Esempio n. 1
0
func updateScaling(W *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(stmp, math.Sqrt)
	s.SetIndexes(matrix.MakeIndexSet(0, m, 1), stmp.FloatArray())

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

	// 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 })
	}
	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 })

	// 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})

	//fmt.Printf("-- end of l:\nz=\n%v\nlmbda=\n%v\n", z.ConvertToString(), lmbda.ConvertToString())
	//fmt.Printf("W[d]=\n%v\n", dset[0].ConvertToString())
	//fmt.Printf("W[di]=\n%v\n", diset[0].ConvertToString())

	// '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

}
Esempio n. 2
0
/*
   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
		}

		err = blas.GerFloat(v, w, x, 2.0, &la_.IOpt{"m", m},
			&la_.IOpt{"n", x.Cols()}, &la_.IOpt{"lda", x.Rows()},
			&la_.IOpt{"offsetA", ind})
		if err != nil {
			return
		}

		var a float64
		if inverse {
			blas.ScalFloat(x, -1.0,
				&la_.IOpt{"offset", ind}, &la_.IOpt{"inc", x.Rows()})
			// a[i,j] := 1.0/W[i,j]
			a = 1.0 / W.At("beta")[0].GetIndex(k)
		} else {
			a = W.At("beta")[0].GetIndex(k)
		}
		for i := 0; i < x.Cols(); i++ {
			blas.ScalFloat(x, a, &la_.IOpt{"n", m}, &la_.IOpt{"offset", ind + i*x.Rows()})
		}
		ind += m
	}

	// Scaling for 's' component xk is
	//
	//     xk := vec( r' * mat(xk) * r )  if trans = 'N'
	//     xk := vec( r * mat(xk) * r' )  if trans = 'T'.
	//
	// r is kth element of W['r'].
	//
	// Inverse scaling is
	//
	//     xk := vec( rti * mat(xk) * rti' )  if trans = 'N'
	//     xk := vec( rti' * mat(xk) * rti )  if trans = 'T'.
	//
	// rti is kth element of W['rti'].
	maxn := 0
	for _, r := range W.At("r") {
		if r.Rows() > maxn {
			maxn = r.Rows()
		}
	}
	a := matrix.FloatZeros(maxn, maxn)
	for k, v := range W.At("r") {
		t := trans
		var r *matrix.FloatMatrix
		if !inverse {
			r = v
			t = !trans
		} else {
			r = W.At("rti")[k]
		}

		n := r.Rows()
		for i := 0; i < x.Cols(); i++ {
			// scale diagonal of xk by 0.5
			blas.ScalFloat(x, 0.5, &la_.IOpt{"offset", ind + i*x.Rows()},
				&la_.IOpt{"inc", n + 1}, &la_.IOpt{"n", n})

			// a = r*tril(x) (t is 'N') or a = tril(x)*r  (t is 'T')
			blas.Copy(r, a)
			if !t {
				err = blas.TrmmFloat(x, a, 1.0, la_.OptRight, &la_.IOpt{"m", n},
					&la_.IOpt{"n", n}, &la_.IOpt{"lda", n}, &la_.IOpt{"ldb", n},
					&la_.IOpt{"offsetA", ind + i*x.Rows()})
				if err != nil {
					return
				}

				// x := (r*a' + a*r')  if t is 'N'
				err = blas.Syr2kFloat(r, a, x, 1.0, 0.0, la_.OptNoTrans, &la_.IOpt{"n", n},
					&la_.IOpt{"k", n}, &la_.IOpt{"ldb", n}, &la_.IOpt{"ldc", n},
					&la_.IOpt{"offsetC", ind + i*x.Rows()})
				if err != nil {
					return
				}

			} else {
				err = blas.TrmmFloat(x, a, 1.0, la_.OptLeft, &la_.IOpt{"m", n},
					&la_.IOpt{"n", n}, &la_.IOpt{"lda", n}, &la_.IOpt{"ldb", n},
					&la_.IOpt{"offsetA", ind + i*x.Rows()})
				if err != nil {
					return
				}

				// x := (r'*a + a'*r)  if t is 'T'
				err = blas.Syr2kFloat(r, a, x, 1.0, 0.0, la_.OptTrans, &la_.IOpt{"n", n},
					&la_.IOpt{"k", n}, &la_.IOpt{"ldb", n}, &la_.IOpt{"ldc", n},
					&la_.IOpt{"offsetC", ind + i*x.Rows()})
				if err != nil {
					return
				}
			}
		}
		ind += n * n
	}
	return
}
Esempio n. 3
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 *DimensionSet, mnl int, inverse bool) (err error) {
	err = nil

	// 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})
	}

	// 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
	}
	// 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(c, scaleF, lmbda.GetIndex(ind2+j))
			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
	}
	return
}