Parallel Algorithms for Large-Scale Stochastic Programming

Stochastic programs address decision problems under uncertainty in diverse fields. The need to account for many contingencies in practical applications leads to very large optimization programs. Efficient parallel algorithms that can exploit the capabilities of modern multiprocessor computers become imperative. This chapter surveys the state-of-the-art in parallel algorithms for stochastic programming. Algorithms are reviewed, classified and compared; their potential and limitations are discussed and promising research directions are pointed out. Qualitative comparisons are based on applicability, ease of implementation, robustness and reliability of each algorithm, while quantitative comparisons are based on the computational performance of algorithmic implementations on multiprocessor systems. Emphasis is placed on the capabilities of parallel algorithms to solve large-scale stochastic programs.

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