Back to Search
Start Over
PRIMAL-DUAL ALGORITHMS FOR OPTIMIZATION WITH STOCHASTIC DOMINANCE.
- Source :
-
SIAM Journal on Optimization . 2017, Vol. 27 Issue 1, p34-66. 33p. - Publication Year :
- 2017
-
Abstract
- Stochastic dominance, a pairwise comparison between random variables, is an effective tool for expressing risk aversion in stochastic optimization. In this paper, we develop a family of primal-dual algorithms for optimization problems with stochastic dominance-constraints. First, we develop an offline primal-dual algorithm and bound its optimality gap as a function of the number of iterations. Then, we extend this algorithm to the online setting where only one random sample is given in each decision epoch. We give probabilistic bounds o n the optimality gap in this setting. This technique also yields an online algorithm for the stochastic dominance-constrained multiarmed bandit with partial feedback. The paper concludes by discussing a dual approach for a batch learning problem with robust stochastic dominance constraints. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 10526234
- Volume :
- 27
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- SIAM Journal on Optimization
- Publication Type :
- Academic Journal
- Accession number :
- 122890477
- Full Text :
- https://doi.org/10.1137/141001251