We propose an alternative approach to stochastic programming based on Monte-Carlo sampling and stochastic gradient optimization. The procedure is by essence probabilistic and the computed solution is a random variable.

In this paper we propose two algorithms for solving both unweighted and weighted constrained two-dimensional two-staged cutting stock problems. The problem is called two-staged cutting problem because each produced (sub) optimal cutting pattern is realized by using two cut-phases.

We present a new result on the asymptotic behavior of non-autonomous subgradient evolution equations.

We first study the competitivity ratio for the on-line version of the problem of finding a maximum-order induced subgraph satisfying some hereditary property, under the hypothesis that the input graph is revealed by clusters. Next, we focus ourselves on two of the most known instantiations of...

In this paper we give an overview of the mostimportant characteristics of advanced implementations of interior point methods.

Sequential meta heuristic implementations for the irregular stock-cutting problem have highlighted a number of common problems. The implementation in this paper adopts a hybrid tabu search approach that incorporates two very different optimisation routines which utilise alternative neighborhoods...

A standard approach to duality in stochastic optimization problems with constraints in L(infinite) relies upon the Yosida-Hewitt theorem. We develop an alternative technique which employs only "elementary" means. The technique is based on an e-regularization of the original problem and on...

This paper studies models where the correspondences (or functions) under consideration are never increasing (or weakly decreasing) in endogenous variables, and weakly increasing in exogenous parameters. Such models include games of strategic substitutes, and include cases where additionally,...