Now showing items 1-10 of 19
Simple Integer Recourse Models
Convexity and Convex Approximations
We consider the objective function of a simple recourse problem with fixed technology matrix and integer second-stage variables. Separability due to the simple recourse structure allows to study a one-dimensional version ...
Extending algebraic modelling languages for Stochastic Programming
The algebraic modelling languages (AML) have gained wide acceptance and use in Mathematical Programming by researchers and practitioners. At a basic level, stochastic programming models can be defined using these languages ...
Stochastic integer programming
Limit theorems and confidence intervals
We consider empirical approximations of two-stage stochastic mixed-integer programs and derive central limit theorems for the objectives and optimal values. The limit theorems are based on empirical process theory and the ...
Notes on free lunch in the limit and pricing by conjugate duality theory
King and Korf introduced, in the framework of a discrete-time dynamic market model on a general probability space, a new concept of arbitrage called free lunch in the limit which is slightly weaker than the common free ...
Two-stage stochastic semidefinite programming and decomposition based interior point methods
We introduce two stage stochastic semidefinite programs with recourse and present a Benders decomposition based linearly convergent interior point algorithm to solve them. This extends the results in Zhao  wherein it ...
A Branch-Reduce-Cut Algorithm for the Global Optimization of Probabilistically Constrained Linear Programs
We consider probabilistic constrained linear programs with general distributions for the uncertain parameters. These problems generally involve non-convex feasible sets. We develop a branch and bound algorithm that searches ...
Decomposing CVaR minimization in two-stage stochastic models
Based on the polyhedral representation of Künzi-Bay and Mayer (2005), we propose a decomposition framework for the minimization of CVaR in two-stage stochastic models.We show that the decomposed problems can be effectively ...
A Comparative Study of Decomposition Algorithms for Stochastic Combinatorial Optimization
This paper presents comparative computational results using three decomposition algorithms on a battery of instances drawn from three different applications. In order to preserve the commonalities among the algorithms in ...