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# Modeling

The module `cvxopt.modeling` can be used to specify and solve optimization problems with convex piecewise-linear objective and constraint functions. Using this modeling tool, one can specify an optimization problem by first defining the optimization variables (see the section Variables), and then specifying the objective and constraint functions using linear operations (vector addition and subtraction, matrix-vector multiplication, indexing and slicing) and nested evaluations of `max`, `min`, `abs` and `sum` (see the section Functions).

A more general Python convex modeling package is CVXMOD.

## Variables

Optimization variables are represented by `variable` objects.

`cvxopt.modeling.``variable`([size[, name]])

  A vector variable. The first argument is the dimension of the vector (a positive integer with default value 1). The second argument is a string with a name for the variable. The name is optional and has default value `""`. It is only used when displaying variables (or objects that depend on variables, such as functions or constraints) using `print` statements, when calling the built-in functions `repr` or `str`, or when writing linear programs to MPS files.

The function `len` returns the length of a `variable`. A `variable` `x` has two attributes.

`name`

  The name of the variable.

`value`

  Either `None` or a dense `'d'` matrix of size `len(x)` by 1.

  The attribute `x.value` is set to `None` when the variable `x` is created. It can be given a numerical value later, typically by solving an LP that has `x` as one of its variables. One can also make an explicit assignment `x.value = y`. The assigned value `y` must be an integer or float, or a dense `'d'` matrix of size `(len(x), 1)`. If `y` is an integer or float, all the elements of `x.value` are set to the value of `y`.

    >>> from cvxopt import matrix
    >>> from cvxopt.modeling import variable
    >>> x = variable(3,'a')
    >>> len(x)
    3
    >>> print(x.name)
    a
    >>> print(x.value)
    None
    >>> x.value = matrix([1.,2.,3.])
    >>> print(x.value)
    [ 1.00e+00]
    [ 2.00e+00]
    [ 3.00e+00]
    >>> x.value = 1
    >>> print(x.value)
    [ 1.00e+00]
    [ 1.00e+00]
    [ 1.00e+00]

## Functions

Objective and constraint functions can be defined via overloaded operations on variables and other functions. A function `f` is interpreted as a column vector, with length `len(f)` and with a value that depends on the values of its variables. Functions have two public attributes.

`variables`

  Returns a copy of the list of variables of the function.

`value`

  The function value. If any of the variables of `f` has value `None`, then `f.value()` returns `None`. Otherwise, it returns a dense `'d'` matrix of size `(len(f),1)` with the function value computed from the `value` attributes of the variables of `f`.

Three types of functions are supported: affine, convex piecewise-linear, and concave piecewise-linear.

Affine functions represent vector valued functions of the form

The coefficients can be scalars or dense or sparse matrices. The constant term is a scalar or a column vector.

Affine functions result from the following operations.

Unary operations
  For a variable `x`, the unary operation `+x` results in an affine function with `x` as variable, coefficient 1.0, and constant term 0.0. The unary operation `-x` returns an affine function with `x` as variable, coefficient -1.0, and constant term 0.0. For an affine function `f`, `+f` is a copy of `f`, and `-f` is a copy of `f` with the signs of its coefficients and constant term reversed.
Addition and subtraction

  Sums and differences of affine functions, variables and constants result in new affine functions. The constant terms in the sum can be of type integer or float, or dense or sparse `'d'` matrices with one column.

  The rules for addition and subtraction follow the conventions for matrix addition and subtraction in the section Arithmetic Operations, with variables and affine functions interpreted as dense `'d'` matrices with one column. In particular, a scalar term (integer, float, 1 by 1 dense `'d'` matrix, variable of length 1, or affine function of length 1) can be added to an affine function or variable of length greater than 1.

