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Title: Stochastic Optimization of Complex Systems

This project focused on methodologies for the solution of stochastic optimization problems based on relaxation and penalty methods, Monte Carlo simulation, parallel processing, and inverse optimization. The main results of the project were the development of a convergent method for the solution of models that include expectation constraints as in equilibrium models, improvement of Monte Carlo convergence through the use of a new method of sample batch optimization, the development of new parallel processing methods for stochastic unit commitment models, and the development of improved methods in combination with parallel processing for incorporating automatic differentiation methods into optimization.
  1. University of Chicago
Publication Date:
OSTI Identifier:
Report Number(s):
DOE Contract Number:
Resource Type:
Technical Report
Research Org:
Univ. of Chicago, IL (United States)
Sponsoring Org:
USDOE Office of Science (SC)
Country of Publication:
United States
97 MATHEMATICS AND COMPUTING; optimization, stochastic methods, complex systems