An extension of Chubanov’s algorithm to symmetric cones

In this work we present an extension of Chubanov’s algorithm to the case of homogeneous feasibility problems over a symmetric cone K. As in Chubanov’s method for linear feasibility problems, the algorithm consists of a basic procedure and a step where the solutions are confined to the intersection of a half-space and K. Following an … Read more

An optimization-based approach for delivering radio-pharmaceuticals to medical imaging centers

It is widely recognized that early diagnosis of most types of cancers can increase the chances of full recovery or substantially prolong the life of patients. Positron Emission Tomography (PET) has become the standard way to diagnose many types of cancers by generating high quality images of the affected organs. In order to create an … Read more

Recent Progress Using Matheuristics for Strategic Maritime Inventory Routing

This paper presents an extensive computational study of simple, but prominent matheuristics (i.e., heuristics that rely on mathematical programming models) to fi nd high quality ship schedules and inventory policies for a class of maritime inventory routing problems. Our computational experiments are performed on a set of the publicly available MIRPLib instances. This class of inventory … Read more

Automated timetabling for small colleges and high schools using huge integer programs

We formulate an integer program to solve a highly constrained academic timetabling problem at the United States Merchant Marine Academy. The IP instance that results from our real case study has approximately both 170,000 rows and columns and solves to near optimality in 12 hours, using a commercial solver. Our model is applicable to both … Read more

Flow formulations for curriculum-based course timetabling

In this paper we present two mixed-integer programming formulations for the curriculum based course timetabling problem (CTT). We show that the formulations contain underlying network structures by dividing the CTT into two separate models and then connect the two models using flow formulation techniques. The first mixed-integer programming formulation is based on an underlying minimum … Read more

Optimality conditions for problems over symmetric cones and a simple augmented Lagrangian method

In this work we are interested in nonlinear symmetric cone problems (NSCPs), which contain as special cases nonlinear semidefinite programming, nonlinear second order cone programming and the classical nonlinear programming problems. We explore the possibility of reformulating NSCPs as common nonlinear programs (NLPs), with the aid of squared slack variables. Through this connection, we show … Read more

The p-cones in dimension n>=3 are not homogeneous when p \neq 2

Using the T-algebra machinery we show that the only strictly convex homogeneous cones in R^n with n >= 3 are the 2-cones, also known as Lorentz cones or second order cones. In particular, this shows that the p-cones are not homogeneous when p is not 2, 1 < p <\infty and n >= 3, thus … Read more

Efficiency of minimizing compositions of convex functions and smooth maps

We consider the problem of minimizing a sum of a convex function and a composition of a convex function with a smooth map. Important examples include exact penalty formulations of nonlinear programs and nonlinear least squares problems with side constraints. The basic algorithm we rely on is the well-known prox-linear method, which in each iteration … Read more

A Limited-Memory Quasi-Newton Algorithm for Bound-Constrained Nonsmooth Optimization

We consider the problem of minimizing a continuous function that may be nonsmooth and nonconvex, subject to bound constraints. We propose an algorithm that uses the L-BFGS quasi-Newton approximation of the problem’s curvature together with a variant of the weak Wolfe line search. The key ingredient of the method is an active-set selection strategy that … Read more

Understanding Deep Neural Networks with Rectified Linear Units

In this paper we investigate the family of functions representable by deep neural networks (DNN) with rectified linear units (ReLU). We give the first-ever polynomial time (in the size of data) algorithm to train a ReLU DNN with one hidden layer to {\em global optimality}. This follows from our complete characterization of the ReLU DNN … Read more