Multiplication
  Suppose `v` is an affine function or a variable, and `a` is an integer, float, sparse or dense `'d'` matrix. The products `a * v` and `v * a` are valid affine functions whenever the product is allowed under the rules for matrix and scalar multiplication of the section Arithmetic Operations, with `v` interpreted as a `'d'` matrix with one column. In particular, the product `a * v` is defined if `a` is a scalar (integer, float, or 1 by 1 dense `'d'` matrix), or a matrix (dense or sparse) with `a.size[1]` equal to `len(v)`. The operation `v * a` is defined if `a` is scalar, or if `len(v)` is 1 and `a` is a matrix with one column.
Inner products

  The following two functions return scalar affine functions defined as inner products of a constant vector with a variable or affine function.

  `cvxopt.modeling.``sum`(v)

    The argument is an affine function or a variable. The result is an affine function of length 1, with the sum of the components of the argument `v`.

  `cvxopt.modeling.``dot`(u, v)

    If `v` is a variable or affine function and `u` is a `'d'` matrix of size `(len(v), 1)`, then `dot(u, v)` and `dot(v, u)` are equivalent to `u.trans() * v`.

    If `u` and `v` are dense matrices, then `dot` is equivalent to the function `blas.dot`, i.e., it returns the inner product of the two matrices.

In the following example, the variable `x` has length 1 and `y` has length 2. The functions `f` and `g` are given by

    >>> from cvxopt.modeling import variable
    >>> x = variable(1,'x')
    >>> y = variable(2,'y')
    >>> f = 2*x + y + 3
    >>> A = matrix([[1., 2.], [3.,4.]])
    >>> b = matrix([1.,-1.])
    >>> g = A*f + sum(y) + b
    >>> print(g)
    affine function of length 2
    constant term:
    [ 1.30e+01]
    [ 1.70e+01]
    linear term: linear function of length 2
    coefficient of variable(2,'y'):
    [ 2.00e+00  4.00e+00]
    [ 3.00e+00  5.00e+00]
    coefficient of variable(1,'x'):
    [ 8.00e+00]
    [ 1.20e+01]

In-place operations
  For an affine function `f` the operations `f += u` and `f -= u`, with `u` a constant, a variable or an affine function, are allowed if they do not change the length of `f`, i.e., if `u` has length `len(f)` or length 1. In-place multiplication `f *= u` and division `f /= u` are allowed if `u` is an integer, float, or 1 by 1 matrix.
Indexing and slicing
  Variables and affine functions admit single-argument indexing of the four types described in the section Indexing and Slicing. The result of an indexing or slicing operation is an affine function.

    >>> x = variable(4,'x')
    >>> f = x[::2]
    >>> print(f)
    linear function of length 2
    linear term: linear function of length 2
    coefficient of variable(4,'x'):
    [ 1.00e+00     0         0         0    ]
    [    0         0      1.00e+00     0    ]
    >>> y = variable(3,'x')
    >>> g = matrix(range(12),(3,4),'d')*x - 3*y + 1
    >>> print(g[0] + g[2])
    affine function of length 1
    constant term:
    [ 2.00e+00]
    linear term: linear function of length 1
    coefficient of variable(4,'x'):
    [ 2.00e+00  8.00e+00  1.40e+01  2.00e+01]
    coefficient of variable(3,'x'):
    [-3.00e+00     0     -3.00e+00]

The general expression of a convex piecewise-linear function is

The maximum in this expression is a componentwise maximum of its vector arguments, which can be constant vectors, variables, affine functions or convex piecewise-linear functions. The general expression for a concave piecewise-linear function is

Here the arguments of the `min` can be constants, variables, affine functions or concave piecewise-linear functions.

Piecewise-linear functions can be created using the following operations.

Maximum

  If the arguments in `f = max(y1, y2, ...)` do not include any variables or functions, then the Python built-in `max` is evaluated.

  If one or more of the arguments are variables or functions, `max` returns a piecewise-linear function defined as the elementwise maximum of its arguments. In other words, `f[k] = max(y1[k], y2[k], ...)` for `k` = 0, ..., `len(f) - 1`. The length of `f` is equal to the maximum of the lengths of the arguments. Each argument must have length equal to `len(f)` or length one. Arguments with length one are interpreted as vectors of length `len(f)` with identical entries.

