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On the computational complexity and generalization properties of multi-stage and stage-wise coupled scenario programs
Affiliation:1. Automatic Control Laboratory, Department of Information Technology and Electrical Engineering, ETH Zurich, Switzerland;2. Control Group, Department of Engineering Science, University of Oxford, United Kingdom;1. Mathematics Department, American University of Beirut, Riad el Solh 11-0236, Beirut, Lebanon;2. Department of Mathematics, University of South Carolina, Columbia, SC 29208, United States;1. Department of Physics, Fatima Mata National College, Kollam, Kerala, India;2. Department of Studies in Chemistry, University of Mysore, Manasagangotri, Mysore 570006, India;3. Department of Physics, TKM College of Arts and Science, Kollam, Kerala, India;4. R.L. Fine Chem., Bangalore 560 064, India;5. Department of Chemistry, King Fahd University of Petroleum and Minerals, Dahran 31261, Saudi Arabia;6. University of Antwerp, Chemistry Department, Universiteitsplein 1, B2610 Antwerp, Belgium;7. Department of Chemistry, HSG University, Sagar, M.P. 470003, India;1. Performance Engineering Laboratory, Dublin City University, Ireland;2. National College of Ireland, Dublin, Ireland
Abstract:We discuss the computational complexity and feasibility properties of scenario sampling techniques for uncertain optimization programs. We propose an alternative way of dealing with a special class of stage-wise coupled programs and compare it with existing methods in the literature in terms of feasibility and computational complexity. We identify trade-offs between different methods depending on the problem structure and the desired probability of constraint satisfaction. To illustrate our results, an example from the area of approximate dynamic programming is considered.
Keywords:Scenario approach  Randomized optimization  Uncertain systems  Approximate dynamic programming
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