東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Adaptive online optimization of Mark...

Campos-Nanez, Enrique.

FindBook

Google Book

Amazon

博客來

Adaptive online optimization of Markov reward processes with application to pricing of multiclass loss network services.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Adaptive online optimization of Markov reward processes with application to pricing of multiclass loss network services./
作者:	Campos-Nanez, Enrique.
面頁冊數:	148 p.
附註:	Source: Dissertation Abstracts International, Volume: 64-03, Section: B, page: 1468.
Contained By:	Dissertation Abstracts International64-03B.
標題:	Engineering, System Science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3083136

Adaptive online optimization of Markov reward processes with application to pricing of multiclass loss network services.
Campos-Nanez, Enrique.

Adaptive online optimization of Markov reward processes with application to pricing of multiclass loss network services. - 148 p.

Source: Dissertation Abstracts International, Volume: 64-03, Section: B, page: 1468.

Thesis (Ph.D.)--University of Virginia, 2003.

This work studies the problem of adaptive online optimization of Markov reward processes. The problem at hand is the following: given a Markov chain whose transition probability matrix and its expected cost per stage are functions of a (1) a set of tunable parameters, and (2) a set of unknown but fixed parameters, find the set of (tunable) parameters that maximizes the average reward per stage observed. This work introduces techniques that improve the performance of existing simulation-based methods, and that are robust to uncertainty of the system parameters. We show the almost sure convergence of the algorithms to locally optimal values, including the adaptive case, while the tracking ability of the adaptive algorithm is illustrated numerically.Subjects--Topical Terms:

1018128
Engineering, System Science.

Adaptive online optimization of Markov reward processes with application to pricing of multiclass loss network services.
LDR:02375nmm 2200289 4500 001 1854518
005 20040615083618.5
008 130614s2003 eng d
035 $a (UnM)AAI3083136
035 $a AAI3083136
040 $a UnM $c UnM
100 1 $a Campos-Nanez, Enrique. $3 1942354
245 1 0 $a Adaptive online optimization of Markov reward processes with application to pricing of multiclass loss network services.
300 $a 148 p.
500 $a Source: Dissertation Abstracts International, Volume: 64-03, Section: B, page: 1468.
500 $a Adviser: Stephen D. Patek.
502 $a Thesis (Ph.D.)--University of Virginia, 2003.
520 $a This work studies the problem of adaptive online optimization of Markov reward processes. The problem at hand is the following: given a Markov chain whose transition probability matrix and its expected cost per stage are functions of a (1) a set of tunable parameters, and (2) a set of unknown but fixed parameters, find the set of (tunable) parameters that maximizes the average reward per stage observed. This work introduces techniques that improve the performance of existing simulation-based methods, and that are robust to uncertainty of the system parameters. We show the almost sure convergence of the algorithms to locally optimal values, including the adaptive case, while the tracking ability of the adaptive algorithm is illustrated numerically.
520 $a The methodological work in online methods is applied to a significant optimization problem, namely the problem of setting prices for services in a multiclass loss networks. Such networks consists of a set of resources shared by multiple classes of users characterized by their usage patterns. The network sets the price per-call/per-class and it is assumed that users are sensitive to prices, in the sense that prices affect the arrival process. The algorithms developed here are applied to the solution to this problem. The tracking ability of the algorithms is illustrated by scenarios where the service time parameters change smoothly, or infrequently, over time.
590 $a School code: 0246.
650 4 $a Engineering, System Science. $3 1018128
650 4 $a Operations Research. $3 626629
650 4 $a Computer Science. $3 626642
690 $a 0790
690 $a 0796
690 $a 0984
710 2 0 $a University of Virginia. $3 645578
773 0 $t Dissertation Abstracts International $g 64-03B.
790 1 0 $a Patek, Stephen D., $e advisor
790 $a 0246
791 $a Ph.D.
792 $a 2003
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3083136