Random Multi-Constraint Projection: Stochastic Gradient Methods for Convex Optimization with Many Constraints

Consider convex optimization problems subject to a large number of constraints. We focus on stochastic problems in which the objective takes the form of expected values and the feasible set is the intersection of a large number of convex sets. We propose a class of algorithms that perform both stochastic gradient descent and random feasibility … Read more

Efficient Algorithms for Large Scale Global Optimization: Lennard-Jones clusters

A standard stochastic global optimization method is applied to the challenging problem of finding the minimum energy conformation of cluster of identical atoms interacting through the Lennard-Jones potential. The method proposed is based on the use of a two-phase local search procedure which is capable of significantly enlarge the basin of attraction of the global … Read more