Projection and discretization methods in stochastic programming

Aggregation and discretization in multistage stochastic programming

Multistage stochastic programs have applications in many areas and support policy makers in finding rational decisions that hedge against unforeseen negative events. In order to ensure computational tractability, continuous-state stochastic programs are usually discretized; and frequently, the curse of dimensionality dictates that decision stages must be aggregated. In this article we construct...

Semi-infinite Programming: Discretization Methods, Sip

Methods for Stochastic Trust Fund Projection∗

Current methods used for stochastic projection of the finances of the combined Old-Age and Survivors Insurance and Disability Insurance (OASDI) trust fund are described and found to have problems. Structural time series models and alternative Monte Carlo procedures are identified as new methods that may be able to solve these problems. The new methods are implemented using the productivity grow...

Dual Barrier - Projection Methods Inlinear Programming

A surjective space transformation technique is used to convert an original dual linear programming problem with equality and inequality constraints into a problem involving only equality constraints. Continuous and discrete versions of the stable gradient projection method are applied to the reduced problem. The numerical methods involve performing inverse transformations. The convergence rate ...

Stable barrier-projection and barrier-Newton methods in linear programming

The present paper is devoted to the application of the space transformation techniques for solving linear programming problems. By using a surjective mapping the original constrained optimization problem is transformed to a problem in a new space with only equality constraints. For the numerical solution of the latter problem the stable version of the gradient-projection and Newton’s methods ar...