# Nonlinear Convex Optimization

In this chapter we consider nonlinear convex optimization problems of the form

The functions are convex and twice differentiable and the linear inequalities are generalized inequalities with respect to a proper convex cone, defined as a product of a nonnegative orthant, second-order cones, and positive semidefinite cones.

The basic functions are `cp` and `cpl`, described in the sections Problems with Nonlinear Objectives and Problems with Linear Objectives. A simpler interface for geometric programming problems is discussed in the section Geometric Programming. In the section Exploiting Structure we explain how custom solvers can be implemented that exploit structure in specific classes of problems. The last section describes the algorithm parameters that control the solvers.

## Problems with Nonlinear Objectives

cvxopt.solvers.cp(F[, G, h[, dims[, A, b[, kktsolver]]]])

  Solves a convex optimization problem

  (1)

  The argument `F` is a function that evaluates the objective and nonlinear constraint functions. It must handle the following calling sequences.

  - `F()` returns a tuple (`m`, `x0`), where is the number of nonlinear constraints and is a point in the domain of . `x0` is a dense real matrix of size (, 1).

  - `F(x)`, with `x` a dense real matrix of size (, 1), returns a tuple (`f`, `Df`). `f` is a dense real matrix of size (, 1), with `f[k]` equal to . (If is zero, `f` can also be returned as a number.) `Df` is a dense or sparse real matrix of size ( + 1, ) with `Df[k,:]` equal to the transpose of the gradient . If is not in the domain of , `F(x)` returns `None` or a tuple (`None`, `None`).

  - `F(x,z)`, with `x` a dense real matrix of size (, 1) and `z` a positive dense real matrix of size ( + 1, 1) returns a tuple (`f`, `Df`, `H`). `f` and `Df` are defined as above. `H` is a square dense or sparse real matrix of size (, ), whose lower triangular part contains the lower triangular part of

    If `F` is called with two arguments, it can be assumed that is in the domain of .

  The linear inequalities are with respect to a cone defined as a Cartesian product of a nonnegative orthant, a number of second-order cones, and a number of positive semidefinite cones:

  with

  Here denotes a symmetric matrix stored as a vector in column major order.

  The arguments `h` and `b` are real single-column dense matrices. `G` and `A` are real dense or sparse matrices. The default values for `A` and `b` are sparse matrices with zero rows, meaning that there are no equality constraints. The number of rows of `G` and `h` is equal to

  The columns of `G` and `h` are vectors in

  where the last components represent symmetric matrices stored in column major order. The strictly upper triangular entries of these matrices are not accessed (i.e., the symmetric matrices are stored in the `'L'`-type column major order used in the `blas` and `lapack` modules).

  The argument `dims` is a dictionary with the dimensions of the cones. It has three fields.

  `dims['l']`:

    , the dimension of the nonnegative orthant (a nonnegative integer).

  `dims['q']`:

    , a list with the dimensions of the second-order cones (positive integers).

  `dims['s']`:

    , a list with the dimensions of the positive semidefinite cones (nonnegative integers).

  The default value of `dims` is `{'l': h.size[0], 'q': [], 's': []}`, i.e., the default assumption is that the linear inequalities are componentwise inequalities.

  The role of the optional argument `kktsolver` is explained in the section Exploiting Structure.

  `cp` returns a dictionary that contains the result and information about the accuracy of the solution. The most important fields have keys `'status'`, `'x'`, `'snl'`, `'sl'`, `'y'`, `'znl'`, `'zl'`. The possible values of the `'status'` key are:

  `'optimal'`

    In this case the `'x'` entry of the dictionary is the primal optimal solution, the `'snl'` and `'sl'` entries are the corresponding slacks in the nonlinear and linear inequality constraints, and the `'znl'`, `'zl'` and `'y'` entries are the optimal values of the dual variables associated with the nonlinear inequalities, the linear inequalities, and the linear equality constraints. These vectors approximately satisfy the Karush-Kuhn-Tucker (KKT) conditions

    where .

  `'unknown'`

    This indicates that the algorithm terminated before a solution was found, due to numerical difficulties or because the maximum number of iterations was reached. The `'x'`, `'snl'`, `'sl'`, `'y'`, `'znl'`, and `'zl'` entries contain the iterates when the algorithm terminated.

