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Robust Convex Optimization

Ben-Tal, A., Nemirovski, A.·1998·Mathematics of Operations Research

Abstract

We study convex optimization problems for which the data is not specified exactly and it is only known to belong to a given uncertainty set U, yet the constraints must hold for all possible values of the data from U. The ensuing optimization problem is called robust optimization. In this paper we lay the foundation of robust convex optimization. In the main part of the paper we show that if U is an ellipsoidal uncertainty set, then for some of the most important generic convex optimization problems (linear programming, quadratically constrained programming, semidefinite programming and others) the corresponding robust convex program is either exactly, or approximately, a tractable problem which lends itself to efficient algorithms such as polynomial time interior point methods.

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Metadata

Authors
Ben-Tal, A., & Nemirovski, A.
Venue
Mathematics of Operations Research
Published
1998-11
Volume
23
Issue
4
Pages
769-805
ISSN
  • 0364-765X (ISSN-L)
  • 1526-5471
OpenAlex
W2089105401
Cited by
2,598
References
0
FWCI
8.96
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Robust Convex Optimization. https://app.cochat.ai/papers/10.1287/moor.23.4.769
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Ben-Tal, A., & Nemirovski, A. (1998). Robust Convex Optimization. Mathematics of Operations Research. https://doi.org/10.1287/moor.23.4.769

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