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Separable Optimization - Theory and Methods (Hardcover, 2nd ed. 2021)
Loot Price: R3,779
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Separable Optimization - Theory and Methods (Hardcover, 2nd ed. 2021)
Series: Springer Optimization and Its Applications, 177
Expected to ship within 10 - 15 working days
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In this book, the theory, methods and applications of separable
optimization are considered. Some general results are presented,
techniques of approximating the separable problem by linear
programming problem, and dynamic programming are also studied.
Convex separable programs subject to inequality/ equality
constraint(s) and bounds on variables are also studied and
convergent iterative algorithms of polynomial complexity are
proposed. As an application, these algorithms are used in the
implementation of stochastic quasigradient methods to some
separable stochastic programs. The problems of numerical
approximation of tabulated functions and numerical solution of
overdetermined systems of linear algebraic equations and some
systems of nonlinear equations are solved by separable convex
unconstrained minimization problems. Some properties of the
Knapsack polytope are also studied. This second edition includes a
substantial amount of new and revised content. Three new chapters,
15-17, are included. Chapters 15-16 are devoted to the further
analysis of the Knapsack problem. Chapter 17 is focused on the
analysis of a nonlinear transportation problem. Three new
Appendices (E-G) are also added to this edition and present
technical details that help round out the coverage. Optimization
problems and methods for solving the problems considered are
interesting not only from the viewpoint of optimization theory,
optimization methods and their applications, but also from the
viewpoint of other fields of science, especially the artificial
intelligence and machine learning fields within computer science.
This book is intended for the researcher, practitioner, or engineer
who is interested in the detailed treatment of separable
programming and wants to take advantage of the latest theoretical
and algorithmic results. It may also be used as a textbook for a
special topics course or as a supplementary textbook for graduate
courses on nonlinear and convex optimization.
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