Convex duality in stochastic programming and mathematical finance
Computational Finance
2010-06-28 v1 Optimization and Control
Abstract
This paper proposes a general duality framework for the problem of minimizing a convex integral functional over a space of stochastic processes adapted to a given filtration. The framework unifies many well-known duality frameworks from operations research and mathematical finance. The unification allows the extension of some useful techniques from these two fields to a much wider class of problems. In particular, combining certain finite-dimensional techniques from convex analysis with measure theoretic techniques from mathematical finance, we are able to close the duality gap in some situations where traditional topological arguments fail.
Cite
@article{arxiv.1006.4083,
title = {Convex duality in stochastic programming and mathematical finance},
author = {Teemu Pennanen},
journal= {arXiv preprint arXiv:1006.4083},
year = {2010}
}