MW: A Software Framework for Combinatorial Optimization on Computational Grids

Our goal in this paper is to demonstrate that branch-and-bound algorithms for combinatorial optimization can be effectively implemented on a relatively new type of multiprocessor platform known as a computational grid. We will argue that to easily and effectively harness the power of computational grids for branch-and-bound algorithms, the master-worker paradigm should be used to … Read more

The Impact of Sampling Methods on Bias and Variance in Stochastic Linear Programs

Two-stage stochastic linear programs can be solved approximately by drawing a subset of all possible random scenarios and solving the problem based on this subset, an approach known as sample path optimization. Sample path optimization creates two kinds of objective function bias. First, the expected optimal objective function value for the sampled problem is lower … Read more

Noncommercial Software for Mixed-Integer Linear Programming

We present an overview of noncommercial software tools for the solution of mixed-integer linear programs (MILPs). We first review solution methodologies for MILPs and then present an overview of the available software, including detailed descriptions of eight software packages available under open source or other noncommercial licenses. Each package is categorized as a black box … Read more

A Simplicial Branch-and-Bound Algorithm for Solving Quadratically Constrained Quadratic Programs

We propose a branch-and-bound algorithm for solving nonconvex quadratically-constrained quadratic programs. The algorithm is novel in that branching is done by partitioning the feasible region into the Cartesian product of two-dimensional triangles and rectangles. Explicit formulae for the convex and concave envelopes of bilinear functions over triangles and rectangles are derived and shown to be … Read more

The Empirical Behavior of Sampling Methods for Stochastic Programming

We investigate the quality of solutions obtained from sample-average approximations to two-stage stochastic linear programs with recourse. We use a recently developed software tool executing on a computational grid to solve many large instances of these problems, allowing us to obtain high-quality solutions and to verify optimality and near-optimality of the computed solutions in various … Read more

Facets of The Cardinality Constrained Circuit Polytope

The Cardinality Constrained Circuit Problem (CCCP) is the problem of finding a minimum cost circuit in a graph where the circuit is constrained to have at most $k$ edges. The CCCP is NP-Hard. We present classes of facet-inducing inequalities for the convex hull of feasible circuits. Article Download View Facets of The Cardinality Constrained Circuit … Read more

Decomposition Algorithms for Stochastic Programming on a Computational Grid

We describe algorithms for two-stage stochastic linear programming with recourse and their implementation on a grid computing platform. In particular, we examine serial and asynchronous versions of the L-shaped method and a trust-region method. The parallel platform of choice is the dynamic, heterogeneous, opportunistic platform provided by the Condor system. The algorithms are of master-worker … Read more

Solving Large Quadratic Assignment Problems on Computational Grids

The quadratic assignment problem (QAP) is among the hardest combinatorial optimization problems. Some instances of size n >= 30 have remained unsolved for decades. The solution of these problems requires both improvements in mathematical programming algorithms and the utilization of powerful computational platforms. In this article we describe a novel approach to solve QAPs using … Read more

A Parallel, Linear Programming Based Heuristic for Large Scale Set Partitioning Problems

We describe a parallel, linear programming and implication based heuristic for solving set partitioning problems on distributed memory computer architectures. Our implementation is carefully designed to exploit parallelism to greatest advantage in advanced techniques like preprocessing and probing, primal heuristics, and cut generation. A primal-dual subproblem simplex method is used for solving the linear programming … Read more