Grundlagen f�r eine Theorie des Jacobischen Kettenbruchalgorithmus

Abstract

Die beiden zuvor angegebenen Methoden erhglt man hieraus durch Spezialisierung, indem man in jeder Zeile s~imtliche b~(') bis auf ein besfimmtes unter ihnen gleieh Null setzt. Bei diesem formalen Ansatz liegt es nun aber nahe~ die bt(') in anderer Weise zu w~hlen, ngmlich b~ ~ls grS~tes Ganzes yon _R0 sodann bs als grSBtes Ganzes yon y,-b~yt usw., ebenso y~ ~ y, b t' als grSl~tes Oanzes yon Y'-' y,, bs als gr~l~tes Ganzes yon yl-ysb' y, usw., wodureh die weitere Wahl der b,(') gen~igend ersichtlieh sein d~rfte. Ieh win nun zeigen, dab das System (/~), obwohl es in dieser Form viel allgemeiner erseheint als (a), sich doch durch eine bloBe);.nderung der Bezeichnung mit letzterem zur Deckung bringen Igi~t. Zu dem Ende setze man Yo-bxYt-b~ y~ ..

A Fuzzy Integrated Approach for Evaluating Third-Party Logistics

“…With respect to the position of alternatives in this route, they should be prioritized. For instance, a directed Hamilton route such as (3,1,2,4,5,6) shows number 3 is the best alternative while number 6 has the least priority. This approach has some difficulties, because in general, there are several Hamilton routes.…”

“…If matrix A is initial then based on Perron Frobenius theorem [5], [6] the absolute highest eigenvalue will be a positive number such as r and therefore we have [8]; (2) B. ABC Analysis There are many methods for inventory management. One the most popular methods is the ABC inventory classification.…”

“…[13] III. THE PROPOSED APPROACH Our model is based on Perron Frobenius [5], [6] and the ABC Inventory classification and includes four steps:…”

“…Furthermore the proposed method is examined using a real case study. We also applied Perron Frobenius theoremdeveloped firstly by Perron [5] for a matrix with positive entries and then extended by Frobenius [6] for a non-negative case. The theorem measures eigenvectors and eigenvalues of a non-negative entry matrix.…”

“…In the actual world, matrixes are large and complex, and this leads to some numerical problems and errors during calculating their eigenvalues. Among various tools for measuring eigenvalues, the Perron Frobenius theorem [5,6] does not produce these errors. It is useful in producing some eigenvalue features of non-negative entries' matrixes.…”