3 citations · 3 across the 2 of their papers we have counts for
7 papers
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…
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…