Golang sets.DimensionSet类代码示例

/*
   Matrix-vector multiplication.

   A is a matrix or spmatrix of size (m, n) where

       N = dims['l'] + sum(dims['q']) + sum( k**2 for k in dims['s'] )

   representing a mapping from R^n to S.

   If trans is 'N':

       y := alpha*A*x + beta * y   (trans = 'N').

   x is a vector of length n.  y is a vector of length N.

   If trans is 'T':

       y := alpha*A'*x + beta * y  (trans = 'T').

   x is a vector of length N.  y is a vector of length n.

   The 's' components in S are stored in unpacked 'L' storage.
*/
func sgemv(A, x, y *matrix.FloatMatrix, alpha, beta float64, dims *sets.DimensionSet, opts ...la_.Option) error {

	m := dims.Sum("l", "q") + dims.SumSquared("s")
	n := la_.GetIntOpt("n", -1, opts...)
	if n == -1 {
		n = A.Cols()
	}
	trans := la_.GetIntOpt("trans", int(la_.PNoTrans), opts...)
	offsetX := la_.GetIntOpt("offsetx", 0, opts...)
	offsetY := la_.GetIntOpt("offsety", 0, opts...)
	offsetA := la_.GetIntOpt("offseta", 0, opts...)

	if trans == int(la_.PTrans) && alpha != 0.0 {
		trisc(x, dims, offsetX)
		//fmt.Printf("trisc x=\n%v\n", x.ConvertToString())
	}
	//fmt.Printf("alpha=%.4f beta=%.4f m=%d n=%d\n", alpha, beta, m, n)
	//fmt.Printf("A=\n%v\nx=\n%v\ny=\n%v\n", A, x.ConvertToString(), y.ConvertToString())
	err := blas.GemvFloat(A, x, y, alpha, beta, &la_.IOpt{"trans", trans},
		&la_.IOpt{"n", n}, &la_.IOpt{"m", m}, &la_.IOpt{"offseta", offsetA},
		&la_.IOpt{"offsetx", offsetX}, &la_.IOpt{"offsety", offsetY})
	//fmt.Printf("gemv y=\n%v\n", y.ConvertToString())

	if trans == int(la_.PTrans) && alpha != 0.0 {
		triusc(x, dims, offsetX)
	}
	return err
}

/*
   Scales the strictly lower triangular part of the 's' components of x
   by 0.5.

*/
func triusc(x *matrix.FloatMatrix, dims *sets.DimensionSet, offset int) error {

	//m := dims.Sum("l", "q") + dims.SumSquared("s")
	ind := offset + dims.Sum("l", "q")

	for _, mk := range dims.At("s") {
		for j := 1; j < mk; j++ {
			blas.ScalFloat(x, 0.5, &la_.IOpt{"n", mk - j}, &la_.IOpt{"offset", ind + mk*(j-1) + j})
		}
		ind += mk * mk
	}
	return nil
}

/*
   Copy x to y using packed storage.

   The vector x is an element of S, with the 's' components stored in
   unpacked storage.  On return, x is copied to y with the 's' components
   stored in packed storage and the off-diagonal entries scaled by
   sqrt(2).
*/
func pack(x, y *matrix.FloatMatrix, dims *sets.DimensionSet, opts ...la_.Option) (err error) {
	/*DEBUGGED*/
	err = nil
	mnl := la_.GetIntOpt("mnl", 0, opts...)
	offsetx := la_.GetIntOpt("offsetx", 0, opts...)
	offsety := la_.GetIntOpt("offsety", 0, opts...)

	nlq := mnl + dims.At("l")[0] + dims.Sum("q")
	blas.Copy(x, y, &la_.IOpt{"n", nlq}, &la_.IOpt{"offsetx", offsetx},
		&la_.IOpt{"offsety", offsety})

	iu, ip := offsetx+nlq, offsety+nlq
	for _, n := range dims.At("s") {
		for k := 0; k < n; k++ {
			blas.Copy(x, y, &la_.IOpt{"n", n - k}, &la_.IOpt{"offsetx", iu + k*(n+1)},
				&la_.IOpt{"offsety", ip})
			y.SetIndex(ip, (y.GetIndex(ip) / math.Sqrt(2.0)))
			ip += n - k
		}
		iu += n * n
	}
	np := dims.SumPacked("s")
	blas.ScalFloat(y, math.Sqrt(2.0), &la_.IOpt{"n", np}, &la_.IOpt{"offset", offsety + nlq})
	return
}

