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020 _a9780387241494
_9978-0-387-24149-4
024 7 _a10.1007/b102601
_2doi
035 _avtls000330022
039 9 _a201509030228
_bVLOAD
_c201405070454
_dVLOAD
_c201401311326
_dstaff
_c201401311151
_dstaff
_y201401291445
_zstaff
_wmsplit0.mrc
_x443
050 4 _aQA402.5-402.6
100 1 _aBartholomew-Biggs, Michael.
_eautor
_9302146
245 1 0 _aNonlinear Optimization with Financial Applications /
_cby Michael Bartholomew-Biggs.
264 1 _aBoston, MA :
_bSpringer US,
_c2005.
300 _aXVII, 261páginas, 20 illus.
_brecurso en línea.
336 _atexto
_btxt
_2rdacontent
337 _acomputadora
_bc
_2rdamedia
338 _arecurso en línea
_bcr
_2rdacarrier
347 _aarchivo de texto
_bPDF
_2rda
500 _aSpringer eBooks
505 0 _aPortfolio Optimization -- One-Variable Optimization -- Optimal Portfolios with N Assets -- Unconstrained Optimization in N Variables -- The Steepest Descent Method -- The Newton Method -- Quasi-Newton Methods -- Conjugate Gradient Methods -- Optimal Portfolios with Restrictions -- Larger-Scale Portfolios -- Data-Fitting & The Gauss-Newton Method -- Equality Constrained Optimization -- Linear Equality Constraints -- Penalty Function Methods -- Sequential Quadratic Programming -- Further Portfolio Problems -- Inequality Constrained Optimization -- Extending Equality-Constraint Methods to Inequalities -- Barrier Function Methods -- Interior Point Methods -- Data Fitting Using Inequality Constraints -- Portfolio Re-Balancing and other Problems -- Global Unconstrained Optimization.
520 _a• The book introduces the key ideas behind practical nonlinear optimization. • Computational finance—an increasingly popular area of mathematics degree programmes—is combined here with the study of an important class of numerical techniques. • The financial content of the book is designed to be relevant and interesting to specialists. However, this material—which occupies about one-third of the text—is also sufficiently accessible to allow the book to be used on optimization courses of a more general nature. • The essentials of most currently popular algorithms are described and their performance is demonstrated on a range of optimization problems arising in financial mathematics. • Theoretical convergence properties of methods are stated and formal proofs are provided in enough cases to be instructive rather than overwhelming. • Practical behaviour of methods is illustrated by computational examples and discussions of efficiency, accuracy and computational costs. • Supporting software for the examples and exercises is available (but the text does not require the reader to use or understand these particular codes). • The author has been active in optimization for over thirty years in algorithm development and application and in teaching and research supervision. Audience The book is aimed at lecturers and students (undergraduate and postgraduate) in mathematics, computational finance and related subjects. It is also useful for researchers and practitioners who need a good introduction to nonlinear optimization.
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:
_z9781402081101
856 4 0 _uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/b102601
_zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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