On $${\epsilon}$$ -solutions for convex optimization problems with uncertainty data

On $${\epsilon}$$ -solutions for convex optimization problems with uncertainty data We consider $${\epsilon}$$ -solutions (approximate solutions) for a robust convex optimization problem in the face of data uncertainty. Using robust optimization approach (worst-case approach), we establish an optimality theorem and duality theorems for $${\epsilon}$$ -solutions for the robust convex optimization problem. Moreover, we give an example illustrating the duality theorems. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Positivity Springer Journals

On $${\epsilon}$$ -solutions for convex optimization problems with uncertainty data

, Volume 16 (3) – Jun 26, 2012
18 pages

/lp/springer_journal/on-epsilon-solutions-for-convex-optimization-problems-with-uncertainty-Rp5D37Ztwh
Publisher
Springer Journals
Subject
Mathematics; Operator Theory; Calculus of Variations and Optimal Control; Optimization; Econometrics; Potential Theory; Fourier Analysis
ISSN
1385-1292
eISSN
1572-9281
D.O.I.
10.1007/s11117-012-0186-4
Publisher site
See Article on Publisher Site

Abstract

We consider $${\epsilon}$$ -solutions (approximate solutions) for a robust convex optimization problem in the face of data uncertainty. Using robust optimization approach (worst-case approach), we establish an optimality theorem and duality theorems for $${\epsilon}$$ -solutions for the robust convex optimization problem. Moreover, we give an example illustrating the duality theorems.

Journal

PositivitySpringer Journals

Published: Jun 26, 2012

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