000			03170nam a22003735i 4500
001			298851
003			MX-SnUAN
005			20170705134244.0
007			cr nn 008mamaa
008			150903s2009 gw | o |||| 0|eng d
020			_a9783540856344 _99783540856344
024	7		_a10.1007/9783540856344 _2doi
035			_avtls000352089
039		9	_a201509030933 _bVLOAD _c201405060300 _dVLOAD _y201402171152 _zstaff
040			_aMX-SnUAN _bspa _cMX-SnUAN _erda
050		4	_aQA315-316
100	1		_aPytlak, Rados?aw. _eautor _9335460
245	1	0	_aConjugate Gradient Algorithms in Nonconvex Optimization / _cby Rados?aw Pytlak.
264		1	_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg, _c2009.
300			_axxvI, 477 páginas 95 ilustraciones _brecurso en línea.
336			_atexto _btxt _2rdacontent
337			_acomputadora _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_aarchivo de texto _bPDF _2rda
490	0		_aNonconvex Optimization and Its Applications, _x1571-568X ; _v89
500			_aSpringer eBooks
505	0		_aConjugate Direction Methods for Quadratic Problems -- Conjugate Gradient Methods for Nonconvex Problems -- Memoryless Quasi-Newton Methods -- Preconditioned Conjugate Gradient Algorithms -- Limited Memory Quasi-Newton Algorithms -- The Method of Shortest Residuals and Nondifferentiable Optimization -- The Method of Shortest Residuals for Differentiable Problems -- The Preconditioned Shortest Residuals Algorithm -- Optimization on a Polyhedron -- Conjugate Gradient Algorithms for Problems with Box Constraints -- Preconditioned Conjugate Gradient Algorithms for Problems with Box Constraints -- Preconditioned Conjugate Gradient Based Reduced-Hessian Methods.
520			_aThis up-to-date book is on algorithms for large-scale unconstrained and bound constrained optimization. Optimization techniques are shown from a conjugate gradient algorithm perspective. Large part of the book is devoted to preconditioned conjugate gradient algorithms. In particular memoryless and limited memory quasi-Newton algorithms are presented and numerically compared to standard conjugate gradient algorithms. The special attention is paid to the methods of shortest residuals developed by the author. Several effective optimization techniques based on these methods are presented. Because of the emphasis on practical methods, as well as rigorous mathematical treatment of their convergence analysis, the book is aimed at a wide audience. It can be used by researches in optimization, graduate students in operations research, engineering, mathematics and computer science. Practitioners can benefit from numerous numerical comparisons of professional optimization codes discussed in the book.
590			_aPara consulta fuera de la UANL se requiere clave de acceso remoto.
710	2		_aSpringerLink (Servicio en línea) _9299170
776	0	8	_iEdición impresa: _z9783540856337
856	4	0	_uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-3-540-85634-4 _zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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999			_c298851 _d298851