/*
   Sets upper triangular part of the 's' components of x equal to zero
   and scales the strictly lower triangular part by 2.0.
*/
func trisc(x *matrix.FloatMatrix, dims *sets.DimensionSet, offset int) error {

	//m := dims.Sum("l", "q") + dims.SumSquared("s")
	ind := offset + dims.Sum("l", "q")

	for _, mk := range dims.At("s") {
		for j := 1; j < mk; j++ {
			blas.ScalFloat(x, 0.0, la_.IntOpt("n", mk-j), la_.IntOpt("inc", mk),
				la_.IntOpt("offset", ind+j*(mk+1)-1))
			blas.ScalFloat(x, 2.0, la_.IntOpt("n", mk-j), la_.IntOpt("offset", ind+mk*(j-1)+j))
		}
		ind += mk * mk
	}
	return nil
}

func checkConeLpDimensions(dims *sets.DimensionSet) error {
	if len(dims.At("l")) == 0 {
		dims.Set("l", []int{0})
	} else if dims.At("l")[0] < 0 {
		return errors.New("dimension 'l' must be nonnegative integer")
	}
	for _, m := range dims.At("q") {
		if m < 1 {
			return errors.New("dimension 'q' must be list of positive integers")
		}
	}
	for _, m := range dims.At("s") {
		if m < 1 {
			return errors.New("dimension 's' must be list of positive integers")
		}
	}
	return nil
}

// In-place version of pack(), which also accepts matrix arguments x.
// The columns of x are elements of S, with the 's' components stored
// in unpacked storage.  On return, the 's' components are stored in
// packed storage and the off-diagonal entries are scaled by sqrt(2).
//
func pack2(x *matrix.FloatMatrix, dims *sets.DimensionSet, mnl int) (err error) {
	if len(dims.At("s")) == 0 {
		return nil
	}

	const sqrt2 = 1.41421356237309504880

	iu := mnl + dims.Sum("l", "q")
	ip := iu
	row := matrix.FloatZeros(1, x.Cols())
	//fmt.Printf("x.size = %d %d\n", x.Rows(), x.Cols())
	for _, n := range dims.At("s") {
		for k := 0; k < n; k++ {
			cnt := n - k
			row = x.GetRow(iu+(n+1)*k, row)
			//fmt.Printf("%02d: %v\n", iu+(n+1)*k, x.FloatArray())
			x.SetRow(ip, row)
			for i := 1; i < n-k; i++ {
				row = x.GetRow(iu+(n+1)*k+i, row)
				//fmt.Printf("%02d: %v\n", iu+(n+1)*k+i, x.FloatArray())
				x.SetRow(ip+i, row.Scale(sqrt2))
			}
			ip += cnt
		}
		iu += n * n
	}
	return nil
}

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
}

// Returns min {t | x + t*e >= 0}, where e is defined as follows
//
//  - For the nonlinear and 'l' blocks: e is the vector of ones.
//  - For the 'q' blocks: e is the first unit vector.
//  - For the 's' blocks: e is the identity matrix.
//
// When called with the argument sigma, also returns the eigenvalues
// (in sigma) and the eigenvectors (in x) of the 's' components of x.
func maxStep(x *matrix.FloatMatrix, dims *sets.DimensionSet, mnl int, sigma *matrix.FloatMatrix) (rval float64, err error) {
	/*DEBUGGED*/

	rval = 0.0
	err = nil
	t := make([]float64, 0, 10)
	ind := mnl + dims.Sum("l")
	if ind > 0 {
		t = append(t, -minvec(x.FloatArray()[:ind]))
	}
	for _, m := range dims.At("q") {
		if m > 0 {
			v := blas.Nrm2Float(x, &la_.IOpt{"offset", ind + 1}, &la_.IOpt{"n", m - 1})
			v -= x.GetIndex(ind)
			t = append(t, v)
		}
		ind += m
	}

