A two-phase method for selecting IMRT treatment beam angles: Branch-and-Prune and local neighborhood search

This paper presents a new two-phase solution approach to the beam angle and fluence map optimization problem in Intensity Modulated Radiation Therapy (IMRT) planning. We introduce Branch-and-Prune (B&P) to generate a robust feasible solution in the first phase. A local neighborhood search algorithm is developed to find a local optimal solution from the Phase I … Read more

An FPTAS for Optimizing a Class of Low-Rank Functions Over a Polytope

We present a fully polynomial time approximation scheme (FPTAS) for optimizing a very general class of nonlinear functions of low rank over a polytope. Our approximation scheme relies on constructing an approximate Pareto-optimal front of the linear functions which constitute the given low-rank function. In contrast to existing results in the literature, our approximation scheme … Read more

A General Framework for Designing Approximation Schemes for Combinatorial Optimization Problems with Many Objectives Combined Into One

In this paper, we present a general framework for designing approximation schemes for combinatorial optimization problems in which the objective function is a combination of more than one function. Examples of such problems include those in which the objective function is a product or ratio of two linear functions, parallel machine scheduling problems with the … Read more

Lower bounds for Chvátal-Gomory style operators

Valid inequalities or cutting planes for (mixed-) integer programming problems are an essential theoretical tool for studying combinatorial properties of polyhedra. They are also of utmost importance for solving optimization problems in practice; in fact any modern solver relies on several families of cutting planes. The Chvátal-Gomory procedure, one such approach, has a peculiarity that … Read more

Strong Branching Inequalities for Convex Mixed Integer Nonlinear Programs

Strong branching is an effective branching technique that can significantly reduce the size of the branch-and-bound tree for solving Mixed Integer Nonlinear Programming (MINLP) problems. The focus of this paper is to demonstrate how to effectively use discarded information from strong branching to strengthen relaxations of MINLP problems. Valid inequalities such as branching-based linearizations, various … Read more

Trajectory-following methods for large-scale degenerate convex quadratic programming

We consider a class of infeasible, path-following methods for convex quadratric programming. Our methods are designed to be effective for solving both nondegerate and degenerate problems, where degeneracy is understood to mean the failure of strict complementarity at a solution. Global convergence and a polynomial bound on the number of iterations required is given. An … Read more

Facets for the Maximum Common Induced Subgraph Problem Polytope

This paper presents some strong valid inequalities for the Maximum Common Induced Subgraph Problem (MCIS) and the proofs that the inequalities are facet-defining under certain conditions. The MCIS is an NP-hard problem and, therefore, no polynomial time algorithm is known to solve it. In this context, the study of its polytope can help in the … Read more

Adjoint Sensitivity Analysis for Numerical Weather Prediction: Applications to Power Grid Optimization

We present an approach to estimate adjoint sensitivities of economic metrics of relevance in the power grid with respect to physical weather variables using numerical weather prediction models. We demonstrate that this capability can significantly enhance planning and operations. We illustrate the method using a large-scale computational study where we compute sensitivities of the regional … Read more

A comparison of routing sets for robust network design

Designing a network able to route a set of non-simultaneous demand vectors is an important problem arising in telecommunications. The problem can be seen a two-stage robust program where the recourse function consists in choosing the routing for each demand vector. Allowing the routing to change arbitrarily as the demand varies yields a very difficult … Read more

Cuts over Extended Formulations by Flow Discretization

Large-sized extended formulations have the potential for providing high-quality bounds on some combinatorial optimization problems where the natural formulations perform poorly. This chapter discusses the use of some families of cuts that have been recently applied on extended formulations that are obtained by the discretization of the continuous variables occurring in the natural formulation of … Read more