68. simplex

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68.1 Introduction to simplex

simplex is a package for linear optimization using the simplex algorithm.

Example:

(%i1) load("simplex")$
(%i2) minimize_lp(x+y, [3*x+2*y>2, x+4*y>3]);
                  9        7       1
(%o2)            [--, [y = --, x = -]]
                  10       10      5

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68.2 Functions and Variables for simplex

Option variable: epsilon_lp

Default value: 10^-8

Epsilon used for numerical computations in linear_program.

See also: linear_program.

Function: linear_program (A, b, c)

linear_program is an implementation of the simplex algorithm. linear_program(A, b, c) computes a vector x for which c.x is minimum possible among vectors for which A.x = b and x >= 0. Argument A is a matrix and arguments b and c are lists.

linear_program returns a list which contains the minimizing vector x and the minimum value c.x. If the problem is not bounded, it returns "Problem not bounded!" and if the problem is not feasible, it returns "Problem not feasible!".

To use this function first load the simplex package with load(simplex);.

Example:

(%i2) A: matrix([1,1,-1,0], [2,-3,0,-1], [4,-5,0,0])$
(%i3) b: [1,1,6]$
(%i4) c: [1,-2,0,0]$
(%i5) linear_program(A, b, c);
                   13     19        3
(%o5)            [[--, 4, --, 0], - -]
                   2      2         2

See also: minimize_lp, scale_lp, and epsilon_lp.

Function: maximize_lp (obj, cond, [pos])

Maximizes linear objective function obj subject to some linear constraints cond. See minimize_lp for detailed description of arguments and return value.

See also: minimize_lp.

Function: minimize_lp (obj, cond, [pos])

Minimizes a linear objective function obj subject to some linear constraints cond. cond a list of linear equations or inequalities. In strict inequalities > is replaced by >= and < by <=. The optional argument pos is a list of decision variables which are assumed to be positive.

If the minimum exists, minimize_lp returns a list which contains the minimum value of the objective function and a list of decision variable values for which the minimum is attained. If the problem is not bounded, minimize_lp returns "Problem not bounded!" and if the problem is not feasible, it returns "Ploblem not feasible!".

The decision variables are not assumed to be nonegative by default. If all decision variables are nonegative, set nonegative_lp to true. If only some of decision variables are positive, list them in the optional argument pos (note that this is more efficient than adding constraints).

minimize_lp uses the simplex algorithm which is implemented in maxima linear_program function.

To use this function first load the simplex package with load(simplex);.

Examples:

(%i1) minimize_lp(x+y, [3*x+y=0, x+2*y>2]);
                      4       6        2
(%o1)                [-, [y = -, x = - -]]
                      5       5        5
(%i2) minimize_lp(x+y, [3*x+y>0, x+2*y>2]), nonegative_lp=true;
(%o2)                [1, [y = 1, x = 0]]
(%i3) minimize_lp(x+y, [3*x+y=0, x+2*y>2]), nonegative_lp=true;
(%o3)                Problem not feasible!
(%i4) minimize_lp(x+y, [3*x+y>0]);
(%o4)                Problem not bounded!

See also: maximize_lp, nonegative_lp, epsilon_lp.

Option variable: nonegative_lp

Default value: false

If nonegative_lp is true all decision variables to minimize_lp and maximize_lp are assumed to be positive.