  `cp` solves the problem by applying `cpl` to the epigraph form problem

  The other entries in the output dictionary of `cp` describe the accuracy of the solution and are copied from the output of `cpl` applied to this epigraph form problem.

  `cp` requires that the problem is strictly primal and dual feasible and that

  for all and all positive .

Example: equality constrained analytic centering

  The equality constrained analytic centering problem is defined as

  The function `acent` defined below solves the problem, assuming it is solvable.

      from cvxopt import solvers, matrix, spdiag, log

      def acent(A, b):
          m, n = A.size
          def F(x=None, z=None):
              if x is None: return 0, matrix(1.0, (n,1))
              if min(x) <= 0.0: return None
              f = -sum(log(x))
              Df = -(x**-1).T
              if z is None: return f, Df
              H = spdiag(z[0] * x**-2)
              return f, Df, H
          return solvers.cp(F, A=A, b=b)['x']

Example: robust least-squares

  The function `robls` defined below solves the unconstrained problem

  where .

      from cvxopt import solvers, matrix, spdiag, sqrt, div

      def robls(A, b, rho):
          m, n = A.size
          def F(x=None, z=None):
              if x is None: return 0, matrix(0.0, (n,1))
              y = A*x-b
              w = sqrt(rho + y**2)
              f = sum(w)
              Df = div(y, w).T * A
              if z is None: return f, Df
              H = A.T * spdiag(z[0]*rho*(w**-3)) * A
              return f, Df, H
          return solvers.cp(F)['x']

Example: analytic centering with cone constraints

>     from cvxopt import matrix, log, div, spdiag, solvers

>     def F(x = None, z = None):
>          if x is None:  return 0, matrix(0.0, (3,1))
>          if max(abs(x)) >= 1.0:  return None
>          u = 1 - x**2
>          val = -sum(log(u))
>          Df = div(2*x, u).T
>          if z is None:  return val, Df
>          H = spdiag(2 * z[0] * div(1 + x**2, u**2))
>          return val, Df, H

>     G = matrix([ [0., -1.,  0.,  0., -21., -11.,   0., -11.,  10.,   8.,   0.,   8., 5.],
>                  [0.,  0., -1.,  0.,   0.,  10.,  16.,  10., -10., -10.,  16., -10., 3.],
>                  [0.,  0.,  0., -1.,  -5.,   2., -17.,   2.,  -6.,   8., -17.,  -7., 6.] ])
>     h = matrix([1.0, 0.0, 0.0, 0.0, 20., 10., 40., 10., 80., 10., 40., 10., 15.])
>     dims = {'l': 0, 'q': [4], 's':  [3]}
>     sol = solvers.cp(F, G, h, dims)
>     print(sol['x'])
>     [ 4.11e-01]
>     [ 5.59e-01]
>     [-7.20e-01]

## Problems with Linear Objectives

cvxopt.solvers.cpl(c, F[, G, h[, dims[, A, b[, kktsolver]]]])

  Solves a convex optimization problem with a linear objective

  `c` is a real single-column dense matrix.

  `F` is a function that evaluates the nonlinear constraint functions. It must handle the following calling sequences.

  - `F()` returns a tuple (`m`, `x0`), where `m` is the number of nonlinear constraints and `x0` is a point in the domain of . `x0` is a dense real matrix of size (, 1).

  - `F(x)`, with `x` a dense real matrix of size (, 1), returns a tuple (`f`, `Df`). `f` is a dense real matrix of size (, 1), with `f[k]` equal to . `Df` is a dense or sparse real matrix of size (, ) with `Df[k,:]` equal to the transpose of the gradient . If is not in the domain of , `F(x)` returns `None` or a tuple (`None`, `None`).

  - `F(x,z)`, with `x` a dense real matrix of size (, 1) and `z` a positive dense real matrix of size (, 1) returns a tuple (`f`, `Df`, `H`). `f` and `Df` are defined as above. `H` is a square dense or sparse real matrix of size (, ), whose lower triangular part contains the lower triangular part of

    If `F` is called with two arguments, it can be assumed that is in the domain of .

