On the complexity of computing the handicap of a sufficient matrix

The class of sufficient matrices is important in the study of the linear complementarity problem(LCP) – some interior point methods (IPM’s) for LCP’s with sufficient data matrices have complexity polynomial in the bit size of the matrix and its handicap. In this paper we show that the handicap of a sufficient matrix may be exponential … Read more

Chemotherapy operations planning and scheduling

Chemotherapy operations planning and scheduling in oncology clinics is a complex problem due to several factors such as the cyclic nature of chemotherapy treatment plans, the high variability in resource requirements (treatment time, nurse time, pharmacy time) and the multiple clinic resources involved. Treatment plans are made by oncologists for each patient according to existing … Read more

Complexity of the Critical Node Problem over trees

In this paper we deal with the Critical Node Problem (CNP), i.e., the problem of searching for a given number K of nodes in a graph G, whose removal minimizes the number of connections between pairs of nodes in the residual graph. While the NP-completeness of this problem for general graphs has been already established … Read more

Discriminants and Nonnegative Polynomials

For a semialgebraic set K in R^n, let P_d(K) be the cone of polynomials in R^n of degrees at most d that are nonnegative on K. This paper studies the geometry of its boundary. When K=R^n and d is even, we show that its boundary lies on the irreducible hypersurface defined by the discriminant of … Read more

A comparison of sample-based Stochastic Optimal Control methods

In this paper, we compare the performance of two scenario-based numerical methods to solve stochastic optimal control problems: scenario trees and particles. The problem consists in finding strategies to control a dynamical system perturbed by exogenous noises so as to minimize some expected cost along a discrete and finite time horizon. We introduce the Mean … Read more

Sparse optimization with least-squares constraints

The use of convex optimization for the recovery of sparse signals from incomplete or compressed data is now common practice. Motivated by the success of basis pursuit in recovering sparse vectors, new formulations have been proposed that take advantage of different types of sparsity. In this paper we propose an efficient algorithm for solving a … Read more

A concave optimization-based approach for sparse portfolio selection

This paper considers a portfolio selection problem in which portfolios with minimum number of active assets are sought. This problem is motivated by the need of inducing sparsity on the selected portfolio to reduce transaction costs, complexity of portfolio management, and instability of the solution. The resulting problem is a difficult combinatorial problem. We propose … Read more

Efficient and Fair Routing for Mesh Networks

Inspired by the One Laptop Per Child project, we consider mesh networks that connect devices that cannot recharge their batteries easily. We study how the mesh should retransmit information to make use of the energy stored in each of the nodes effectively. The solution that minimizes the total energy spent by the whole network may … Read more

Linearized augmented Lagrangian and alternating direction methods for nuclear norm minimization

The nuclear norm is widely used to induce low-rank solutions for many optimization problems with matrix variables. Recently, it has been shown that the augmented Lagrangian method (ALM) and the alternating direction method (ADM) are very efficient for many convex programming problems arising from various applications, provided that the resulting subproblems are sufficiently simple to … Read more