### Daniel Kuhn (Imperial College)

#### Convergent bounds for stochastic programs with expected value constraints

*Joint work with Panos Parpas and Berc Rustem.*

*Tuesday 27 November 2007 at 11.30, JCMB 5325*

##### Abstract

This talk elaborates an approximation scheme
for convex multistage stochastic programs (MSP) with expected value
constraints. The considered problem class thus comprises models with
integrated chance constraints and CVaR constraints. We develop two
computationally tractable approximate problems that provide bounds on
the (untractable) original problem, and we show that the gap between
the bounds can be made small. The solutions of the approximate MSPs
give rise to a feasible policy for the original MSP, and this
policy's optimality gap is shown to be smaller than the
difference of the bounds. Furthermore, we propose a threshold
accepting algorithm that attempts to find the most accurate
discretization among all discretizations of a given complexity. Our
approach is illustrated on a portfolio optimization problem.

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