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

Title:

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

Creator:

Branda, Martin

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:a6f88dfb-e68d-4241-98a0-750f1db7bee6
uuid:a6f88dfb-e68d-4241-98a0-750f1db7bee6

Subject:

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

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

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.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Coverage:

[105]-122

Source:

Kybernetika | 2012 Volume:48 | Number:1

Harvested from:

CDK

Metadata only:

false