  The arguments can be scalars of type integer or float, dense `'d'` matrices with one column, variables, affine functions or convex piecewise-linear functions.

  With one argument, `f = max(u)` is interpreted as `f = max(u[0], u[1], ..., u[len(u)-1])`.

Minimum
  Similar to `max` but returns a concave piecewise-linear function. The arguments can be scalars of type integer or float, dense `'d'` matrices with one column, variables, affine functions or concave piecewise-linear functions.
Absolute value
  If `u` is a variable or affine function then `f = abs(u)` returns the convex piecewise-linear function `max(u, -u)`.
Unary plus and minus
  `+f` creates a copy of `f`. `-f` is a concave piecewise-linear function if `f` is convex and a convex piecewise-linear function if `f` is concave.
Addition and subtraction
  Sums and differences involving piecewise-linear functions are allowed if they result in convex or concave functions. For example, one can add two convex or two concave functions, but not a convex and a concave function. The command `sum(f)` is equivalent to `f[0] + f[1] + ... + f[len(f) - 1]`.
Multiplication
  Scalar multiplication `a * f` of a piecewise-linear function `f` is defined if `a` is an integer, float, 1 by 1 `'d'` matrix. Matrix-matrix multiplications `a * f` or `f * a` are only defined if `a` is a dense or sparse 1 by 1 matrix.
Indexing and slicing
  Piecewise-linear functions admit single-argument indexing of the four types described in the section Indexing and Slicing. The result of an indexing or slicing operation is a new piecewise-linear function.

In the following example, `f` is the 1-norm of a vector variable `x` of length 10, `g` is its infinity-norm, and `h` is the function

    >>> from cvxopt.modeling import variable, max
    >>> x = variable(10, 'x')
    >>> f = sum(abs(x))
    >>> g = max(abs(x))
    >>> h = sum(max(0, abs(x)-1, 2*abs(x)-3))

In-place operations
  If `f` is piecewise-linear then the in-place operations `f += u`, `f -= u`, `f *= u`, `f /= u` are defined if the corresponding expanded operations `f = f + u`, `f = f - u`, `f = f * u`, and `f = f/u` are defined and if they do not change the length of `f`.

## Constraints

Linear equality and inequality constraints of the form

where is a convex function, are represented by `constraint` objects. Equality constraints are created by expressions of the form

    f1 == f2

Here `f1` and `f2` can be any objects for which the difference `f1 - f2` yields an affine function. Inequality constraints are created by expressions of the form

    f1 <= f2
    f2 >= f1

where `f1` and `f2` can be any objects for which the difference `f1 - f2` yields a convex piecewise-linear function. The comparison operators first convert the expressions to `f1 - f2 == 0`, resp., `f1 - f2 <= 0`, and then return a new `constraint` object with constraint function `f1 - f2`.

In the following example we create three constraints

for a variable of length 5.

    >>> x = variable(5,'x')
    >>> c1 = (x <= 1)
    >>> c2 = (x >= 0)
    >>> c3 = (sum(x) == 2)

The built-in function `len` returns the dimension of the constraint function.

Constraints have four public attributes.

`type`

  Returns `'='` if the constraint is an equality constraint, and ‘<’ if the constraint is an inequality constraint.

`value`

  Returns the value of the constraint function.

`multiplier`

  For a constraint `c`, `c.multiplier` is a `variable` object of dimension `len(c)`. It is used to represent the Lagrange multiplier or dual variable associated with the constraint. Its value is initialized as `None`, and can be modified by making an assignment to `c.multiplier.value`.

`name`

  The name of the constraint. Changing the name of a constraint also changes the name of the multiplier of `c`. For example, the command `c.name = 'newname'` also changes `c.multiplier.name` to `'newname_mul'`.

