Multi-Stage Robust Mixed-Integer Programming

Multi-stage robust optimization, in which decisions are taken sequentially as new information becomes available about the uncertain problem parameters, is a very versatile yet computationally challenging paradigm for decision-making under uncertainty. In this paper, we propose a new model and solution approach for multi-stage robust mixed-integer programs, which may contain both continuous and discrete decisions … Read more

A Quasi-Newton Algorithm for Optimal Discretization of Markov Processes

In stochastic programming and stochastic-dynamic programming discretization of random model parameters is often unavoidable. We propose a quasi-Newton learning algorithm to discretize multi-dimensional, continuous discrete-time Markov processes to scenario lattices by minimizing the Wasserstein distance between the unconditional distributions of process and lattice. Scenario lattices enable accurate discretization of the conditional distributions of Markov processes … Read more

On the B-differential of the componentwise minimum of two affine vector functions

This paper focuses on the description and computation of the B-differential of the componentwise minimum of two affine vector functions. This issue arises in the reformulation of the linear complementarity problem with the Min C-function. The question has many equivalent formulations and we identify some of them in linear algebra, convex analysis and discrete geometry. … Read more

Balancing Communication and Computation in Gradient Tracking Algorithms for Decentralized Optimization

Gradient tracking methods have emerged as one of the most popular approaches for solving decentralized optimization problems over networks. In this setting, each node in the network has a portion of the global objective function, and the goal is to collectively optimize this function. At every iteration, gradient tracking methods perform two operations (steps): (1) … Read more

Proximal bundle methods for hybrid weakly convex composite optimization problems

This paper establishes the iteration-complexity of proximal bundle methods for solving hybrid (i.e., a blend of smooth and nonsmooth) weakly convex composite optimization (HWC-CO) problems. This is done in a unified manner by considering a proximal bundle framework (PBF) based on a generic bundle update scheme which includes various well-known bundle update schemes. In contrast … Read more

Polyhedral Properties of RLT Relaxations of Nonconvex Quadratic Programs and Their Implications on Exact Relaxations

We study linear programming relaxations of nonconvex quadratic programs given by the reformulation-linearization technique (RLT), referred to as RLT relaxations. We investigate the relations between the polyhedral properties of the feasible regions of a quadratic program and its RLT relaxation. We establish various connections between recession directions, boundedness, and vertices of the two feasible regions. … Read more

Test Instances for Multiobjective Mixed-Integer Nonlinear Optimization

A suitable set of test instances, also known as benchmark problems, is a key ingredient to systematically evaluate numerical solution algorithms for a given class of optimization problems. While in recent years several solution algorithms for the class of multiobjective mixed-integer nonlinear optimization problems have been proposed, there is a lack of a well-established set … Read more

An Inexact Proximal-indefinite Stochastic ADMM with applications in 3D CT reconstruction

In this paper, we develop an Inexact Proximal-indefinite Stochastic ADMM (abbreviated as IPS-ADMM) for solving a class of separable convex optimization problems whose objective functions consist of two parts: one is an average of many smooth convex functions and another is a convex but possibly nonsmooth function. The involved smooth subproblem is tackled by an … Read more

Integer Programming Approaches for Distributionally Robust Chance Constraints with Adjustable Risks

We study distributionally robust chance constrained programs (DRCCPs)  with individual chance constraints and random right-hand sides. The DRCCPs treat the risk tolerances associated with the distributionally robust chance constraints (DRCCs) as decision variables to trade off between the system cost and risk of violations by penalizing the risk tolerances in the objective function. We consider … Read more

The Online Shortest Path Problem: Learning Travel Times Using A Multi-Armed Bandit Framework

In the age of e-commerce, many logistic companies must operate in large road networks without accurate knowledge of travel times for their specific fleet of vehicles. Moreover, millions of dollars are spent on routing services that do not accurately capture the specific characteristics of the companies’ drivers and the types of vehicles they must use. … Read more