Selected parallel optimization methods for financial management under uncertainty

Abstract

A review of some of the most important existing parallel solution algorithms for stochastic dynamic problems arising in financial planning is the main focus of this work. Optimization remains the most difficult, time and resource consuming part of the process of decision support for financial planning under uncertainty. However, other parts of a specialized decision support system (DSS) are also briefly outlined to provide appropriate background. Finally, financial modeling is but one of the possible application fields of stochastic dynamic optimization. Therefore the same fairly general methods described here are also useful in many other contexts. Authors hope that the overview of this application field may be of interest to readers concerned with development of parallel programming paradigms, methodology and tools. Therefore special care was taken to ensure that the presentation is easily understandable without much previous knowledge of theory and methods of operations research.