Approximate linear programming for average-cost dynamic programming

Advances in Neural Information Processing Systems

Volumn , Issue , 2003, Pages

  De Farias, Daniela Pucci ^a   Van Roy, Benjamin ^b  

^a IBM ALMADEN RESEARCH CENTER   (United States)

^b Stanford University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATION ALGORITHMS; COST FUNCTIONS; COSTS; LINEAR PROGRAMMING; OPTIMIZATION;

APPROXIMATE LINEAR PROGRAMMING; AVERAGE-COST CRITERIA; DIFFERENTIAL COSTS; EXTERNAL CONTROL; OPTIMIZING PERFORMANCE; PERFORMANCE BOUNDS; PROVIDE GUIDANCES; RELATIVE ACCURACY;

COST BENEFIT ANALYSIS;

EID: 84898987009     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (11)

1. D. Adelman. A price-directed approach to stochastic inventory/routing. Preprint, 2002.
2. D. Adelman. Price-directed replenishment of subsets: Methodology and its application to inventory routing. Preprint, 2002.
3. D. Bertsekas. Dynamic Programming and Optimal Control. Athena Scientific, 1995.
4. D. Bertsekas and J.N. Tsitsiklis. Neuro-Dynamic Programming. Athena Scientific, 1996.
5. D. Bertsimas, D. Gamarnik, and J.N. Tsitsiklis. Performance of multiclass Markovian queueing networks via piecewise linear Lyapunov functions. Annals of Applied Probability, 11.
6. D.P. de Farias and B. Van Roy. The linear programming approach to approximate dynamic programming. To appear in Operations Research, 2001.
7. D.P. de Farias and B. Van Roy. On constraint sampling in the linear programming approach to approximate dynamic programming. Conditionally accepted to Mathematics of Operations Research, 2001.
8. C. Guestrin, D. Koller, and R. Parr. Efficient solution algorithms for factored MDPs. Submitted to Journal of Artificial Intelligence Research, 2001.
9. A.S. Manne. Linear programming and sequential decisions. Management Science, 6(3): 259-267, 1960.
10. J.R. Morrison and PR. Kumar. New linear program performance bounds for queueing networks. Journal of Optimization Theory and Applications, 100(3): 575-597, 1999.
11. P. Schweitzer and A. Seidmann. Generalized polynomial approximations in Markovian decision processes. Journal of Mathematical Analysis and Applications, 110: 568-582, 1985.