An Adaptive Gradient Sampling Algorithm for Nonsmooth Optimization

We present an algorithm for the minimization of f : Rn → R, assumed to be locally Lipschitz and continuously differentiable in an open dense subset D of Rn. The objective f may be non-smooth and/or non-convex. The method is based on the gradient sampling (GS) algorithm of Burke et al. [A robust gradient sampling … Read more

A decomposition-based warm-start method for stochastic programming

In this paper we propose a warm-start technique for interior point methods applicable to multi-stage stochastic programming problems. The main idea is to generate an initial point for the interior point solver by decomposing the barrier problem associated with the deterministic equivalent at the sec- ond stage and using a concatenation of the solutions of … Read more

A multi-step interior point warm-start approach for large-scale stochastic linear programming

Interior point methods (IPM) have been recognised as an efficient approach for the solution of large scale stochastic programming problems due to their ability of exploiting the block-angular structure of the augmented system particular to this problem class. Stochastic programming problems, however, have exploitable structure beyond the simple matrix shape: namely the scenarios are typically … Read more

Rapidly Solving an Online Sequence of Maximum Flow Problems

We investigate how to rapidly solve an online sequence of maximum flow problems. Sequences of maximum flow problems arise in a diverse collection of settings, including stochastic network programming and real-time scheduling of jobs on a two-processor computer. In this paper, we formulate solving an online sequence of maximum flow problems as the Maximum Flow … Read more