	//var Q *matrix.FloatMatrix
	//var w *matrix.FloatMatrix
	ind2 := 0
	//if sigma == nil && len(dims.At("s")) > 0 {
	//	mx := dims.Max("s")
	//	Q = matrix.FloatZeros(mx, mx)
	//	w = matrix.FloatZeros(mx, 1)
	//}
	for _, m := range dims.At("s") {
		if sigma == nil {
			Q := matrix.FloatZeros(m, m)
			w := matrix.FloatZeros(m, 1)
			blas.Copy(x, Q, &la_.IOpt{"offsetx", ind}, &la_.IOpt{"n", m * m})
			err = lapack.SyevrFloat(Q, w, nil, 0.0, nil, []int{1, 1}, la_.OptRangeInt,
				&la_.IOpt{"n", m}, &la_.IOpt{"lda", m})
			if m > 0 && err == nil {
				t = append(t, -w.GetIndex(0))
			}
		} else {
			err = lapack.SyevdFloat(x, sigma, la_.OptJobZValue, &la_.IOpt{"n", m},
				&la_.IOpt{"lda", m}, &la_.IOpt{"offseta", ind}, &la_.IOpt{"offsetw", ind2})
			if m > 0 {
				t = append(t, -sigma.GetIndex(ind2))
			}
		}
		ind += m * m
		ind2 += m
	}

	if len(t) > 0 {
		rval = maxvec(t)
	}
	return
}

// Inner product of two vectors in S.
func sdot(x, y *matrix.FloatMatrix, dims *sets.DimensionSet, mnl int) float64 {
	/*DEBUGGED*/
	ind := mnl + dims.At("l")[0] + dims.Sum("q")
	a := blas.DotFloat(x, y, &la_.IOpt{"n", ind})
	for _, m := range dims.At("s") {
		a += blas.DotFloat(x, y, &la_.IOpt{"offsetx", ind}, &la_.IOpt{"offsety", ind},
			&la_.IOpt{"incx", m + 1}, &la_.IOpt{"incy", m + 1}, &la_.IOpt{"n", m})
		for j := 1; j < m; j++ {
			a += 2.0 * blas.DotFloat(x, y, &la_.IOpt{"offsetx", ind + j}, &la_.IOpt{"offsety", ind + j},
				&la_.IOpt{"incx", m + 1}, &la_.IOpt{"incy", m + 1}, &la_.IOpt{"n", m - j})
		}
		ind += m * m
	}
	return a
}

// The product x := y o y.   The 's' components of y are diagonal and
// only the diagonals of x and y are stored.
func ssqr(x, y *matrix.FloatMatrix, dims *sets.DimensionSet, mnl int) (err error) {
	/*DEBUGGED*/
	blas.Copy(y, x)
	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
	}

	for _, m := range dims.At("q") {
		v := blas.Nrm2Float(y, &la_.IOpt{"n", m}, &la_.IOpt{"offset", ind})
		x.SetIndex(ind, v*v)
		blas.ScalFloat(x, 2.0*y.GetIndex(ind), &la_.IOpt{"n", m - 1}, &la_.IOpt{"offset", ind + 1})
		ind += m
	}
	err = blas.Tbmv(y, x, &la_.IOpt{"n", dims.Sum("s")}, &la_.IOpt{"k", 0},
		&la_.IOpt{"lda", 1}, &la_.IOpt{"offseta", ind}, &la_.IOpt{"offsetx", ind})
	return
}

/*
   The vector x is an element of S, with the 's' components stored
   in unpacked storage and off-diagonal entries scaled by sqrt(2).
   On return, x is copied to y with the 's' components stored in
   unpacked storage.

