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  • Book

  • Combinatorial Optimization

    • Authors: Korte, Bernhard (2002)

  • This book on combinatorial optimization is a beautiful example of the ideal textbook.The second edition (with corrections and many updates) of this very recommendable book documents the relevant knowledge on combinatorial optimization and records those problems and algorithms that define this discipline today. To read this is very stimulating for all the researchers, practitioners, and students interested in combinatorial optimization
  • Book

  • Convex optimization

    • Authors: Stephen Boyd (2004)

  • Convex optimization problems arise frequently in many different fields. A comprehensive introduction to the subject, this book shows in detail how such problems can be solved numerically with great efficiency. The focus is on recognizing convex optimization problems and then finding the most appropriate technique for solving them. The text contains many worked examples and homework exercises and will appeal to students, researchers and practitioners in fields such as engineering, computer science, mathematics, statistics, finance, and economics.
  • Book

  • Optimizatiom

    • Authors: Aharon Ben-Tal (2012)

  • LECTURE OUTLINE • The Role of Convexity in Optimization • Duality Theory • Algorithms and Duality • Course Organization
  • Book

  • Introduction to Differential Equations

    • Authors: Jeffrey R. Chasnov (2016)

  • Contents: - A short mathematical review - Introduction to odes - First-order odes - Second-order odes, constant coefficients - The Laplace transform - Series solutions - Systems of equations - Nonlinear differential equations - Partial differential equations - Second-order odes, constant coefficients
  • Book

  • Solutions Manual to accompany Nonlinear Programming

    • Authors: Mokhtar S. Bazaraa (2006)

  • As the Solutions Manual, this book is meant to accompany the main title, Nonlinear Programming: Theory and Algorithms, Third Edition. This book presents recent developments of key topics in nonlinear programming (NLP) using a logical and self-contained format. The volume is divided into three sections: convex analysis, optimality conditions, and dual computational techniques. Precise statements of algortihms are given along with convergence analysis. Each chapter contains detailed numerical examples, graphical illustrations, and numerous exercises to aid readers in understanding the concepts and methods discussed.
  • Book

  • Applied numerical analysis

    • Authors: Curtis F. Gerald (2004)

  • Applied Numerical Analysis The seventh edition of this classic text has retained the features that make it popular, while updating its treatment and inclusion of Computer Algebra Systems and Programming Languages. Interesting and timely applications motivate and enhance students' understanding of methods and analysis of results. This text incorporates a balance of theory with techniques and applications, including optional ...
  • Book

  • Linear Algebra

    • Authors: Jim Hefferon (2016)

  • This book helps students to master the material of a standard US undergraduate first course in Linear Algebra. The material is standard in that the subjects covered are Gaussian reduction, vector spaces, linear maps, determinants, and eigenvalues and eigenvectors. Another standard is book’s audience: sophomores or juniors, usually with a background of at least one semester of calculus. The help that it gives to students comes from taking a developmental approach — this book’s presentation emphasizes motivation and naturalness, using many examples.
  • Book

  • Elementary linear algebra

    • Authors: Kenneth Kuttler (2012)

  • This is an introduction to linear algebra. The main part of the book features row operations and everything is done in terms of the row reduced echelon form and specific algorithms. At the end, the more abstract notions of vector spaces and linear transformations on vector spaces are presented. However, this is intended to be a first course in linear algebra for students who are sophomores or juniors who have had a course in one variable calculus and a reasonable background in college algebra. I have given complete proofs of all the fundamental ideas, but some topics such as Markov matrices are not complete in this book but receive a plausible introduction. The book contains a complete treatment of determinants and a simple proof of the Cayley Hamilton theorem although these are op...