Asymptotic theory for solutions in statistical estimation and stochastic programming
Mathematics of Operations Research
Retrospective approximation algorithms for stochastic root finding
WSC '94 Proceedings of the 26th conference on Winter simulation
Single-Period Multiproduct Inventory Models with Substitution
Operations Research
Convex Optimization
Retrospective-approximation algorithms for the multidimensional stochastic root-finding problem
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Optimal Allocation of Surgery Blocks to Operating Rooms Under Uncertainty
Operations Research
A Sequential Sampling Procedure for Stochastic Programming
Operations Research
An introspective on the retrospective-approximation paradigm
Proceedings of the Winter Simulation Conference
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In a recent paper, Gongyun Zhao introduced what appears to be the first interior point formulation for solving two-stage stochastic linear programs for finite support random variables. In this paper, we generalize Gongyun Zhao's formulation by incorporating it into a retrospective approximation framework. What results is an implementable interior-point solution paradigm that can be used to solve general two-stage stochastic linear programs. After discussing some basic properties, we characterize the complexity of the algorithm, leading to guidance on the number of samples that should be generated to construct the sub-problem linear programs, effort expended in solving the sub-problems, and the effort expended in solving the master problem.