Section:
Scientific Foundations
Polyhedral
approaches for MIP
Adding valid inequalities to the polyhedral description of an MIP
allows one to improve the resulting LP bound and hence to better
prune the enumeration tree. In a cutting plane procedure, one
attempt to identify valid inequalities that are violated by the LP
solution of the current formulation and adds them to the
formulation. This can be done at each node of the branch-and-bound
tree giving rise to a so-called branch-and-cut algorithm
[59] . The goal is to reduce the resolution of an
integer program to that of a linear program by deriving a linear
description of the convex hull of the feasible solutions. Polyhedral
theory tells us that if is a mixed integer program: where with
matrix , then is a
polyhedron that can be described in terms of linear constraints,
i.e. it writes as
for some matrix although the
dimension is typically quite large. A fundamental result in
this field is the equivalence of complexity between solving the
combinatorial optimization problem and
solving the separation problem over the associated polyhedron
: if , find a linear inequality
satisfied by all points in but
violated by . Hence, for NP-hard problems, one can not
hope to get a compact description of nor a polynomial time
exact separation routine. Polyhedral studies focus on identifying
some of the inequalities that are involved in the polyhedral
description of and derive efficient separation
procedures (cutting plane generation). Only a subset of the
inequalities can offer a good approximation, that
combined with a branch-and-bound enumeration techniques permits to
solve the problem. Using cutting plane algorithm at each node
of the branch-and-bound tree, gives rise to the algorithm called
branch-and-cut.