Institute of Information Theory and Automation

Publication details

Chance constrained problems: penalty reformulation and performance of sample approximation technique

Journal Article

Branda Martin

serial: Kybernetika vol.48, 1 (2012), p. 105-122

research: CEZ:AV0Z10750506

project(s): GBP402/12/G097, GA ČR

keywords: chance constrained problems, penalty functions, asymptotic equivalence, sample approximation technique, investment problem

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abstract (eng):

We explore reformulation of nonlinear stochastic programs with several joint chance constraints by stochastic programs with suitably chosen penalty-type objectives. We show that the two problems are asymptotically equivalent. Simpler cases with one chance constraint and particular penalty functions were studied in [6,11]. The obtained problems with penalties and with a fixed set of feasible solutions are simpler to solve and analyze then the chance constrained programs. We discuss solving both problems using Monte-Carlo simulation techniques for the cases when the set of feasible solution is finite or infinite bounded. The approach is applied to a financial optimization problem with Value at Risk constraint, transaction costs and integer allocations. We compare the ability to generate a feasible solution of the original chance constrained problem using the sample approximations of the chance constraints directly or via sample approximation of the penalty function objective.


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Last modification: 21.12.2012
Institute of Information Theory and Automation