  The linear inequalities are with respect to a cone defined as a Cartesian product of a nonnegative orthant, a number of second-order cones, and a number of positive semidefinite cones:

  with

  Here denotes a symmetric matrix stored as a vector in column major order.

  The arguments `h` and `b` are real single-column dense matrices. `G` and `A` are real dense or sparse matrices. The default values for `A` and `b` are sparse matrices with zero rows, meaning that there are no equality constraints. The number of rows of `G` and `h` is equal to

  The columns of `G` and `h` are vectors in

  where the last components represent symmetric matrices stored in column major order. The strictly upper triangular entries of these matrices are not accessed (i.e., the symmetric matrices are stored in the `'L'`-type column major order used in the `blas` and `lapack` modules.

  The argument `dims` is a dictionary with the dimensions of the cones. It has three fields.

  `dims['l']`:

    , the dimension of the nonnegative orthant (a nonnegative integer).

  `dims['q']`:

    , a list with the dimensions of the second-order cones (positive integers).

  `dims['s']`:

    , a list with the dimensions of the positive semidefinite cones (nonnegative integers).

  The default value of `dims` is `{'l': h.size[0], 'q': [], 's': []}`, i.e., the default assumption is that the linear inequalities are componentwise inequalities.

  The role of the optional argument `kktsolver` is explained in the section Exploiting Structure.

  `cpl` returns a dictionary that contains the result and information about the accuracy of the solution. The most important fields have keys `'status'`, `'x'`, `'snl'`, `'sl'`, `'y'`, `'znl'`, `'zl'`. The possible values of the `'status'` key are:

  `'optimal'`

    In this case the `'x'` entry of the dictionary is the primal optimal solution, the `'snl'` and `'sl'` entries are the corresponding slacks in the nonlinear and linear inequality constraints, and the `'znl'`, `'zl'`, and `'y'` entries are the optimal values of the dual variables associated with the nonlinear inequalities, the linear inequalities, and the linear equality constraints. These vectors approximately satisfy the Karush-Kuhn-Tucker (KKT) conditions

  `'unknown'`

    This indicates that the algorithm terminated before a solution was found, due to numerical difficulties or because the maximum number of iterations was reached. The `'x'`, `'snl'`, `'sl'`, `'y'`, `'znl'`, and `'zl'` entries contain the iterates when the algorithm terminated.

  The other entries in the output dictionary describe the accuracy of the solution. The entries `'primal objective'`, `'dual objective'`, `'gap'`, and `'relative gap'` give the primal objective , the dual objective, calculated as

  the duality gap

  and the relative gap. The relative gap is defined as

  and `None` otherwise. The entry with key `'primal infeasibility'` gives the residual in the primal constraints,

  where is the point returned by `F()`. The entry with key `'dual infeasibility'` gives the residual

  `cpl` requires that the problem is strictly primal and dual feasible and that

  for all and all positive .

Example: floor planning

  This example is the floor planning problem of section 8.8.2 in the book Convex Optimization:

  This problem has 22 variables

  5 nonlinear inequality constraints, and 26 linear inequality constraints. The code belows defines a function `floorplan` that solves the problem by calling `cp`, then applies it to 4 instances, and creates a figure.

      import pylab
      from cvxopt import solvers, matrix, spmatrix, mul, div

      def floorplan(Amin):

          #     minimize    W+H
          #     subject to  Amink / hk <= wk, k = 1,..., 5
          #                 x1 >= 0,  x2 >= 0, x4 >= 0
          #                 x1 + w1 + rho <= x3
          #                 x2 + w2 + rho <= x3
          #                 x3 + w3 + rho <= x5
          #                 x4 + w4 + rho <= x5
          #                 x5 + w5 <= W
          #                 y2 >= 0,  y3 >= 0,  y5 >= 0
          #                 y2 + h2 + rho <= y1
          #                 y1 + h1 + rho <= y4
          #                 y3 + h3 + rho <= y4
          #                 y4 + h4 <= H
          #                 y5 + h5 <= H
          #                 hk/gamma <= wk <= gamma*hk,  k = 1, ..., 5
          #
          # 22 Variables W, H, x (5), y (5), w (5), h (5).
          #
          # W, H:  scalars; bounding box width and height
          # x, y:  5-vectors; coordinates of bottom left corners of blocks
          # w, h:  5-vectors; widths and heights of the 5 blocks

