Abstracts
Abstract
We show how a technique from signal processing known as zero-delay convolution can be used to develop more efficient dynamic programming algorithms for a broad class of stochastic optimization problems. This class includes several variants of discrete stochastic shortest path, scheduling, and knapsack problems, all of which involve making a series of decisions over time that have stochastic consequences in terms of the temporal delay between successive decisions. We also correct a flaw in the original analysis [8] of the zero-delay convolution algorithm.
Keywords:
- dynamic programming,
- stochastic dynamic programming,
- convolution
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