## Optimization Problems

Optimization problems are be constructed by calling the following function.

`cvxopt.modeling.``op`([objective[, constraints[, name]]])

  The first argument specifies the objective function to be minimized. It can be an affine or convex piecewise-linear function with length 1, a `variable` with length 1, or a scalar constant (integer, float, or 1 by 1 dense `'d'` matrix). The default value is 0.0.

  The second argument is a single `constraint`, or a list of `constraint` objects. The default value is an empty list.

  The third argument is a string with a name for the problem. The default value is the empty string.

The following attributes and methods are useful for examining and modifying optimization problems.

`objective`

  The objective or cost function. One can write to this attribute to change the objective of an existing problem.

`variables`()

  Returns a list of the variables of the problem.

`constraints`()

  Returns a list of the constraints.

`inequalities`()

  Returns a list of the inequality constraints.

`equalities`()

  Returns a list of the equality constraints.

`delconstraint`(c)

  Deletes constraint `c` from the problem.

An optimization problem with convex piecewise-linear objective and constraints can be solved by calling the method `solve`.

`solve`([format[, solver]])

  This function converts the optimization problem to a linear program in matrix form and then solves it using the solver described in the section Linear Programming.

  The first argument is either `'dense'` or `'sparse'`, and denotes the matrix types used in the matrix representation of the LP. The default value is `'dense'`.

  The second argument is either `None`, `'glpk'`, or `'mosek'`, and selects one of three available LP solvers: the default solver written in Python, the GLPK solver (if installed) or the MOSEK LP solver (if installed); see the section Linear Programming. The default value is `None`.

  The solver reports the outcome of optimization by setting the attribute `self.status` and by modifying the `value` attributes of the variables and the constraint multipliers of the problem.

  - If the problem is solved to optimality, `self.status` is set to `'optimal'`. The `value` attributes of the variables in the problem are set to their computed solutions, and the `value` attributes of the multipliers of the constraints of the problem are set to the computed dual optimal solution.
  - If it is determined that the problem is infeasible, `self.status` is set to `'primal infeasible'`. The `value` attributes of the variables are set to `None`. The `value` attributes of the multipliers of the constraints of the problem are set to a certificate of primal infeasibility. With the `'glpk'` option, `solve` does not provide certificates of infeasibility.
  - If it is determined that the problem is dual infeasible, `self.status` is set to `'dual infeasible'`. The `value` attributes of the multipliers of the constraints of the problem are set to `None`. The `value` attributes of the variables are set to a certificate of dual infeasibility. With the `'glpk'` option, `solve` does not provide certificates of infeasibility.
  - If the problem was not solved successfully, `self.status` is set to `'unknown'`. The `value` attributes of the variables and the constraint multipliers are set to `None`.

We refer to the section Linear Programming for details on the algorithms and the different solver options.

As an example we solve the LP

    >>> from cvxopt.modeling import op
    >>> x = variable()
    >>> y = variable()
    >>> c1 = ( 2*x+y <= 3 )
    >>> c2 = ( x+2*y <= 3 )
    >>> c3 = ( x >= 0 )
    >>> c4 = ( y >= 0 )
    >>> lp1 = op(-4*x-5*y, [c1,c2,c3,c4])
    >>> lp1.solve()
    >>> lp1.status
    'optimal'
    >>> print(lp1.objective.value())
    [-9.00e+00]
    >>> print(x.value)
    [ 1.00e+00]
    >>> print(y.value)
    [ 1.00e+00]
    >>> print(c1.multiplier.value)
    [ 1.00e+00]
    >>> print(c2.multiplier.value)
    [ 2.00e+00]
    >>> print(c3.multiplier.value)
    [ 2.87e-08]
    >>> print(c4.multiplier.value)
    [ 2.80e-08]

We can solve the same LP in matrix form as follows.

