Markov programming by successive approximations with respect to weighted supremum norms

J. Wessels

Research output: Contribution to journalArticleAcademicpeer-review

47 Citations (Scopus)

Abstract

Markovian decision processes are considered in the situation of discrete time, countable state space, and general decision space. By introducing a Banach space with a weighted supremum norm, conditions are derived, which guarantee convergence of successive approximations to the value function. These conditions are weaker then those required by the usual supnorm approach. Several properties of the successive approximations are derived.
Original languageEnglish
Pages (from-to)326-335
Number of pages10
JournalJournal of Mathematical Analysis and Applications
Volume58
Issue number2
DOIs
Publication statusPublished - 1977

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Successive Approximation
Banach spaces
Supremum
Programming
Norm
Value Function
Countable
State Space
Discrete-time
Banach space

Cite this

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Markov programming by successive approximations with respect to weighted supremum norms. / Wessels, J.

In: Journal of Mathematical Analysis and Applications, Vol. 58, No. 2, 1977, p. 326-335.

Research output: Contribution to journalArticleAcademicpeer-review

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T1 - Markov programming by successive approximations with respect to weighted supremum norms

AU - Wessels, J.

PY - 1977

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AB - Markovian decision processes are considered in the situation of discrete time, countable state space, and general decision space. By introducing a Banach space with a weighted supremum norm, conditions are derived, which guarantee convergence of successive approximations to the value function. These conditions are weaker then those required by the usual supnorm approach. Several properties of the successive approximations are derived.

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DO - 10.1016/0022-247X(77)90210-4

M3 - Article

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SP - 326

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JO - Journal of Mathematical Analysis and Applications

JF - Journal of Mathematical Analysis and Applications

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