          rho, gamma = 1.0, 5.0   # min spacing, min aspect ratio

          # The objective is to minimize W + H.  There are five nonlinear
          # constraints
          #
          #     -wk + Amink / hk <= 0,  k = 1, ..., 5

          c = matrix(2*[1.0] + 20*[0.0])

          def F(x=None, z=None):
              if x is None:  return 5, matrix(17*[0.0] + 5*[1.0])
              if min(x[17:]) <= 0.0:  return None
              f = -x[12:17] + div(Amin, x[17:])
              Df = matrix(0.0, (5,22))
              Df[:,12:17] = spmatrix(-1.0, range(5), range(5))
              Df[:,17:] = spmatrix(-div(Amin, x[17:]**2), range(5), range(5))
              if z is None: return f, Df
              H = spmatrix( 2.0* mul(z, div(Amin, x[17::]**3)), range(17,22), range(17,22) )
              return f, Df, H

          G = matrix(0.0, (26,22))
          h = matrix(0.0, (26,1))
          G[0,2] = -1.0                                       # -x1 <= 0
          G[1,3] = -1.0                                       # -x2 <= 0
          G[2,5] = -1.0                                       # -x4 <= 0
          G[3, [2, 4, 12]], h[3] = [1.0, -1.0, 1.0], -rho     # x1 - x3 + w1 <= -rho
          G[4, [3, 4, 13]], h[4] = [1.0, -1.0, 1.0], -rho     # x2 - x3 + w2 <= -rho
          G[5, [4, 6, 14]], h[5] = [1.0, -1.0, 1.0], -rho     # x3 - x5 + w3 <= -rho
          G[6, [5, 6, 15]], h[6] = [1.0, -1.0, 1.0], -rho     # x4 - x5 + w4 <= -rho
          G[7, [0, 6, 16]] = -1.0, 1.0, 1.0                   # -W + x5 + w5 <= 0
          G[8,8] = -1.0                                       # -y2 <= 0
          G[9,9] = -1.0                                       # -y3 <= 0
          G[10,11] = -1.0                                     # -y5 <= 0
          G[11, [7, 8, 18]], h[11] = [-1.0, 1.0, 1.0], -rho   # -y1 + y2 + h2 <= -rho
          G[12, [7, 10, 17]], h[12] = [1.0, -1.0, 1.0], -rho  #  y1 - y4 + h1 <= -rho
          G[13, [9, 10, 19]], h[13] = [1.0, -1.0, 1.0], -rho  #  y3 - y4 + h3 <= -rho
          G[14, [1, 10, 20]] = -1.0, 1.0, 1.0                 # -H + y4 + h4 <= 0
          G[15, [1, 11, 21]] = -1.0, 1.0, 1.0                 # -H + y5 + h5 <= 0
          G[16, [12, 17]] = -1.0, 1.0/gamma                   # -w1 + h1/gamma <= 0
          G[17, [12, 17]] = 1.0, -gamma                       #  w1 - gamma * h1 <= 0
          G[18, [13, 18]] = -1.0, 1.0/gamma                   # -w2 + h2/gamma <= 0
          G[19, [13, 18]] = 1.0, -gamma                       #  w2 - gamma * h2 <= 0
          G[20, [14, 19]] = -1.0, 1.0/gamma                   # -w3 + h3/gamma <= 0
          G[21, [14, 19]] = 1.0, -gamma                       #  w3 - gamma * h3 <= 0
          G[22, [15, 20]] = -1.0, 1.0/gamma                   # -w4  + h4/gamma <= 0
          G[23, [15, 20]] = 1.0, -gamma                       #  w4 - gamma * h4 <= 0
          G[24, [16, 21]] = -1.0, 1.0/gamma                   # -w5 + h5/gamma <= 0
          G[25, [16, 21]] = 1.0, -gamma                       #  w5 - gamma * h5 <= 0.0

