Tractable stochastic analysis in high dimensions via robust optimization

Chaithanya Bandi, Dimitris Bertsimas*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

92 Scopus citations


Modern probability theory, whose foundation is based on the axioms set forth byKolmogorov, is currently themajor tool for performance analysis in stochastic systems. While it offers insights in understanding such systems, probability theory, in contrast to optimization, has not been developed with computational tractability as an objective when the dimension increases. Correspondingly, some of its major areas of application remain unsolved when the underlying systems become multidimensional: Queueing networks, auction design in multi-item, multi-bidder auctions, network information theory, pricingmulti-dimensional options, among others.We propose a new approach to analyze stochastic systems based on robust optimization. The key idea is to replace the Kolmogorov axioms and the concept of random variables as primitives of probability theory, with uncertainty sets that are derived from some of the asymptotic implications of probability theory like the central limit theorem. In addition, we observe that several desired system properties such as incentive compatibility and individual rationality in auction design are naturally expressed in the language of robust optimization. In this way, the performance analysis questions become highly structured optimization problems (linear, semidefinite, mixed integer) for which there exist efficient, practical algorithms that are capable of solving problems in high dimensions. We demonstrate that the proposed approach achieves computationally tractable methods for (a) analyzing queueing networks, (b) designing multi-item, multi-bidder auctions with budget constraints, and (c) pricing multi-dimensional options.

Original languageEnglish (US)
Pages (from-to)23-70
Number of pages48
JournalMathematical Programming
Issue number1
StatePublished - Aug 2012


  • Mechanism design
  • Option pricing
  • Queueing
  • Robust optimization
  • Stochastic analysis

ASJC Scopus subject areas

  • Software
  • Mathematics(all)


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