Mathematics for Computer Science
Mathematics for Computer Science

Author: Eric Lehman

Publisher:

Published: 2017-03-08

Total Pages: 988

ISBN-13: 9789888407064

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This book covers elementary discrete mathematics for computer science and engineering. It emphasizes mathematical definitions and proofs as well as applicable methods. Topics include formal logic notation, proof methods; induction, well-ordering; sets, relations; elementary graph theory; integer congruences; asymptotic notation and growth of functions; permutations and combinations, counting principles; discrete probability. Further selected topics may also be covered, such as recursive definition and structural induction; state machines and invariants; recurrences; generating functions.

Optimization Methods in Finance
Optimization Methods in Finance

Author: Gerard Cornuejols

Publisher: Cambridge University Press

Published: 2006-12-21

Total Pages: 358

ISBN-13: 9780521861700

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Optimization models play an increasingly important role in financial decisions. This is the first textbook devoted to explaining how recent advances in optimization models, methods and software can be applied to solve problems in computational finance more efficiently and accurately. Chapters discussing the theory and efficient solution methods for all major classes of optimization problems alternate with chapters illustrating their use in modeling problems of mathematical finance. The reader is guided through topics such as volatility estimation, portfolio optimization problems and constructing an index fund, using techniques such as nonlinear optimization models, quadratic programming formulations and integer programming models respectively. The book is based on Master's courses in financial engineering and comes with worked examples, exercises and case studies. It will be welcomed by applied mathematicians, operational researchers and others who work in mathematical and computational finance and who are seeking a text for self-learning or for use with courses.

Mathematical Theory of Optimization
Mathematical Theory of Optimization

Author: Ding-Zhu Du

Publisher: Springer Science & Business Media

Published: 2013-03-14

Total Pages: 277

ISBN-13: 1475757956

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This book provides an introduction to the mathematical theory of optimization. It emphasizes the convergence theory of nonlinear optimization algorithms and applications of nonlinear optimization to combinatorial optimization. Mathematical Theory of Optimization includes recent developments in global convergence, the Powell conjecture, semidefinite programming, and relaxation techniques for designs of approximation solutions of combinatorial optimization problems.

Python for Finance
Python for Finance

Author: Yves J. Hilpisch

Publisher: "O'Reilly Media, Inc."

Published: 2018-12-05

Total Pages: 682

ISBN-13: 1492024295

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The financial industry has recently adopted Python at a tremendous rate, with some of the largest investment banks and hedge funds using it to build core trading and risk management systems. Updated for Python 3, the second edition of this hands-on book helps you get started with the language, guiding developers and quantitative analysts through Python libraries and tools for building financial applications and interactive financial analytics. Using practical examples throughout the book, author Yves Hilpisch also shows you how to develop a full-fledged framework for Monte Carlo simulation-based derivatives and risk analytics, based on a large, realistic case study. Much of the book uses interactive IPython Notebooks.

Quantities, Units and Symbols in Physical Chemistry
Quantities, Units and Symbols in Physical Chemistry

Author: International Union of Pure and Applied Chemistry. Physical and Biophysical Chemistry Division

Publisher: Royal Society of Chemistry

Published: 2007

Total Pages: 240

ISBN-13: 0854044337

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Prepared by the IUPAC Physical Chemistry Division this definitive manual, now in its third edition, is designed to improve the exchange of scientific information among the readers in different disciplines and across different nations. This book has been systematically brought up to date and new sections added to reflect the increasing volume of scientific literature and terminology and expressions being used. The Third Edition reflects the experience of the contributors with the previous editions and the comments and feedback have been integrated into this essential resource. This edition has been compiled in machine-readable form and will be available online.

Multi-Objective Programming and Goal Programming
Multi-Objective Programming and Goal Programming

Author: Mehrdad Tamiz

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 365

ISBN-13: 3642875610

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Most real-life problems involve making decisions to optimally achieve a number of criteria while satisfying some hard or soft constraints. In this book several methods for solving such problems are presented by the leading experts in the area. The book also contains a number of very interesting application papers which demonstrate theoretical modelling, analysing and solution of real-life problems.

Primal-dual Interior-Point Methods
Primal-dual Interior-Point Methods

Author: Stephen J. Wright

Publisher: SIAM

Published: 1997-01-01

Total Pages: 309

ISBN-13: 9781611971453

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In the past decade, primal-dual algorithms have emerged as the most important and useful algorithms from the interior-point class. This book presents the major primal-dual algorithms for linear programming in straightforward terms. A thorough description of the theoretical properties of these methods is given, as are a discussion of practical and computational aspects and a summary of current software. This is an excellent, timely, and well-written work. The major primal-dual algorithms covered in this book are path-following algorithms (short- and long-step, predictor-corrector), potential-reduction algorithms, and infeasible-interior-point algorithms. A unified treatment of superlinear convergence, finite termination, and detection of infeasible problems is presented. Issues relevant to practical implementation are also discussed, including sparse linear algebra and a complete specification of Mehrotra's predictor-corrector algorithm. Also treated are extensions of primal-dual algorithms to more general problems such as monotone complementarity, semidefinite programming, and general convex programming problems.

Operating Systems and Middleware
Operating Systems and Middleware

Author: Max Hailperin

Publisher: Max Hailperin

Published: 2007

Total Pages: 496

ISBN-13: 0534423698

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By using this innovative text, students will obtain an understanding of how contemporary operating systems and middleware work, and why they work that way.