Tuesday, 14:15 - 14:40 h, Room: MA 144


Raghu Pasupathy
On interior-point based retrospective approximation methods for solving two-stage stochastic linear programs

Coauthor: Soumyadip Ghosh


We consider two-stage stochastic linear programs, the foundational formulation for optimization under uncertainty. The most general form lets the underlying distributions have infinite support. Approximate solutions to such problems are obtained by the sample average approximation approach of solving the program for a finite sample from the distribution. A recent thread of literature focuses on using interior point methods to efficiently solve two-stage programs for finite support random variables. Our contribution generalizes this
formulation by incorporating it into a retrospective approximation (RA) framework. What results is an implementable
interior-point solution paradigm that can be used to solve general two-stage stochastic linear programs to a desirable accuracy. After discussing some basic convergence properties, we characterize the complexity of the algorithm, leading to guidance on the optimal choice of the RA framework's parameters as a function of the effort expended in solving the sub-problems and the effort expended in solving the
master problem.


Talk 3 of the invited session Tue.2.MA 144
"Topics in stochastic programming" [...]
Cluster 22
"Stochastic optimization" [...]


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