Revisiting the Greedy Approach to Submodular Set Function Maximization

We consider the problem of maximizing a nondecreasing submodular set function over various constraint structures. Specifically, we explore the performance of the greedy algorithm, and a related variant, the locally greedy algorithm in solving submodular function maximization problems. Most classic results on the greedy algorithm and its variant assume the existence of an optimal polynomial-time … Read more

A Min-Max Regret Robust Optimization Approach for Large Scale Full Factorial Scenario Design of Data Uncertainty

This paper presents a three-stage optimization algorithm for solving two-stage robust decision making problems under uncertainty with min-max regret objective. The structure of the first stage problem is a general mixed integer (binary) linear programming model with a specific model of uncertainty that can occur in any of the parameters, and the second stage problem … Read more

A Constraint-Reduced Variant of Mehrotra’s Predictor-Corrector Algorithm

Consider linear programs in dual standard form with n constraints and m variables. When typical interior-point algorithms are used for the solution of such problems, updating the iterates, using direct methods for solving the linear systems and assuming a dense constraint matrix A, requires O(nm^2) operations. When n>>m it is often the case that at … Read more

A polynomial-time interior-point method for conic optimization, with inexact barrier evaluations

In this work we develop a primal-dual short-step interior point method for conic convex optimization problems for which exact evaluation of the gradient and Hessian of the barrier function is either impossible or too expensive. As our main contribution, we show that if approximate gradients and Hessians can be computed, and the relative errors in … Read more

A Branch-and-Cut Algorithm based on Semidefinite Programming for the Minimum k-Partition Problem

The minimum k-partition (MkP) problem is the problem of partitioning the set of vertices of a graph into k disjoint subsets so as to minimize the total weight of the edges joining vertices in the same partition. The main contribution of this paper is the design and implementation of a branch-and-cut algorithm based on semidefinite … Read more

Algorithms to Separate {0,1/2}-Chvatal-Gomory Cuts

Chvatal-Gomory cuts are among the most well-known classes of cutting planes for general integer linear programs (ILPs). In case the constraint multipliers are either 0 or 1/2, such cuts are known as {0, 1/2}-cuts. It has been proven by Caprara and Fischetti (1996) that separation of {0, 1/2}-cuts is NP-hard. In this paper, we study … Read more

A polyhedral study of the Network Pricing Problem with Connected Toll Arcs

Consider the problem that consists in maximizing the revenue generated by tolls set on a subset of arcs of a transportation network, and where origin-destination flows are assigned to shortest paths with respect to the sum of tolls and initial costs. In this work, we address the instance where toll arcs must be connected, as … Read more

Another Face of DIRECT

It is shown that, contrary to a claim of [D. E. Finkel, and C. T. Kelley, Additive scaling and the DIRECT algorithm, J. Glob. Optim. 36 (2006) 597-608], it is possible to divide the smallest hypercube which contains the low function value by considering hyperrectangles whose points are located on the diagonal of the center … Read more

Iterative Minimization Schemes for Solving the Single Source Localization Problem

We consider the problem of locating a single radiating source from several noisy measurements using a maximum likelihood (ML) criteria. The resulting optimization problem is nonconvex and nonsmooth and thus finding its global solution is in principal a hard task. Exploiting the special structure of the objective function, we introduce and analyze two iterative schemes … Read more

Outcome-Space Outer Approximation Algorithm for Linear Multiplicative Programming

This paper presents an outcome-space outer approximation algorithm for globally solving the linear multiplicative programming problem. We prove that the proposed algorithm is finite. To illustrate the new algorithm, we apply it to solve some sample problems. Citation10, Hanoi University of Technology, 07/2007ArticleDownload View PDF