03554nam a22005295i 4500001001800000003000900018005001700027007001500044008004100059020001800100024003100118041000800149050001600157072001700173072002300190072002300213082001200236100002700248245009400275260006100369264006100430300003400491336002600525337002600551338003600577347002400613490005700637505021000694520160000904650001702504650001602521650002402537650003902561650001602600650001702616650002402633650004902657650002502706650001602731650005102747700003502798710003402833773002002867776003602887830005702923856004402980978-3-540-46103-6DE-He21320141014114420.0cr nn 008mamaa121227s1989 gw | s |||| 0|eng d a97835404610367 a10.1007/3-540-50871-62doi aeng 4aQA297-299.4 7aPBKS2bicssc 7aMAT0210002bisacsh 7aMAT0060002bisacsh04a5182231 aTörn, Aimo.eeditor.10aGlobal Optimizationh[electronic resource] /cedited by Aimo Törn, Antanas Žilinskas. 1aBerlin, Heidelberg :bSpringer Berlin Heidelberg,c1989. 1aBerlin, Heidelberg :bSpringer Berlin Heidelberg,c1989. aXII, 260 p.bonline resource. atextbtxt2rdacontent acomputerbc2rdamedia aonline resourcebcr2rdacarrier atext filebPDF2rda1 aLecture Notes in Computer Science,x0302-9743 ;v3500 aCovering methods -- Methods of generalized descent -- Random search methods -- Clustering methods -- Methods based on statistical models of objective functions -- Miscellaneous -- Testing and applications. aGlobal optimization is concerned with finding the global extremum (maximum or minimum) of a mathematically defined function (the objective function) in some region of interest. In many practical problems it is not known whether the objective function is unimodal in this region; in many cases it has proved to be multimodal. Unsophisticated use of local optimization techniques is normally inefficient for solving such problems. Therefore, more sophisticated methods designed for global optimization, i.e. global optimization methods, are important from a practical point of view. Most methods discussed here assume that the extremum is attained in the interior of the region of interest, i.e., that the problem is essentially unconstrained. Some methods address the general constrained problem. What is excluded is the treatment of methods designed for problems with a special structure, such as quadratic programming with negatively quadratic forms. This book is the first broad treatment of global optimization with an extensive bibliography covering research done both in east and west. Different ideas and methods proposed for global optimization are classified, described and discussed. The efficiency of algorithms is compared by using both artificial test problems and some practical problems. The solutions of two practical design problems are demonstrated and several other applications are referenced. The book aims at aiding in the education, at stimulating the research in the field, and at advising practitioners in using global optimization methods for solving practical problems. 0aMathematics. 0aAlgorithms. 0aNumerical analysis. 0aDistribution (Probability theory). 0aStatistics.14aMathematics.24aNumerical Analysis.24aProbability Theory and Stochastic Processes.24aStatistics, general.24aAlgorithms.24aSystems and Information Theory in Engineering.1 aŽilinskas, Antanas.eeditor.2 aSpringerLink (Online service)0 tSpringer eBooks08iPrinted edition:z9783540508717 0aLecture Notes in Computer Science,x0302-9743 ;v35040uhttp://dx.doi.org/10.1007/3-540-50871-6