64 research outputs found
A Finite-Time Cutting Plane Algorithm for Distributed Mixed Integer Linear Programming
Many problems of interest for cyber-physical network systems can be
formulated as Mixed Integer Linear Programs in which the constraints are
distributed among the agents. In this paper we propose a distributed algorithm
to solve this class of optimization problems in a peer-to-peer network with no
coordinator and with limited computation and communication capabilities. In the
proposed algorithm, at each communication round, agents solve locally a small
LP, generate suitable cutting planes, namely intersection cuts and cost-based
cuts, and communicate a fixed number of active constraints, i.e., a candidate
optimal basis. We prove that, if the cost is integer, the algorithm converges
to the lexicographically minimal optimal solution in a finite number of
communication rounds. Finally, through numerical computations, we analyze the
algorithm convergence as a function of the network size.Comment: 6 pages, 3 figure
Lift-and-project inequalities
The lift-and-project technique is a systematic way to generate valid inequalities
for a mixed binary program. The technique is interesting both on the theoretical and
on the practical point of view. On the theoretical side it allows one to construct the
inequality description of the convex hull of all mixed-{0,1}
solutions of a binary MIP in n repeated applications of the technique, where
n is the number of binary variables.
On the practical side, a variant of the method allows one to derive some cutting planes
from the simplex tableau rather efficiently
Continuous cutting plane algorithms in integer programming
Cutting planes for mixed-integer linear programs (MILPs) are typically
computed in rounds by iteratively solving optimization problems. Instead, we
reframe the problem of finding good cutting planes as a continuous optimization
problem over weights parametrizing families of valid inequalities. We propose a
practical two-step algorithm, and demonstrate empirical gains when optimizing
Gomory mixed-integer inequalities over various families of MILPs. Finally, a
reinterpretation of our approach as optimization of the subadditive dual using
a deep neural network is provided. Code for reproducing the experiments can be
found at https://github.com/dchetelat/subadditive
An In-Out Approach to Disjunctive Optimization
Abstract. Cutting plane methods are widely used for solving convex optimization problems and are of fundamental importance, e.g., to pro-vide tight bounds for Mixed-Integer Programs (MIPs). This is obtained by embedding a cut-separation module within a search scheme. The importance of a sound search scheme is well known in the Constraint Programming (CP) community. Unfortunately, the “standard ” search scheme typically used for MIP problems, known as the Kelley method, is often quite unsatisfactory because of saturation issues. In this paper we address the so-called Lift-and-Project closure for 0-1 MIPs associated with all disjunctive cuts generated from a given set of elementary disjunction. We focus on the search scheme embedding the generated cuts. In particular, we analyze a general meta-scheme for cutting plane algorithms, called in-out search, that was recently proposed by Ben-Ameur and Neto [1]. Computational results on test instances from the literature are presented, showing that using a more clever meta-scheme on top of a black-box cut generator may lead to a significant improvement
Branching via Cutting Plane Selection: Improving Hybrid Branching
Cutting planes and branching are two of the most important algorithms for
solving mixed-integer linear programs. For both algorithms, disjunctions play
an important role, being used both as branching candidates and as the
foundation for some cutting planes. We relate branching decisions and cutting
planes to each other through the underlying disjunctions that they are based
on, with a focus on Gomory mixed-integer cuts and their corresponding split
disjunctions. We show that selecting branching decisions based on quality
measures of Gomory mixed-integer cuts leads to relatively small
branch-and-bound trees, and that the result improves when using cuts that more
accurately represent the branching decisions. Finally, we show how the history
of previously computed Gomory mixed-integer cuts can be used to improve the
performance of the state-of-the-art hybrid branching rule of SCIP. Our results
show a 4\% decrease in solve time, and an 8\% decrease in number of nodes over
affected instances of MIPLIB 2017
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