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Robust Convex Optimization
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.
Lectures on modern convex optimization - analysis, algorithms, and engineering applications
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.
Tod Morrison University of Colorado at Denver and Health Sciences Center 14.0 is celebrating its 20th anniversary with a celebration and celebration of the life of Tod Morrison, the first openly gay president of the United States.
Adjustable robust solutions of uncertain linear programs
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.
Robust solutions of uncertain linear programs
Robust solutions of Linear Programming problems contaminated with uncertain data
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.
Lectures on modern convex optimization
- A. Ben-Tal
- Computer Science
A comprehensive introduction to the subject, this book shows in detail how such problems can be solved in many different fields, and proves the vanishing of a determinant whose ...
Robust Solutions of Optimization Problems Affected by Uncertain Probabilities
- A. Ben-Tal, D. D. Hertog, A. D. Waegenaere, B. Melenberg, G. Rennen
- Computer Science, MathematicsManag. Sci.
- 25 May 2011
The robust counterpart of a linear optimization problem with φ-divergence uncertainty is tractable for most of the choices of φ typically considered in the literature and extended to problems that are nonlinear in the optimization variables.
Robust optimization – methodology and applications
The study reveals that the feasibility properties of the usual solutions of real world LPs can be severely affected by small perturbations of the data and that the RO methodology can be successfully used to overcome this phenomenon.
A sequential parametric convex approximation method with applications to nonconvex truss topology design problems
It is shown that the approximate convex problem solved at each inner iteration can be cast as a conic quadratic programming problem, hence large scale TTD problems can be efficiently solved by the proposed method.