          # solve and return W, H, x, y, w, h
          sol = solvers.cpl(c, F, G, h)
          return  sol['x'][0], sol['x'][1], sol['x'][2:7], sol['x'][7:12], sol['x'][12:17], sol['x'][17:]

      pylab.figure(facecolor='w')
      pylab.subplot(221)
      Amin = matrix([100., 100., 100., 100., 100.])
      W, H, x, y, w, h =  floorplan(Amin)
      for k in range(5):
          pylab.fill([x[k], x[k], x[k]+w[k], x[k]+w[k]],
                     [y[k], y[k]+h[k], y[k]+h[k], y[k]], facecolor = '#D0D0D0')
          pylab.text(x[k]+.5*w[k], y[k]+.5*h[k], "%d" %(k+1))
      pylab.axis([-1.0, 26, -1.0, 26])
      pylab.xticks([])
      pylab.yticks([])

      pylab.subplot(222)
      Amin = matrix([20., 50., 80., 150., 200.])
      W, H, x, y, w, h =  floorplan(Amin)
      for k in range(5):
          pylab.fill([x[k], x[k], x[k]+w[k], x[k]+w[k]],
                     [y[k], y[k]+h[k], y[k]+h[k], y[k]], 'facecolor = #D0D0D0')
          pylab.text(x[k]+.5*w[k], y[k]+.5*h[k], "%d" %(k+1))
      pylab.axis([-1.0, 26, -1.0, 26])
      pylab.xticks([])
      pylab.yticks([])

      pylab.subplot(223)
      Amin = matrix([180., 80., 80., 80., 80.])
      W, H, x, y, w, h =  floorplan(Amin)
      for k in range(5):
          pylab.fill([x[k], x[k], x[k]+w[k], x[k]+w[k]],
                     [y[k], y[k]+h[k], y[k]+h[k], y[k]], 'facecolor = #D0D0D0')
          pylab.text(x[k]+.5*w[k], y[k]+.5*h[k], "%d" %(k+1))
      pylab.axis([-1.0, 26, -1.0, 26])
      pylab.xticks([])
      pylab.yticks([])

      pylab.subplot(224)
      Amin = matrix([20., 150., 20., 200., 110.])
      W, H, x, y, w, h =  floorplan(Amin)
      for k in range(5):
          pylab.fill([x[k], x[k], x[k]+w[k], x[k]+w[k]],
                     [y[k], y[k]+h[k], y[k]+h[k], y[k]], 'facecolor = #D0D0D0')
          pylab.text(x[k]+.5*w[k], y[k]+.5*h[k], "%d" %(k+1))
      pylab.axis([-1.0, 26, -1.0, 26])
      pylab.xticks([])
      pylab.yticks([])

      pylab.show()

## Geometric Programming

cvxopt.solvers.gp(K, F, g[, G, h[, A, b]])

  Solves a geometric program in convex form

  where

  and the vector inequality denotes componentwise inequality. `K` is a list of + 1 positive integers with `K[i]` equal to the number of rows in . `F` is a dense or sparse real matrix of size `(sum(K), n)`. `g` is a dense real matrix with one column and the same number of rows as `F`. `G` and `A` are dense or sparse real matrices. Their default values are sparse matrices with zero rows. `h` and `b` are dense real matrices with one column. Their default values are matrices of size (0, 1).

  `gp` returns a dictionary with keys `'status'`, `'x'`, `'snl'`, `'sl'`, `'y'`, `'znl'`, and `'zl'`. The possible values of the `'status'` key are:

  `'optimal'`

    In this case the `'x'` entry is the primal optimal solution, the `'snl'` and `'sl'` entries are the corresponding slacks in the nonlinear and linear inequality constraints. The `'znl'`, `'zl'`, and `'y'` entries are the optimal values of the dual variables associated with the nonlinear and linear inequality constraints and the linear equality constraints. These values approximately satisfy

  `'unknown'`

    This indicates that the algorithm terminated before a solution was found, due to numerical difficulties or because the maximum number of iterations was reached. The `'x'`, `'snl'`, `'sl'`, `'y'`, `'znl'`, and `'zl'` contain the iterates when the algorithm terminated.

  The other entries in the output dictionary describe the accuracy of the solution, and are taken from the output of `cp`.

