Sample-Path Solutions for Simulation Optimization Problems and Stochastic Variational Inequalities

  • Gül Gürkan
  • A. Yonca Özge
  • Stephen M. Robinson
Part of the Operations Research/Computer Science Interfaces Series book series (ORCS, volume 9)


In this paper, we give an overview of some recent developments in using simulation together with gradient estimation techniques to provide solutions for difficult stochastic optimization problems and stochastic variational inequalities. The basic idea is to observe a fixed sample path (by using the method of common random numbers from the simulation literature), solve the resulting deterministic problem using fast and effective methods from nonlinear programming, and then use the resulting solutions to infer information about the solution of the original stochastic problem. We describe these so-called sample-path methods precisely, review some conditions under which they are known to work, and comment on their potential advantages and limitations. We also illustrate some application areas in which these ideas have been successful.


Variational Inequality Option Price Infinitesimal Perturbation Analysis American Call Option Common Random Number 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media New York 1998

Authors and Affiliations

  • Gül Gürkan
    • 1
  • A. Yonca Özge
    • 2
  • Stephen M. Robinson
    • 3
  1. 1.CentER for Economic ResearchTilburg UniversityTilburgThe Netherlands
  2. 2.Information Technology LaboratoryGeneral Electric CompanyNiskayunaUSA
  3. 3.Department of Industrial EngineeringUniversity of Wisconsin-MadisonMadisonUSA

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