000			03334nam a22004335i 4500
999			_c387521 _d387521
001			387521
003			ES-MaUEC
005			20230419113315.0
006			a||||fo|||| 00| 0
007			cr nn 008mamaa
008			230419s2014 sz | s |||| 0|eng d
020			_a9783031024061
024	7		_a10.1007/978-3-031-02406-1 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA331.5 _b2014 EB
100	1		_aMordukhovich, B. Sh. _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9688147 _q(Boris Sholimovich)
245	1	3	_aAn Easy Path to Convex Analysis and Applications _cby Boris Mordukhovich, Nguyen Mau
250			_a1st edition 2014
264		1	_aCham _bSpringer International Publishing _c2014
300			_a1 recurso en línea (XVI, 202 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSynthesis Lectures on Mathematics & Statistics _x1938-1751
505	0		_aPreface -- Acknowledgments -- List of Symbols -- Convex Sets and Functions -- Subdifferential Calculus -- Remarkable Consequences of Convexity -- Applications to Optimization and Location Problems -- Solutions and Hints for Exercises -- Bibliography -- Authors' Biographies -- Index .
520			_aConvex optimization has an increasing impact on many areas of mathematics, applied sciences, and practical applications. It is now being taught at many universities and being used by researchers of different fields. As convex analysis is the mathematical foundation for convex optimization, having deep knowledge of convex analysis helps students and researchers apply its tools more effectively. The main goal of this book is to provide an easy access to the most fundamental parts of convex analysis and its applications to optimization. Modern techniques of variational analysis are employed to clarify and simplify some basic proofs in convex analysis and build the theory of generalized differentiation for convex functions and sets in finite dimensions. We also present new applications of convex analysis to location problems in connection with many interesting geometric problems such as the Fermat-Torricelli problem, the Heron problem, the Sylvester problem, and their generalizations. Of course, we do not expect to touch every aspect of convex analysis, but the book consists of sufficient material for a first course on this subject. It can also serve as supplemental reading material for a course on convex optimization and applications.
988			_aSynthesis Collection of Technology_2014
650		7	_2embne _9138443 _aAnálisis matemático
650		7	_2embne _9140803 _aFunciones de variable compleja
650		7	_2embne _9145705 _aOptimización matemática
700	1		_aNguyen, Mau Nam _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9688148
776	0	8	_iPrinted edition: _z9783031012785
776	0	8	_iPrinted edition: _z9783031035340
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-02406-1 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b04/2023 _da _eIG _zSI