  `gp` requires that the problem is strictly primal and dual feasible and that

  for all and all positive .

As an example, we solve the small GP of section 2.4 of the paper A Tutorial on Geometric Programming. The posynomial form of the problem is

with variables , , .

    from cvxopt import matrix, log, exp, solvers

    Aflr  = 1000.0
    Awall = 100.0
    alpha = 0.5
    beta  = 2.0
    gamma = 0.5
    delta = 2.0

    F = matrix( [[-1., 1., 1., 0., -1.,  1.,  0.,  0.],
                 [-1., 1., 0., 1.,  1., -1.,  1., -1.],
                 [-1., 0., 1., 1.,  0.,  0., -1.,  1.]])
    g = log( matrix( [1.0, 2/Awall, 2/Awall, 1/Aflr, alpha, 1/beta, gamma, 1/delta]) )
    K = [1, 2, 1, 1, 1, 1, 1]
    h, w, d = exp( solvers.gp(K, F, g)['x'] )

## Exploiting Structure

By default, the functions `cp` and `cpl` do not exploit problem structure. Two mechanisms are provided for implementing customized solvers that take advantage of problem structure.

Providing a function for solving KKT equations

  The most expensive step of each iteration of `cp` is the solution of a set of linear equations (KKT equations) of the form

  (2)

  where

  The matrix depends on the current iterates and is defined as follows. Suppose

  where

  Then is a block-diagonal matrix,

  with the following diagonal blocks.

  - The first block is a positive diagonal scaling with a vector :

    This transformation is symmetric:

  - The second block is a positive diagonal scaling with a vector :

    This transformation is symmetric:

  - The next blocks are positive multiples of hyperbolic Householder transformations:

    where

    These transformations are also symmetric:

  - The last blocks are congruence transformations with nonsingular matrices:

    In general, this operation is not symmetric, and

  It is often possible to exploit problem structure to solve (2) faster than by standard methods. The last argument `kktsolver` of `cp` allows the user to supply a Python function for solving the KKT equations. This function will be called as `f = kktsolver(x, z, W)`. The argument `x` is the point at which the derivatives in the KKT matrix are evaluated. `z` is a positive vector of length it + 1, containing the coefficients in the 1,1 block . `W` is a dictionary that contains the parameters of the scaling:

  - `W['dnl']` is the positive vector that defines the diagonal scaling for the nonlinear inequalities. `W['dnli']` is its componentwise inverse.

  - `W['d']` is the positive vector that defines the diagonal scaling for the componentwise linear inequalities. `W['di']` is its componentwise inverse.

  - `W['beta']` and `W['v']` are lists of length with the coefficients and vectors that define the hyperbolic Householder transformations.

  - `W['r']` is a list of length with the matrices that define the the congruence transformations. `W['rti']` is a list of length with the transposes of the inverses of the matrices in `W['r']`.

  The function call `f = kktsolver(x, z, W)` should return a routine for solving the KKT system (2) defined by `x`, `z`, `W`. It will be called as `f(bx, by, bz)`. On entry, `bx`, `by`, `bz` contain the right-hand side. On exit, they should contain the solution of the KKT system, with the last component scaled, i.e., on exit,

  The role of the argument `kktsolver` in the function `cpl` is similar, except that in (2),

Specifying constraints via Python functions

  In the default use of `cp`, the arguments `G` and `A` are the coefficient matrices in the constraints of (2). It is also possible to specify these matrices by providing Python functions that evaluate the corresponding matrix-vector products and their adjoints.

  - If the argument `G` of `cp` is a Python function, then `G(u, v[, alpha = 1.0, beta = 0.0, trans = 'N'])` should evaluates the matrix-vector products

  - Similarly, if the argument `A` is a Python function, then `A(u, v[, alpha = 1.0, beta = 0.0, trans = 'N'])` should evaluate the matrix-vector products

  - In a similar way, when the first argument `F` of `cp` returns matrices of first derivatives or second derivatives `Df`, `H`, these matrices can be specified as Python functions. If `Df` is a Python function, then `Df(u, v[, alpha = 1.0, beta = 0.0, trans = 'N'])` should evaluate the matrix-vector products

    If `H` is a Python function, then `H(u, v[, alpha, beta])` should evaluate the matrix-vector product

  If `G`, `A`, `Df`, or `H` are Python functions, then the argument `kktsolver` must also be provided.

