts.AbsTol > 0.0 {
		absTolerance = solopts.AbsTol
	}
	if solopts.RelTol > 0.0 {
		relTolerance = solopts.RelTol
	}

	solvername := solopts.KKTSolverName
	if len(solvername) == 0 {
		if dims != nil && (len(dims.At("q")) > 0 || len(dims.At("s")) > 0) {
			solvername = "qr"
			//kktsolver = solvers["qr"]
		} else {
			solvername = "chol2"
			//kktsolver = solvers["chol2"]
		}
	}

	if q == nil || q.Cols() != 1 {
		err = errors.New("'q' must be non-nil matrix with one column")
		return
	}
	if P == nil || P.Rows() != q.Rows() || P.Cols() != q.Rows() {
		err = errors.New(fmt.Sprintf("'P' must be non-nil matrix of size (%d, %d)",
			q.Rows(), q.Rows()))
		return
	}
	fP := func(x, y *matrix.FloatMatrix, alpha, beta float64) error {
		return blas.SymvFloat(P, x, y, alpha, beta)
	}

	if h == nil {
		h = matrix.FloatZeros(0, 1)
	}
	if h.Cols() != 1 {
		err = errors.New("'h' must be non-nil matrix with one column")
		return
	}
	if dims == nil {
		dims = DSetNew("l", "q", "s")
		dims.Set("l", []int{h.Rows()})
	}

	err = checkConeQpDimensions(dims)
	if err != nil {
		return
	}

	cdim := dims.Sum("l", "q") + dims.SumSquared("s")
	//cdim_pckd := dims.Sum("l", "q") + dims.SumPacked("s")
	cdim_diag := dims.Sum("l", "q", "s")

	if h.Rows() != cdim {
//.........这里部分代码省略.........