Discounted dynamic programming on Euclidean spaces
A. Nowak (1979)
Applicationes Mathematicae
Similarity:
The search session has expired. Please query the service again.
The search session has expired. Please query the service again.
The search session has expired. Please query the service again.
The search session has expired. Please query the service again.
The search session has expired. Please query the service again.
The search session has expired. Please query the service again.
The search session has expired. Please query the service again.
The search session has expired. Please query the service again.
The search session has expired. Please query the service again.
The search session has expired. Please query the service again.
The search session has expired. Please query the service again.
The search session has expired. Please query the service again.
The search session has expired. Please query the service again.
The search session has expired. Please query the service again.
A. Nowak (1979)
Applicationes Mathematicae
Similarity:
H.P. Künzi (1962)
Metrika
Similarity:
(2006)
Control and Cybernetics
Similarity:
Regina Hildenbrandt (2003)
Discussiones Mathematicae Probability and Statistics
Similarity:
Several peculiarities of stochastic dynamic programming problems where random vectors are observed before the decision ismade at each stage are discussed in the first part of this paper. Surrogate problems are given for such problems with distance properties (for instance, transportation problems) in the second part.
Gerhard Hübner (1983)
Acta Universitatis Carolinae. Mathematica et Physica
Similarity:
A. Nowak (1977)
Colloquium Mathematicae
Similarity:
Gary J. Koehler (1979)
RAIRO - Operations Research - Recherche Opérationnelle
Similarity:
Karel Sladký (2018)
Kybernetika
Similarity:
In this note attention is focused on finding policies optimizing risk-sensitive optimality criteria in Markov decision chains. To this end we assume that the total reward generated by the Markov process is evaluated by an exponential utility function with a given risk-sensitive coefficient. The ratio of the first two moments depends on the value of the risk-sensitive coefficient; if the risk-sensitive coefficient is equal to zero we speak on risk-neutral models. Observe that the first...
G. Cioni, A. Miola (1988)
Banach Center Publications
Similarity:
Karel Sladký (2017)
Kybernetika
Similarity:
The article is devoted to Markov reward chains in discrete-time setting with finite state spaces. Unfortunately, the usual optimization criteria examined in the literature on Markov decision chains, such as a total discounted, total reward up to reaching some specific state (called the first passage models) or mean (average) reward optimality, may be quite insufficient to characterize the problem from the point of a decision maker. To this end it seems that it may be preferable if not...
Sebastian Sitarz (2012)
RAIRO - Operations Research - Recherche Opérationnelle
Similarity:
The paper focuses on multi-criteria problems. It presents the interactive compromise hypersphere method with sensitivity analysis as a decision tool in multi-objective programming problems. The method is based on finding a hypersphere (in the criteria space) which is closest to the set of chosen nondominated solutions. The proposed modifications of the compromise hypersphere method are based on using various metrics and analyzing their influence on the original method. Applications of...
Sebastian Sitarz (2012)
RAIRO - Operations Research
Similarity:
The paper focuses on multi-criteria problems. It presents the interactive compromise hypersphere method with sensitivity analysis as a decision tool in multi-objective programming problems. The method is based on finding a hypersphere (in the criteria space) which is closest to the set of chosen nondominated solutions. The proposed modifications of the compromise hypersphere method are based on using various metrics and analyzing their influence on the original method. Applications of...