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math.OC2020

Piecewise Polyhedral Formulations for a Multilinear Term

Kaarthik Sundar, Harsha Nagarajan, Jeff Linderoth +2

In this paper, we present a mixed-integer linear programming (MILP) formulation of a piecewise, polyhedral relaxation (PPR) of a multilinear term using its convex hull representati…

math.OC2019

Integer packing sets form a well-quasi-ordering

Alberto Del Pia, Dion Gijswijt, Jeff Linderoth +1

An integer packing set is a set of non-negative integer vectors with the property that, if a vector is in the set, then every non-negative integer vector with is…

math.OC2018

Strong Convex Nonlinear Relaxations of the Pooling Problem

James Luedtke, Claudia D'Ambrosio, Jeff Linderoth +1

We investigate new convex relaxations for the pooling problem, a classic nonconvex production planning problem in which input materials are mixed in intermediate pools, with the ou…

math.OC2017

Combining Progressive Hedging with a Frank-Wolfe Method to Compute Lagrangian Dual Bounds in Stochastic Mixed-Integer Programming

Natashia Boland, Jeffrey Christiansen, Brian Dandurand +3

We present a new primal-dual algorithm for computing the value of the Lagrangian dual of a stochastic mixed-integer program (SMIP) formed by relaxing its nonanticipativity constrai…

math.OC2001

Decomposition Algorithms for Stochastic Programming on a Computational Grid

Jeff Linderoth, Stephen Wright

We describe algorithms for two-stage stochastic linear programming with recourse and their implementation on a grid computing platform. In particular, we examine serial and asynchr…