Sequential Randomized Algorithms for Robust Convex Optimization

Sequential randomized algorithms are considered for robust convex optimization which minimizes a linear objective function subject to a parameter dependent convex constraint. Employing convex optimization and random sampling of parameter, these algorithms enable us to obtain a suboptimal solution wi...

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Vydáno v:IEEE transactions on automatic control Ročník 60; číslo 12; s. 3356 - 3361
Hlavní autoři: Wada, Takayuki, Fujisaki, Yasumasa
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.12.2015
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Convex functions
Ellipsoids
Linear programming
Optimization
Optimization algorithms
Probabilistic logic
Randomized algorithms
Robust control
Robustness
Upper bound
Optimization algorithms
robust control
randomized algorithms
ISSN:0018-9286, 1558-2523
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Shrnutí:Sequential randomized algorithms are considered for robust convex optimization which minimizes a linear objective function subject to a parameter dependent convex constraint. Employing convex optimization and random sampling of parameter, these algorithms enable us to obtain a suboptimal solution within reasonable computational time. The suboptimal solution is feasible in a probabilistic sense and the suboptimal value belongs to an interval which contains the optimal value. The maximum of the interval is the optimal value of the robust convex optimization plus a specified tolerance. On the other hand, its minimum is the optimal value of the chance constrained optimization which is a probabilistic relaxation of the robust convex optimization, with high probability.
Bibliografie:ObjectType-Article-1
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ISSN:0018-9286
1558-2523
DOI:10.1109/TAC.2015.2423871