As an example, we consider the unconstrained problem

where is an by matrix with less than . The Hessian of the objective is diagonal plus a low-rank term:

We can exploit this property when solving (2) by applying the matrix inversion lemma. We first solve

and then obtain

The following code follows this method. It also uses BLAS functions for matrix-matrix and matrix-vector products.

    from cvxopt import matrix, spdiag, mul, div, log, blas, lapack, solvers, base

    def l2ac(A, b):
        """
        Solves

            minimize  (1/2) * ||A*x-b||_2^2 - sum log (1-xi^2)

        assuming A is m x n with m << n.
        """

        m, n = A.size
        def F(x = None, z = None):
            if x is None:
                return 0, matrix(0.0, (n,1))
            if max(abs(x)) >= 1.0:
                return None
            # r = A*x - b
            r = -b
            blas.gemv(A, x, r, beta = -1.0)
            w = x**2
            f = 0.5 * blas.nrm2(r)**2  - sum(log(1-w))
            # gradf = A'*r + 2.0 * x ./ (1-w)
            gradf = div(x, 1.0 - w)
            blas.gemv(A, r, gradf, trans = 'T', beta = 2.0)
            if z is None:
                return f, gradf.T
            else:
                def Hf(u, v, alpha = 1.0, beta = 0.0):
                   # v := alpha * (A'*A*u + 2*((1+w)./(1-w)).*u + beta *v
                   v *= beta
                   v += 2.0 * alpha * mul(div(1.0+w, (1.0-w)**2), u)
                   blas.gemv(A, u, r)
                   blas.gemv(A, r, v, alpha = alpha, beta = 1.0, trans = 'T')
                return f, gradf.T, Hf

        # Custom solver for the Newton system
        #
        #     z[0]*(A'*A + D)*x = bx
        #
        # where D = 2 * (1+x.^2) ./ (1-x.^2).^2.  We apply the matrix inversion
        # lemma and solve this as
        #
        #     (A * D^-1 *A' + I) * v = A * D^-1 * bx / z[0]
        #     D * x = bx / z[0] - A'*v.

        S = matrix(0.0, (m,m))
        v = matrix(0.0, (m,1))
        def Fkkt(x, z, W):
            ds = (2.0 * div(1 + x**2, (1 - x**2)**2))**-0.5
            Asc = A * spdiag(ds)
            blas.syrk(Asc, S)
            S[::m+1] += 1.0
            lapack.potrf(S)
            a = z[0]
            def g(x, y, z):
                x[:] = mul(x, ds) / a
                blas.gemv(Asc, x, v)
                lapack.potrs(S, v)
                blas.gemv(Asc, v, x, alpha = -1.0, beta = 1.0, trans = 'T')
                x[:] = mul(x, ds)
            return g

        return solvers.cp(F, kktsolver = Fkkt)['x']

## Algorithm Parameters

The following algorithm control parameters are accessible via the dictionary `solvers.options`. By default the dictionary is empty and the default values of the parameters are used.

One can change the parameters in the default solvers by adding entries with the following key values.

`'show_progress'`

  `True` or `False`; turns the output to the screen on or off (default: `True`).

`'maxiters'`

  maximum number of iterations (default: `100`).

`'abstol'`

  absolute accuracy (default: `1e-7`).

`'reltol'`

  relative accuracy (default: `1e-6`).

`'feastol'`

  tolerance for feasibility conditions (default: `1e-7`).

`'refinement'`

  number of iterative refinement steps when solving KKT equations (default: `1`).

For example the command

    >>> from cvxopt import solvers
    >>> solvers.options['show_progress'] = False

turns off the screen output during calls to the solvers. The tolerances `abstol`, `reltol` and `feastol` have the following meaning in `cpl`.

`cpl` returns with status `'optimal'` if

where is the point returned by `F()`, and

where

The functions `cp` and `gp` call `cpl` and hence use the same stopping criteria (with for `gp`).