Computational Aspects of Bayesian Solution Estimators in Stochastic Optimization

We study a class of stochastic programs where some of the elements in the objective function are random, and their probability distribution has unknown parameters. The goal is to find a good estimate for the optimal solution of the stochastic program using data sampled from the distribution of the random elements. We investigate two common … Read more

Stochastic geometric optimization with joint probabilistic constraints

This paper discusses geometric programs with joint probabilistic constraints. When the stochastic parameters are normally distributed and independent of each other, we approximate the problem by using piecewise polynomial functions with non-negative coefficients, and transform the approximation problem into a convex geometric program. We prove that this approximation method provides a lower bound. Then, we … Read more