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Linear and Nonlinear Programming (Paperback, 5th ed. 2021)
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Linear and Nonlinear Programming (Paperback, 5th ed. 2021)
Series: International Series in Operations Research & Management Science, 228
Expected to ship within 9 - 15 working days
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The 5th edition of this classic textbook covers the central
concepts of practical optimization techniques, with an emphasis on
methods that are both state-of-the-art and popular. One major
insight is the connection between the purely analytical character
of an optimization problem and the behavior of algorithms used to
solve that problem. End-of-chapter exercises are provided for all
chapters. The material is organized into three separate parts. Part
I offers a self-contained introduction to linear programming. The
presentation in this part is fairly conventional, covering the main
elements of the underlying theory of linear programming, many of
the most effective numerical algorithms, and many of its important
special applications. Part II, which is independent of Part I,
covers the theory of unconstrained optimization, including both
derivations of the appropriate optimality conditions and an
introduction to basic algorithms. This part of the book explores
the general properties of algorithms and defines various notions of
convergence. In turn, Part III extends the concepts developed in
the second part to constrained optimization problems. Except for a
few isolated sections, this part is also independent of Part I. As
such, Parts II and III can easily be used without reading Part I
and, in fact, the book has been used in this way at many
universities. New to this edition are popular topics in data
science and machine learning, such as the Markov Decision Process,
Farkas' lemma, convergence speed analysis, duality theories and
applications, various first-order methods, stochastic gradient
method, mirror-descent method, Frank-Wolf method, ALM/ADMM method,
interior trust-region method for non-convex optimization,
distributionally robust optimization, online linear programming,
semidefinite programming for sensor-network localization, and
infeasibility detection for nonlinear optimization.
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