Split cuts from sparse disjunctions

Split cuts are arguably the most effective class of cutting planes within a branch-and-cut framework for solving general Mixed-Integer Programs (MIP). Sparsity, on the other hand, is a common characteristic of MIP problems, and it is an important part of why the simplex method works so well inside branch-and-cut. In this work, we evaluate the … Read more

Polynomial Optimization on Chebyshev-Dubiner Webs of Starlike Polygons

We construct web-shaped norming meshes on starlike polygons, by radial and boundary Chebyshev points. Via the approximation theoretic notion of Dubiner distance, we get a (1-eps)-approximation to the minimum of an arbitrary polynomial of degree n by O(n^2/eps) sampling points. Citation Preprint, July 2018 Article Download View Polynomial Optimization on Chebyshev-Dubiner Webs of Starlike Polygons

Multi-objective Ranking and Selection: Optimal Sampling Laws and Tractable Approximations via SCORE

Consider the multi-objective ranking and selection (MORS) problem in which we select the Pareto-optimal set from a finite set of systems evaluated on three or more stochastic objectives. Solving this problem is difficult because we must determine how to allocate a simulation budget among the systems to minimize the probability that any systems are misclassified. … Read more

Cutting Planes by Projecting Interior Points onto Polytope Facets

Given a point x inside a polytope P and a direction d, the projection of x along d asks to find the maximum step length t such that x+td is feasible; we say x+td is a pierce point because it belongs to the boundary of P. We address this projection sub-problem with arbitrary interior points … Read more

Generalized Stochastic Frank-Wolfe Algorithm with Stochastic “Substitute” Gradient for Structured Convex Optimization

The stochastic Frank-Wolfe method has recently attracted much general interest in the context of optimization for statistical and machine learning due to its ability to work with a more general feasible region. However, there has been a complexity gap in the guaranteed convergence rate for stochastic Frank-Wolfe compared to its deterministic counterpart. In this work, … Read more

Probabilistic Envelope Constrained Multiperiod Stochastic Emergency Medical Services Location Model and Decomposition Scheme

This paper considers a multiperiod Emergency Medical Services (EMS) location problem and introduces two two-stage stochastic programming formulations that account for uncertainty about emergency demand. While the first model considers both a constraint on the probability of covering the realized emergency demand and minimizing the expected cost of doing so, the second one employs probabilistic … Read more

A new drayage problem with different customer services and container requirements

This paper investigates a drayage problem generalizing a previously proposed, which is motivated by a case study of a real maritime carrier. To serve export and import customer requests in the hinterland of a port, a fleet of trucks able to carry one or two containers of the same size is adopted. The aim of … Read more

ACQUIRE: an inexact iteratively reweighted norm approach for TV-based Poisson image restoration

We propose a method, called ACQUIRE, for the solution of constrained optimization problems modeling the restoration of images corrupted by Poisson noise. The objective function is the sum of a generalized Kullback-Leibler divergence term and a TV regularizer, subject to nonnegativity and possibly other constraints, such as flux conservation. ACQUIRE is a line-search method that … Read more

A Wolfe line search algorithm for vector optimization

In a recent paper, Lucambio Pérez and Prudente extended the Wolfe conditions for the vector-valued optimization. Here, we propose a line search algorithm for finding a step-size satisfying the strong Wolfe conditions in the vector optimization setting. Well definiteness and finite termination results are provided. We discuss practical aspects related to the algorithm and present … Read more

A two-stage stochastic optimization model for the Bike sharing allocation and rebalancing problem

The Bikesharing allocation and rebalancing problem is the problem of determining the initial daily allocation of bikes to stations in a bikesharing system composed of one depot and multiple capacitated stations, in which bikes can be rebalanced at a point in time later in the day. We propose a two-stage stochastic programming formulation, where the … Read more