On integer stochastic approximation

Approximation in Stochastic Integer Programming

Approximation algorithms are the prevalent solution methods in the field of stochastic programming. Problems in this field are very hard to solve. Indeed, most of the research in this field has concentrated on designing solution methods that approximate the optimal solutions. However, efficiency in the complexity theoretical sense is usually not taken into account. Quality statements mostly rem...

On Stochastic Approximation

This paper deals with a stochastic process for the approximation of the root of a regression equation. This process was first suggested. by Robbins and Monro [1]. The main result here is a necessary and sufficient condition on the iteration coefficients for convergence of the process (convergence with probability one and convergence in the quadratic mean).

Stochastic Simulation on Integer Constraint Sets

Bounds are given on the number of steps sufficient for convergence of simulation algorithms on domains of nonnegative integer constraint sets.

On Stochastic Integer Programming under Probabilistic Constraints

We consider stochastic programming problems with probabilistic constraints in volving integer valued random variables The concept of p e cient points of a prob ability distribution is used to derive various equivalent problem formulations Next we modify the concept of r concave discrete probability distributions and analyse its relevance for problems under consideration These notions are used t...

Stochastic Integer Programming Bibliography