Sage Reference Manual: Numerical Optimization - Mirrors

Sage Reference Manual: Numerical Optimization, Release 6.1.1
EXAMPLE:
sage: p = MixedIntegerLinearProgram()
sage: v = p.new_variable()
sage: p.set_objective(v[0] + v[1])
sage: v.depth()
1
items()
Returns the pairs (keys,value) contained in the dictionary.
EXAMPLE:
sage: p = MixedIntegerLinearProgram()
sage: v = p.new_variable()
sage: p.set_objective(v[0] + v[1])
sage: v.items()
[(0, x_0), (1, x_1)]
keys()
Returns the keys already defined in the dictionary.
EXAMPLE:
sage: p = MixedIntegerLinearProgram()
sage: v = p.new_variable()
sage: p.set_objective(v[0] + v[1])
sage: v.keys()
[0, 1]
values()
Returns the symbolic variables associated to the current dictionary.
EXAMPLE:
sage: p = MixedIntegerLinearProgram()
sage: v = p.new_variable()
sage: p.set_objective(v[0] + v[1])
sage: v.values()
[x_0, x_1]
class sage.numerical.mip.MixedIntegerLinearProgram
Bases: sage.structure.sage_object.SageObject
The MixedIntegerLinearProgram class is the link between Sage, linear programming (LP) and mixed
integer programming (MIP) solvers.
See the Wikipedia article on linear programming for further information on linear programming and the documentation
of the MILP module for its use in Sage.
A mixed integer program consists of variables, linear constraints on these variables, and an objective
function which is to be maximised or minimised under these constraints. An instance of
MixedIntegerLinearProgram also requires the information on the direction of the optimization.
INPUT:
•solver – the following solvers should be available through this class:
–GLPK (solver="GLPK"). See the GLPK web site.
–COIN Branch and Cut (solver="Coin"). See the COIN-OR web site.
–CPLEX (solver="CPLEX"). See the CPLEX web site.
12 Chapter 2. Mixed integer linear programming