*/
func unpack(x, y *matrix.FloatMatrix, dims *sets.DimensionSet, opts ...la_.Option) (err error) {
	/*DEBUGGED*/
	err = nil
	mnl := la_.GetIntOpt("mnl", 0, opts...)
	offsetx := la_.GetIntOpt("offsetx", 0, opts...)
	offsety := la_.GetIntOpt("offsety", 0, opts...)

	nlq := mnl + dims.At("l")[0] + dims.Sum("q")
	err = blas.Copy(x, y, &la_.IOpt{"n", nlq}, &la_.IOpt{"offsetx", offsetx},
		&la_.IOpt{"offsety", offsety})
	if err != nil {
		return
	}

	ip, iu := offsetx+nlq, offsety+nlq
	for _, n := range dims.At("s") {
		for k := 0; k < n; k++ {
			err = blas.Copy(x, y, &la_.IOpt{"n", n - k}, &la_.IOpt{"offsetx", ip},
				&la_.IOpt{"offsety", iu + k*(n+1)})
			if err != nil {
				return
			}

			ip += n - k
			blas.ScalFloat(y, 1.0/math.Sqrt(2.0),
				&la_.IOpt{"n", n - k - 1}, &la_.IOpt{"offset", iu + k*(n+1) + 1})
		}
		iu += n * n
	}
	/*
		nu := dims.SumSquared("s")
		fmt.Printf("-- UnPack: nu=%d, offset=%d\n", nu, offsety+nlq)
		err = blas.ScalFloat(y,
			&la_.IOpt{"n", nu}, &la_.IOpt{"offset", offsety+nlq})
	*/
	return
}

// Solves a convex optimization problem with a linear objective
//
//        minimize    c'*x
//        subject to  f(x) <= 0
//                    G*x <= h
//                    A*x = b.
//
// using custom KTT equation solver and custom constraints G and A.
//
func CplCustomMatrix(F ConvexProg, c *matrix.FloatMatrix, G MatrixG, h *matrix.FloatMatrix,
	A MatrixA, b *matrix.FloatMatrix, dims *sets.DimensionSet, kktsolver KKTCpSolver,
	solopts *SolverOptions) (sol *Solution, err error) {

	var mnl int
	var x0 *matrix.FloatMatrix

	mnl, x0, err = F.F0()
	if err != nil {
		return
	}

	if x0.Cols() != 1 {
		err = errors.New("'x0' must be matrix with one column")
		return
	}
	if c == nil {
		err = errors.New("'c' must be non nil matrix")
		return
	}
	if !c.SizeMatch(x0.Size()) {
		err = errors.New(fmt.Sprintf("'c' must be matrix of size (%d,1)", x0.Rows()))
		return
	}

	if h == nil {
		h = matrix.FloatZeros(0, 1)
	}
	if h.Cols() > 1 {
		err = errors.New("'h' must be matrix with 1 column")
		return
	}

	if dims == nil {
		dims = sets.NewDimensionSet("l", "q", "s")
		dims.Set("l", []int{h.Rows()})
	}

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

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

	// Check b and set defaults if it is nil
	if b == nil {
		b = matrix.FloatZeros(0, 1)
	}
	if b.Cols() != 1 {
		estr := fmt.Sprintf("'b' must be a matrix with 1 column")
		err = errors.New(estr)
		return
	}

	mc := matrixVar{c}
	mb := matrixVar{b}
	var mG MatrixVarG
	var mA MatrixVarA

	if G == nil {
		mG = &matrixVarG{matrix.FloatZeros(0, c.Rows()), dims}
	} else {
		mG = &matrixIfG{G}
	}
	if A == nil {
		mA = &matrixVarA{matrix.FloatZeros(0, c.Rows())}
	} else {
		mA = &matrixIfA{A}
	}

	return cpl_problem(F, &mc, mG, h, mA, &mb, dims, kktsolver, solopts, x0, mnl)
}

// Solves a convex optimization problem with a linear objective
//
//        minimize    c'*x
//        subject to  f(x) <= 0
//                    G*x <= h
//                    A*x = b.
//
// using custom KTT equation solver.
//
func CplCustomKKT(F ConvexProg, c *matrix.FloatMatrix, G, h, A, b *matrix.FloatMatrix,
	dims *sets.DimensionSet, kktsolver KKTCpSolver,
	solopts *SolverOptions) (sol *Solution, err error) {

