ADAPTIVE AGGREGATION METHODS FOR DISCOUNTED DYNAMIC PROGRAMMING.

Dimitri P. Bertsekas, David A. Castanon

Research output: Contribution to journalConference articlepeer-review

4 Scopus citations

Abstract

A class of iterative aggregation algorithms for solving discounted dynamic programming problems is proposed. The idea is to interject aggregation iterations in the course of the usual successive approximation method. A feature that sets the method apart from earlier proposals is that the aggregate groups of states change adaptively from one aggregation iteration to the next, depending on the progress of the computation. This allows acceleration of convergence in difficult problems involving multiple ergodic classes for which methods using fixed groups of aggregate states are ineffective. No knowledge of special problem structure is utilized by the algorithm.

Original languageEnglish (US)
Pages (from-to)1840-1845
Number of pages6
JournalProceedings of the IEEE Conference on Decision and Control
DOIs
StatePublished - 1986
Externally publishedYes

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization

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