We examine the theoretical and numerical global convergence properties of a certain "gradient free" stochastic approximation algorithm called the "simultaneous pertu...
Dynamic optimization presents opportunities for finding run-time bottlenecks and deploying optimizations in statically compiled programs. In this paper, we discuss our current impl...
Howard Chen, Jiwei Lu, Wei-Chung Hsu, Pen-Chung Ye...
There has been a lot of recent work on Bayesian methods for reinforcement learning exhibiting near-optimal online performance. The main obstacle facing such methods is that in most...
Abstract. We define and study two versions of the bipartite matching problem in the framework of two-stage stochastic optimization with recourse. In one version the uncertainty is...
The algorithmic framework developed for improving heuristic solutions of the new version of deterministic TSP [Choi et al., 2002] is extended to the stochastic case. To verify the...