stochastic algorithms

A Stochastic Proximal-Gradient Method for Regularized Generally Constrained Continuous Optimization

Categories Constrained Nonlinear Optimization, Nonlinear Optimization, Nonsmooth Optimization, Stochastic Programming Tags , , , ,

A stochastic proximal-gradient method is proposed and analyzed for solving regularized optimization problems subject to nonlinear equality constraints and nonnegativity bounds. The objective function’s values and gradients are available only through inexact stochastic oracles. The method decomposes each trial step into one component that reduces linearized infeasibility and a second component that seeks optimality while … Read more

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

Categories Convex and Nonsmooth Optimization Tags , , ,

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

Categories Chemical Engineering, Stochastic Approaches Tags , , , , ,

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