Oracle-based robust optimization via online learning

Aharon Ben-Tal, Elad Hazan, Tomer Koren, Shie Mannor

Research output: Contribution to journalArticlepeer-review

36 Scopus citations


Robust optimization is a common optimization framework under uncertainty when problem parameters are unknown, but it is known that they belong to some given uncertainty set. In the robust optimization framework, a min-max problem is solved wherein a solution is evaluated according to its performance on the worst possible realization of the parameters. In many cases, a straightforward solution to a robust optimization problem of a certain type requires solving an optimization problem of a more complicated type, which might be NP-hard in some cases. For example, solving a robust conic quadratic program, such as those arising in a robust support vector machine (SVM) with an ellipsoidal uncertainty set, leads in general to a semidefinite program. In this paper, we develop a method for approximately solving a robust optimization problem using tools from online convex optimization, where at every stage a standard (nonrobust) optimization program is solved. Our algorithms find an approximate robust solution using a number of calls to an oracle that solves the original (nonrobust) problem that is inversely proportional to the square of the target accuracy.

Original languageEnglish (US)
Pages (from-to)628-638
Number of pages11
JournalOperations Research
Issue number3
StatePublished - May 1 2015

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Management Science and Operations Research


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