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Convergence of stochastic average approximation for stochastic optimization problems with mixed expectation
 

Summary: Convergence of stochastic average approximation for
stochastic optimization problems with mixed expectation
and per-scenario constraints $
Mihai Anitescua,1
, John R. Birgeb,2
aArgonne National Laboratory, Mathematics and Computer Science Division, 9700 S. Cass
Avenue, Argonne, IL 60439,USA, anitescu@mcs.anl.gov
bThe University of Chicago Booth School of Business, 5807 S Woodlawn Avenue, Chicago, IL
60637, USA, John.Birge@chicagogsb.edu
Abstract
We present a framework for ensuring convergence of sample average approximations
to stochastic optimization problems that include expectation constraints in addition
to per-scenario constraints.
Key words: Sample average approximation, stochastic optimization, expectation
constraints
1. Introduction
Stochastic optimization problems with
mixed expectations and per-scenario con-
straints (SOESC) are ubiquitous in ap-
plications. As an example problem, con-

  

Source: Anitescu, Mihai - Mathematics and Computer Science Division, Argonne National Laboratory

 

Collections: Mathematics