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Aharon Ben-Tal
Researcher at Technion – Israel Institute of Technology
Publications - 182
Citations - 23354
Aharon Ben-Tal is an academic researcher from Technion – Israel Institute of Technology. The author has contributed to research in topics: Robust optimization & Convex optimization. The author has an hindex of 56, co-authored 180 publications receiving 20933 citations. Previous affiliations of Aharon Ben-Tal include University of Texas at Austin & Tilburg University.
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MonographDOI
Lectures on modern convex optimization: analysis, algorithms, and engineering applications
TL;DR: The authors present the basic theory of state-of-the-art polynomial time interior point methods for linear, conic quadratic, and semidefinite programming as well as their numerous applications in engineering.
Journal ArticleDOI
Robust Convex Optimization
Aharon Ben-Tal,Arkadi Nemirovski +1 more
TL;DR: 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 efficientalgorithms such as polynomial time interior point methods.
Journal ArticleDOI
Robust solutions of uncertain linear programs
Aharon Ben-Tal,Arkadi Nemirovski +1 more
TL;DR: It is shown that the RC of an LP with ellipsoidal uncertainty set is computationally tractable, since it leads to a conic quadratic program, which can be solved in polynomial time.
Journal ArticleDOI
Robust solutions of Linear Programming problems contaminated with uncertain data
Aharon Ben-Tal,Arkadi Nemirovski +1 more
TL;DR: The Robust Optimization methodology is applied to produce “robust” solutions of the above LPs which are in a sense immuned against uncertainty for the NETLIB problems.
Journal ArticleDOI
Adjustable robust solutions of uncertain linear programs
TL;DR: The Affinely Adjustable Robust Counterpart (AARC) problem is shown to be, in certain important cases, equivalent to a tractable optimization problem, and in other cases, having a tight approximation which is tractable.