On Parallel Processors Design for Solving Stochastics Programs

A design based on parallel processing is laid out for solving (multistage) stochastic programs. Because of the very special nature of the decomposition used here, one could rely on hard-wired micro-processors that would be extremely simple in design and fabrication, and would reduce the time required to solving stochastic programs to that needed for solving deterministic linear programs of the same size (ignoring the time required to design the parallel decomposition)

Modeling and Solution Strategies for Unconstrained Stochastic Optimization Problems

We review some modeling alternatives for handling risk in decision making processes for unconstrained stochastic optimization problems. Solution strategies are discussed and compared

Constrained Estimation: Consistency and Asymptotics

We review some of the recent results obtained for constrained estimation, involving possibly nondifferentiable criterion functions. New tools are required to push consistency and asymptotic results beyond those that can be reached by classical means

On the Continuity of the Value of a Linear Program

Results about the continuity of the value of a linear program are reviewed. Particular attention is paid to the interconnection between various sufficient conditions

Quantitative Stability of Variational Systems: I. The Epigraphical Distance

This paper proposes a global measure for the distance between the elements of a variational system (parametrized families of optimization problems)

Existence Results and Finite Horizon Approximates for Infinite Horizon Optimization Problems

The paper deals with infinite horizon optimization problems. The existence of optimal solutions is obtained as a consequence of an asymptotic growth condition. We also exhibit finite horizon approximates that yield upper and lower bounds for the optimal values and whose optimal solutions converge to the long-term optimal trajectories

Stochastic Dynamic Optimization Approaches and Computation

This description of stochastic dynamical optimization models is intended to exhibit some of the connections between various formulations that have appeared in the literature, and indicate some of the difficulties that must be overcome when trying to adapt solution methods that have been successfully applied to one class of problems to an apparently related but different class of problems. The emphasis is on solvable models. The authors begin with the least dynamical versions of stochastic optimization models, one- and two-stage models then consider discrete time models, and conclude with continuous time models