02907nam a22004575i 4500001001800000003000900018005001700027007001500044008004100059020001800100020001900118024002500137040000900162082001400171100002800185245018200213264003800395300003300433336002600466337002600492338003900518347002400557490004300581505020900624520115100833650001701984650001602001650002402017650003102041650002502072650001702097650001802114650001602132650005102148650002402199710003402223773002002257776003602277830004302313856009302356978-0-387-24349-8DE-He21320260521091831.0cr nn 008mamaa100301s2005    xxu|    s    |||| 0|eng d  a9780387243498  a997803872434987 a10.1007/b1052002doi  cCICY04a519.62231 aSnyman, Jan A.eauthor.10aPractical Mathematical Optimizationh[recurso electrónico] :bAn Introduction to Basic Optimization Theory and Classical and New Gradient-Based Algorithms /cby Jan A. Snyman. 1aBoston, MA :bSpringer US,c2005.  aXX, 258 p.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  arecurso en líneabcr2rdacarrier  atext filebPDF2rda1 aApplied Optimization,x1384-6485 ;v970 aLine Search Descent Methods for Unconstrained Minimization -- Standard Methods for Constrained Optimization -- New Gradient-Based Trajectory and Approximation Methods -- Example Problems -- Some Theorems.  aThis book presents basic optimization principles and gradient-based algorithms to a general audience, in a brief and easy-to-read form without neglecting rigour. The work should enable the professional to apply optimization theory and algorithms to his own particular practical field of interest, be it engineering, physics, chemistry, or business economics. Most importantly, for the first time in a relatively brief and introductory work, due attention is paid to the difficulties-such as noise, discontinuities, expense of function evaluations, and the existence of multiple minima-that often unnecessarily inhibit the use of gradient-based methods. In a separate chapter on new gradient-based methods developed by the author and his coworkers, it is shown how these difficulties may be overcome without losing the desirable features of classical gradient-based methods. Audience It is intended that this book be used in senior- to graduate-level semester courses in optimization, as offered in mathematics, engineering, computer science, and operations research departments, and also to be useful to practising professionals in the workplace. 0aMATHEMATICS. 0aALGORITHMS. 0aNUMERICAL ANALYSIS. 0aMATHEMATICAL OPTIMIZATION. 0aOPERATIONS RESEARCH.14aMATHEMATICS.24aOPTIMIZATION.24aALGORITHMS.24aOPERATIONS RESEARCH, MATHEMATICAL PROGRAMMING.24aNUMERICAL ANALYSIS.2 aSpringerLink (Online service)0 tSpringer eBooks08iPrinted edition:z9780387243481 0aApplied Optimization,x1384-6485 ;v9740uhttp://dx.doi.org/10.1007/b105200zVer el texto completo en las instalaciones del CICY