We consider a class of min-max robust problems in which the functions that need to be robustified can be decomposed as the sum of arbitrary functions. This...

We present a case study of using machine learning classification algorithms to initialize a large scale commercial operations research solver (GENCOL) in the...

Drawing on statistical learning theory, we derive out-of-sample and optimality guarantees about the investment strategy obtained from a regularized portfoli...

This paper considers a dynamic Emergency Medical Services (EMS) network design problem and introduces two novel two-stage stochastic programming formulatio...

Randomized decision making refers to the process of taking decisions randomly according to the outcome of an independent randomization device such as a dic...

We consider several time series and for each of them, we fit an appropriate dynamic parametric model. This produces serially independent error terms for each...

The minimum residual method (MINRES) of Paige and Saunders (1975), which is often the method of choice for symmetric linear systems, is a generalization of t...

For nearest neighbor univariate random walks in a periodic environment, where the probability of moving depends on a periodic function, we show how to estim...

Given \(n\) points, a symmetric dissimilarity matrix \(D\) of dimensions \(n\times n\) and an integer \(p\geq 2\), the \(p\)-dispersion problem (pD...

We study the time evolution of a vertically and horizontally differentiated oligopolistic industry, where firms compete in quantity and are divided into gr...

In the Inventory Routing Problem customer demand is satisfied from inventory which is replenished with capacitated vehicles. The objective is to minimize tot...

Computational speed and global optimality are a key need for pratical algorithms of the OPF problem. Recently, we proposed a tight-and-cheap conic relaxation...

Multi-agent reinforcement learning has made significant progress in recent years, but it remains a hard problem. Hence, one often resorts to developing lea...

We propose an iterative method named USYMLQR for the solution of symmetric saddle-point systems that exploits the orthogonal tridiagonalization method of Sa...

We propose a regularization method for nonlinear least-squares problems with equality constraints. Our approach is modeled after those of Arreckx and Orban ...

In demand-response programs, aggregators balance the needs of generation companies and end-users. This work proposes a two-phase framework that shaves the ag...

We are interested in blackbox optimization for which the user is aware of monotonic behaviour of some constraints defining the problem. That is, when incr...

We are witnessing an acceleration in the uptake of renewable energy in power systems. Because of the associated variability and uncertainty of renewables, ...

Recommender systems make use of different sources of information for providing users with recommendations of items. Such systems are often based on collabor...

Given a set of duties to be operated over a cyclic one-week horizon and groups of drivers with similar characteristics, the cyclic bus driver rostering probl...