49 citations · 147 across the 19 of their papers we have counts for
23 papers · 1 filter
Aggregated Column Generation for Two-Stage Stochastic Mixed-Integer Programs
Harshit Kothari, James Luedtke, Luke Marshall
Dantzig-Wolfe reformulation applied to two-stage stochastic mixed-integer programs (SMIP) has been shown empirically to yield a formulation with strong bounds that can be solved vi…
Sparse Cuts for the Positive Semidefinite Cone
Oktay Günlük, Paul Jünger, Jeff Linderoth +2
We consider optimization problems containing nonconvex quadratic functions for which semidefinite programming (SDP) relaxations often yield strong bounds. We investigate linear ine…
Accelerating Benders decomposition for solving a sequence of sample average approximation replications
Harshit Kothari, James R. Luedtke
Sample average approximation (SAA) is a technique for obtaining approximate solutions to stochastic programs that uses the average from a random sample to approximate the expected…
A Framework for Balancing Power Grid Efficiency and Risk with Bi-objective Stochastic Integer Optimization
Ramsey Rossmann, Mihai Anitescu, Julie Bessac +4
Power grid expansion planning requires making large investment decisions in the present that will impact the future cost and reliability of a system exposed to wide-ranging uncerta…
An Integer Programming Approach To Subspace Clustering With Missing Data
Akhilesh Soni, Jeff Linderoth, Jim Luedtke +1
In the Subspace Clustering with Missing Data (SCMD) problem, we are given a collection of n partially observed d-dimensional vectors. The data points are assumed to be concentrated…
Stochastic Dynamic Lot-sizing with Supplier-Driven Substitution and Service Level Constraints
Narges Sereshti, Merve Bodur, James R. Luedtke
We consider a multi-stage stochastic lot-sizing problem with service level constraints and supplier-driven product substitution. A firm has multiple products and it has the option…
Data-Driven Sample Average Approximation with Covariate Information
Rohit Kannan, Güzin Bayraksan, James R. Luedtke
We study optimization for data-driven decision-making when we have observations of the uncertain parameters within the optimization model together with concurrent observations of c…
Sparse multi-term disjunctive cuts for the epigraph of a function of binary variables
Rui Chen, James Luedtke
We propose a new method for separating valid inequalities for the epigraph of a function of binary variables. The proposed inequalities are disjunctive cuts defined by disjunctive…
On Generating Lagrangian Cuts for Two-Stage Stochastic Integer Programs
Rui Chen, James Luedtke
We investigate new methods for generating Lagrangian cuts to solve two-stage stochastic integer programs. Lagrangian cuts can be added to a Benders reformulation, and are derived f…
Heteroscedasticity-aware residuals-based contextual stochastic optimization
Rohit Kannan, Güzin Bayraksan, James Luedtke
We explore generalizations of some integrated learning and optimization frameworks for data-driven contextual stochastic optimization that can adapt to heteroscedasticity. We ident…
Residuals-based distributionally robust optimization with covariate information
Rohit Kannan, Güzin Bayraksan, James R. Luedtke
We consider data-driven approaches that integrate a machine learning prediction model within distributionally robust optimization (DRO) given limited joint observations of uncertai…
Lagrangian Dual Decision Rules for Multistage Stochastic Mixed Integer Programming
Maryam Daryalal, Merve Bodur, James R. Luedtke
Multistage stochastic programs can be approximated by restricting policies to follow decision rules. Directly applying this idea to problems with integer decisions is difficult bec…
On sample average approximation for two-stage stochastic programs without relatively complete recourse
Rui Chen, James Luedtke
We investigate sample average approximation (SAA) for two-stage stochastic programs without relatively complete recourse, i.e., for problems in which there are first-stage feasible…
Stochastic DC Optimal Power Flow With Reserve Saturation
Rohit Kannan, James R. Luedtke, Line A. Roald
We propose an optimization framework for stochastic optimal power flow with uncertain loads and renewable generator capacity. Our model follows previous work in assuming that gener…
Solving Chance-Constrained Problems via a Smooth Sample-Based Nonlinear Approximation
Alejandra Peña-Ordieres, James R. Luedtke, Andreas Wächter
We introduce a new method for solving nonlinear continuous optimization problems with chance constraints. Our method is based on a reformulation of the probabilistic constraint as…
Intersection disjunctions for reverse convex sets
Eli Towle, James Luedtke
We present a framework to obtain valid inequalities for a reverse convex set: the set of points in a polyhedron that lie outside a given open convex set. Reverse convex sets arise…
A stochastic approximation method for approximating the efficient frontier of chance-constrained nonlinear programs
Rohit Kannan, James Luedtke
We propose a stochastic approximation method for approximating the efficient frontier of chance-constrained nonlinear programs. Our approach is based on a bi-objective viewpoint of…
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…
A Dual Approximate Dynamic Programming Approach to Multi-stage Stochastic Unit Commitment
Jagdish Ramakrishnan, James Luedtke
We study the multi-stage stochastic unit commitment problem in which commitment and generation decisions can be made and adjusted in each time period. We formulate this problem as…
New solution approaches for the maximum-reliability stochastic network interdiction problem
Eli Towle, James Luedtke
We investigate methods to solve the maximum-reliability stochastic network interdiction problem (SNIP). In this problem, a defender interdicts arcs on a directed graph to minimize…
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…
Two-stage Linear Decision Rules for Multi-stage Stochastic Programming
Merve Bodur, James Luedtke
Multi-stage stochastic linear programs (MSLPs) are notoriously hard to solve in general. Linear decision rules (LDRs) yield an approximation of an MSLP by restricting the decisions…
A Cycle-Based Formulation and Valid Inequalities for DC Power Transmission Problems with Switching
Burak Kocuk, Hyemin Jeon, Santanu S. Dey +3
It is well-known that optimizing network topology by switching on and off transmission lines improves the efficiency of power delivery in electrical networks. In fact, the USA Ener…