000 03293nam a22003855i 4500
001 288257
003 MX-SnUAN
005 20160429154621.0
007 cr nn 008mamaa
008 150903s2013 xxu| o |||| 0|eng d
020 _a9781461458388
_99781461458388
024 7 _a10.1007/9781461458388
_2doi
035 _avtls000341744
039 9 _a201509030337
_bVLOAD
_c201405050233
_dVLOAD
_y201402061107
_zstaff
040 _aMX-SnUAN
_bspa
_cMX-SnUAN
_erda
050 4 _aQA276-280
100 1 _aLange, Kenneth.
_eautor
_9313126
245 1 0 _aOptimization /
_cby Kenneth Lange.
250 _a2nd ed. 2013.
264 1 _aNew York, NY :
_bSpringer New York :
_bImprint: Springer,
_c2013.
300 _axvii, 529 páginas 19 ilustraciones, 3 ilustraciones en color.
_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 _aSpringer Texts in Statistics,
_x1431-875X ;
_v95
500 _aSpringer eBooks
505 0 _aElementary Optimization -- The Seven C’s of Analysis -- The Gauge Integral -- Differentiation -- Karush-Kuhn-Tucker Theory -- Convexity -- Block Relaxation -- The MM Algorithm -- The EM Algorithm -- Newton’s Method and Scoring -- Conjugate Gradient and Quasi-Newton -- Analysis of Convergence -- Penalty and Barrier Methods -- Convex Calculus -- Feasibility and Duality -- Convex Minimization Algorithms -- The Calculus of Variations -- Appendix: Mathematical Notes -- References -- Index.
520 _aFinite-dimensional optimization problems occur throughout the mathematical sciences. The majority of these problems cannot be solved analytically. This introduction to optimization attempts to strike a balance between presentation of mathematical theory and development of numerical algorithms. Building on students’ skills in calculus and linear algebra, the text provides a rigorous exposition without undue abstraction. Its stress on statistical applications will be especially appealing to graduate students of statistics and biostatistics. The intended audience also includes students in applied mathematics, computational biology, computer science, economics, and physics who want to see rigorous mathematics combined with real applications.   In this second edition, the emphasis remains on finite-dimensional optimization. New material has been added on the MM algorithm, block descent and ascent, and the calculus of variations. Convex calculus is now treated in much greater depth.  Advanced topics such as the Fenchel conjugate, subdifferentials, duality, feasibility, alternating projections, projected gradient methods, exact penalty methods, and Bregman iteration will equip students with the essentials for understanding modern data mining techniques in high dimensions.
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:
_z9781461458371
856 4 0 _uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-1-4614-5838-8
_zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
942 _c14
999 _c288257
_d288257