	var mnl int
	var x0 *matrix.FloatMatrix

	mnl, x0, err = F.F0()
	if err != nil {
		return
	}

	if x0.Cols() != 1 {
		err = errors.New("'x0' must be matrix with one column")
		return
	}
	if c == nil {
		err = errors.New("'c' must be non nil matrix")
		return
	}
	if !c.SizeMatch(x0.Size()) {
		err = errors.New(fmt.Sprintf("'c' must be matrix of size (%d,1)", x0.Rows()))
		return
	}

	if h == nil {
		h = matrix.FloatZeros(0, 1)
	}
	if h.Cols() > 1 {
		err = errors.New("'h' must be matrix with 1 column")
		return
	}

	if dims == nil {
		dims = sets.NewDimensionSet("l", "q", "s")
		dims.Set("l", []int{h.Rows()})
	}

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

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

	if G == nil {
		G = matrix.FloatZeros(0, c.Rows())
	}
	if !G.SizeMatch(cdim, c.Rows()) {
		estr := fmt.Sprintf("'G' must be of size (%d,%d)", cdim, c.Rows())
		err = errors.New(estr)
		return
	}

	// Check A and set defaults if it is nil
	if A == nil {
		// zeros rows reduces Gemv to vector products
		A = matrix.FloatZeros(0, c.Rows())
	}
	if A.Cols() != c.Rows() {
		estr := fmt.Sprintf("'A' must have %d columns", c.Rows())
		err = errors.New(estr)
		return
	}

	// Check b and set defaults if it is nil
	if b == nil {
		b = matrix.FloatZeros(0, 1)
	}
	if b.Cols() != 1 {
		estr := fmt.Sprintf("'b' must be a matrix with 1 column")
		err = errors.New(estr)
		return
	}
	if b.Rows() != A.Rows() {
		estr := fmt.Sprintf("'b' must have length %d", A.Rows())
		err = errors.New(estr)
		return
	}

	var mc = matrixVar{c}
	var mb = matrixVar{b}
	var mA = matrixVarA{A}
	var mG = matrixVarG{G, dims}

	return cpl_problem(F, &mc, &mG, h, &mA, &mb, dims, kktsolver, solopts, x0, mnl)
}

// Solves a pair of primal and dual cone programs using custom KKT solver and constraint
// interfaces MatrixG and MatrixA
//
func ConeLpCustomMatrix(c *matrix.FloatMatrix, G MatrixG, h *matrix.FloatMatrix,
	A MatrixA, b *matrix.FloatMatrix, dims *sets.DimensionSet, kktsolver KKTConeSolver,
	solopts *SolverOptions, primalstart, dualstart *sets.FloatMatrixSet) (sol *Solution, err error) {

	err = nil

	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 err = checkConeLpDimensions(dims); err != nil {
		return
	}

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

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

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

	// Data for kth 's' constraint are found in rows inds[k]:inds[k+1] of G.
	inds := make([]int, 0)
	inds = append(inds, indq[len(indq)-1])
	for _, k := range dims.At("s") {
		inds = append(inds, inds[len(inds)-1]+k*k)
	}

	// Check b and set defaults if it is nil
	if b == nil {
		b = matrix.FloatZeros(0, 1)
	}
	if b.Cols() != 1 {
		estr := fmt.Sprintf("'b' must be a matrix with 1 column")
		err = errors.New(estr)
		return
	}
	if b.Rows() > c.Rows() || b.Rows()+cdim_pckd < c.Rows() {
		err = errors.New("Rank(A) < p or Rank([G; A]) < n")
		return
	}

	if kktsolver == nil {
		err = errors.New("nil kktsolver not allowed.")
		return
	}

	var mA MatrixVarA
	var mG MatrixVarG
	if G == nil {
		mG = &matrixVarG{matrix.FloatZeros(0, c.Rows()), dims}
	} else {
		mG = &matrixIfG{G}
	}
	if A == nil {
		mA = &matrixVarA{matrix.FloatZeros(0, c.Rows())}
	} else {
		mA = &matrixIfA{A}
	}
	var mc = &matrixVar{c}
	var mb = &matrixVar{b}

	return conelp_problem(mc, mG, h, mA, mb, dims, kktsolver, solopts, primalstart, dualstart)
}

/*
   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]].
//.........这里部分代码省略.........