    >>> from cvxopt.modeling import op, dot
    >>> x = variable(2)
    >>> A = matrix([[2.,1.,-1.,0.], [1.,2.,0.,-1.]])
    >>> b = matrix([3.,3.,0.,0.])
    >>> c = matrix([-4.,-5.])
    >>> ineq = ( A*x <= b )
    >>> lp2 = op(dot(c,x), ineq)
    >>> lp2.solve()
    >>> print(lp2.objective.value())
    [-9.00e+00]
    >>> print(x.value)
    [ 1.00e+00]
    [ 1.00e+00]
    >>> print(ineq.multiplier.value)
    [1.00e+00]
    [2.00e+00]
    [2.87e-08]
    [2.80e-08]

The `op` class also includes two methods for writing and reading files in MPS format.

`tofile`(filename)

  If the problem is an LP, writes it to the file filename using the MPS format. Row and column labels are assigned based on the variable and constraint names in the LP.

`fromfile`(filename)

  Reads the LP from the file filename. The file must be a fixed-format MPS file. Some features of the MPS format are not supported: comments beginning with dollar signs, the row types ‘DE’, ‘DL’, ‘DG’, and ‘DN’, and the capability of reading multiple righthand side, bound or range vectors.

## Examples

Norm and Penalty Approximation

> In the first example we solve the norm approximation problems

> and the penalty approximation problem

> We use randomly generated data.

> The code uses the Matplotlib package for plotting the histograms of the residual vectors for the two solutions. It generates the figure shown below.

>     from cvxopt import normal
>     from cvxopt.modeling import variable, op, max, sum
>     import pylab

>     m, n = 500, 100
>     A = normal(m,n)
>     b = normal(m)

>     x1 = variable(n)
>     op(max(abs(A*x1-b))).solve()

>     x2 = variable(n)
>     op(sum(abs(A*x2-b))).solve()

>     x3 = variable(n)
>     op(sum(max(0, abs(A*x3-b)-0.75, 2*abs(A*x3-b)-2.25))).solve()

>     pylab.subplot(311)
>     pylab.hist(A*x1.value-b, m/5)
>     pylab.subplot(312)
>     pylab.hist(A*x2.value-b, m/5)
>     pylab.subplot(313)
>     pylab.hist(A*x3.value-b, m/5)
>     pylab.show()

> Equivalently, we can formulate and solve the problems as LPs.

>     t = variable()
>     x1 = variable(n)
>     op(t, [-t <= A*x1-b, A*x1-b<=t]).solve()

>     u = variable(m)
>     x2 = variable(n)
>     op(sum(u), [-u <= A*x2+b, A*x2+b <= u]).solve()

>     v = variable(m)
>     x3 = variable(n)
>     op(sum(v), [v >= 0, v >= A*x3+b-0.75, v >= -(A*x3+b)-0.75, v >= 2*(A*x3-b)-2.25, v >= -2*(A*x3-b)-2.25]).solve()

Robust Linear Programming

> The robust LP

> is equivalent to the problem

> The following code computes the solution and the solution of the equivalent LP

> for randomly generated data.

>     from cvxopt import normal, uniform
>     from cvxopt.modeling import variable, dot, op, sum

>     m, n = 500, 100
>     A = normal(m,n)
>     b = uniform(m)
>     c = normal(n)

>     x = variable(n)
>     op(dot(c,x), A*x+sum(abs(x)) <= b).solve()

>     x2 = variable(n)
>     y = variable(n)
>     op(dot(c,x2), [A*x2+sum(y) <= b, -y <= x2, x2 <= y]).solve()

1-Norm Support Vector Classifier

> The following problem arises in classification:

> It can be solved as follows.

>     x = variable(A.size[1],'x')
>     u = variable(A.size[0],'u')
>     op(sum(abs(x)) + sum(u), [A*x >= 1-u, u >= 0]).solve()

> An equivalent unconstrained formulation is

>     x = variable(A.size[1],'x')
>     op(sum(abs(x)) + sum(max(0,1-A*x))).solve()

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