東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Some Results on a Class of Functiona...

Dewhurst, David Rushing.

Linked to FindBook

Google Book

Amazon

博客來

Some Results on a Class of Functional Optimization Problems.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Some Results on a Class of Functional Optimization Problems./
Author:	Dewhurst, David Rushing.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
Description:	60 p.
Notes:	Source: Masters Abstracts International, Volume: 57-05.
Contained By:	Masters Abstracts International57-05(E).
Subject:	Mathematics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10786670
ISBN:	9780355832488

Some Results on a Class of Functional Optimization Problems.
Dewhurst, David Rushing.

Some Results on a Class of Functional Optimization Problems. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 60 p.

Source: Masters Abstracts International, Volume: 57-05.

Thesis (M.S.)--The University of Vermont and State Agricultural College, 2018.

We first describe a general class of optimization problems that describe many natu- ral, economic, and statistical phenomena. After noting the existence of a conserved quantity in a transformed coordinate system, we outline several instances of these problems in statistical physics, facility allocation, and machine learning. A dynamic description and statement of a partial inverse problem follow. When attempting to optimize the state of a system governed by the generalized equipartitioning princi- ple, it is vital to understand the nature of the governing probability distribution. We show that optimiziation for the incorrect probability distribution can have catas- trophic results, e.g., infinite expected cost, and describe a method for continuous Bayesian update of the posterior predictive distribution when it is stationary. We also introduce and prove convergence properties of a time-dependent nonparametric kernel density estimate (KDE) for use in predicting distributions over paths. Finally, we extend the theory to the case of networks, in which an event probability density is defined over nodes and edges and a system resource is to be partitioning among the nodes and edges as well. We close by giving an example of the theory's application by considering a model of risk propagation on a power grid.

ISBN: 9780355832488Subjects--Topical Terms:

515831
Mathematics.

Some Results on a Class of Functional Optimization Problems.
LDR:02295nmm a2200313 4500 001 2201635
005 20190429091134.5
008 201008s2018 ||||||||||||||||| ||eng d
020 $a 9780355832488
035 $a (MiAaPQ)AAI10786670
035 $a (MiAaPQ)uvm:10688
035 $a AAI10786670
040 $a MiAaPQ $c MiAaPQ
100 1 $a Dewhurst, David Rushing. $3 3428355
245 1 0 $a Some Results on a Class of Functional Optimization Problems.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2018
300 $a 60 p.
500 $a Source: Masters Abstracts International, Volume: 57-05.
500 $a Advisers: William A. Gibson; Peter S. Dodds.
502 $a Thesis (M.S.)--The University of Vermont and State Agricultural College, 2018.
520 $a We first describe a general class of optimization problems that describe many natu- ral, economic, and statistical phenomena. After noting the existence of a conserved quantity in a transformed coordinate system, we outline several instances of these problems in statistical physics, facility allocation, and machine learning. A dynamic description and statement of a partial inverse problem follow. When attempting to optimize the state of a system governed by the generalized equipartitioning princi- ple, it is vital to understand the nature of the governing probability distribution. We show that optimiziation for the incorrect probability distribution can have catas- trophic results, e.g., infinite expected cost, and describe a method for continuous Bayesian update of the posterior predictive distribution when it is stationary. We also introduce and prove convergence properties of a time-dependent nonparametric kernel density estimate (KDE) for use in predicting distributions over paths. Finally, we extend the theory to the case of networks, in which an event probability density is defined over nodes and edges and a system resource is to be partitioning among the nodes and edges as well. We close by giving an example of the theory's application by considering a model of risk propagation on a power grid.
590 $a School code: 0243.
650 4 $a Mathematics. $3 515831
650 4 $a Physics. $3 516296
650 4 $a Economic theory. $3 1556984
690 $a 0405
690 $a 0605
690 $a 0511
710 2 $a The University of Vermont and State Agricultural College. $b Mathematics. $3 3170225
773 0 $t Masters Abstracts International $g 57-05(E).
790 $a 0243
791 $a M.S.
792 $a 2018
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10786670