Asymptotic results of Stochastic Decomposition for Two-stage Stochastic Quadratic Programming

This paper presents stochastic decomposition (SD) algorithms for two classes of stochastic programming problems: 1) two-stage stochastic quadratic-linear programming (SQLP) in which a quadratic program defines the objective function in the first stage and a linear program defines the value function in the second stage; 2) two-stage stochastic quadratic-quadratic programming (SQQP) which has quadratic programming … Read more

Stopping Rules for Box-Constrained Stochastic Global Optimization

We present three new stopping rules for Multistart based methods. The first uses a device that enables the determination of the coverage of the bounded search domain. The second is based on the comparison of asymptotic expectation values of observable quantities to the actually measured ones. The third offers a probabilistic estimate for the number … Read more

Gradient-Controlled, Typical-Distance Clustering for Global Optimization

We present a stochastic global optimization method that employs a clustering technique which is based on a typical distance and a gradient test. The method aims to recover all the local minima inside a rectangular domain. A new stopping rule is used. Comparative results on a set of test functions are reported. Citation Preprint, no … Read more