A fast parallel algorithm to numerically compute the Markov renewal kernel via tight lower and upper bounds /

In this dissertation, a fast numerical algorithm for computing the Markov renewal kernel via tight lower and upper bounds is developed. The Markov renewal kernel can then used to solve the well-known Markov renewal equation. Error analysis shows the numerically obtained bounds to be tight. The algor...

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Bibliographic Details
Main Author: Elkins, Debra Ann
Format: Thesis Book
Language:English
Published: [Place of publication not identified] : [publisher not identified] ; 2000.
Subjects:
Online Access:http://proxy.library.tamu.edu/login?url=http://proquest.umi.com/pqdweb?did=731990051&sid=1&Fmt=2&clientId=2945&RQT=309&VName=PQD
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Summary:In this dissertation, a fast numerical algorithm for computing the Markov renewal kernel via tight lower and upper bounds is developed. The Markov renewal kernel can then used to solve the well-known Markov renewal equation. Error analysis shows the numerically obtained bounds to be tight. The algorithm design and approximate solution process yield to a fast parallel solution methodology, which has speed-up behavior that is a linear function of the number of processors used. In particular, the parallel algorithm is of order O(k/2), when k is the number of processors used.
Item Description:Vita.
"Major Subject: Industrial Engineering".
Physical Description:viii, 86 leaves : illustrations ; 28 cm.
Issued also on microfiche from University Microfilm Inc.
Bibliography:Includes bibliographical references (leaves 81-85).