2 edition of Nonlinear discrete optimization found in the catalog.
Includes bibliographical references (p. -134) and index.
|Series||Zurich lectures in advanced mathematics|
|The Physical Object|
|Pagination||x, 137 :|
|Number of Pages||137|
From the review by Panos Pardalos (Optimization Methods and Sofware, ): "This beautifully written book provides an introductory treatment of linear, nonlinear, and discrete network optimization problems. In mathematics and science, a nonlinear system is a system in which the change of the output is not proportional to the change of the input. Nonlinear problems are of interest to engineers, biologists, physicists, mathematicians, and many other scientists because most systems are inherently nonlinear in nature. Nonlinear dynamical systems, describing changes in variables over time, may appear.
This is a substantially expanded (by pages) and improved edition of our best-selling nonlinear programming book. The treatment focuses on iterative algorithms for constrained and unconstrained optimization, Lagrange multipliers and duality, large scale problems, and on the interface between continuous and discrete optimization. An insightful, comprehensive, and up-to-date treatment of linear, nonlinear, and discrete/combinatorial network optimization problems, their applications, and their analytical and algorithmic methodology. It covers extensively theory, algorithms, and applications, and it aims to bridge the gap 4/5(5).
use of nonlinear expressions and discrete variables in optimization models. Both of these extensions of the linear programming model are NP-hard, thus representing very challenging problems. On the brighter side, recent advances in algorithmic and computing technology make it possible to re-. This book is addressed to students in the fields of engineering and technology as well as practicing engineers. It covers the fundamentals of commonly used optimization methods in engineering design. These include graphical optimization, linear and nonlinear programming, numerical optimization, and discrete optimization/5(26).
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Get this from a library. Nonlinear discrete optimization: an algorithmic theory. [Shmuel Onn] -- This monograph develops an algorithmic theory of nonlinear discrete optimization. It introduces a simple and useful setup which enables the polynomial time solution of broad fundamental classes of.
This extensive rigorous texbook, developed through instruction at MIT, focuses on nonlinear and other types of optimization: iterative algorithms for constrained and unconstrained optimization, Lagrange multipliers and duality, large scale problems, and the interface /5(8).
Nonlinear Discrete Optimization Based on several papers joint with several co-authors including Berstein, De Loera, Hemmecke, Lee, Rothblum, Weismantel.
Outline 1. Convex Discrete Maximization 2. Nonlinear Integer Programming 3. Nonlinear Combinatorial Optimization 0. The Setup Shmuel Onn. Shmuel Onn Setup for Nonlinear Discrete Optimization. This book offers an introduction to optimization theory in normed spaces.
The topics covered include existence results, various differentiability notions together with optimality conditions, the contingent cone, a generalization of the Lagrange multiplier rule, duality theory, extended semidefinite optimization, and an investigation of linear quadratic and time minimal control problems.
Purchase Discrete Optimization - 1st Edition. Print Book & E-Book. ISBNBook Edition: 1. This book covers important topics like stability, hyperbolicity, bifurcation Nonlinear discrete optimization book and chaos, topics which are essential in order to understand the fascinating behavior of nonlinear discrete dynamical systems.
The theory is illuminated by several examples and exercises, many of them taken from population dynamical studies. This book treats the fundamental issues and algorithmic strategies emerging as the core of the discipline of discrete optimization in a comprehensive and rigorous fashion.
Following an introductory chapter on computational complexity, the basic algorithmic results for the two major models of polynomial algorithms are introduced--models using.
Graduate students and researchers in applied mathematics, optimization, engineering, computer science, and management science will find this book a useful reference which provides an introduction to applications and fundamental theories in nonlinear combinatorial optimization.
Nonlinear combinatorial optimization is a new research area within. Discrete Optimization publishes research papers on the mathematical, computational and applied aspects of all areas of integer programming and combinatorial optimization.
In addition to reports on mathematical results pertinent to discrete optimization, the journal welcomes submissions on algorithmic developments, computational experiments. Purchase Discrete Optimization, Volume 11 - 1st Edition. Print Book & E-Book.
ISBNNetwork optimization lies in the middle of the great divide that separates the two major types of optimization problems, continuous and discrete. The ties between linear programming and combinatorial optimization can be traced to the representation of the constraint.
This book provides an up-to-date, comprehensive, and rigorous account of nonlinear programming at the first year graduate student level.
It covers descent algorithms for unconstrained and constrained optimization, Lagrange multiplier theory, interior point and augmented Lagrangian methods for linear and nonlinear programs, duality theory, and major aspects of large-scale optimization. lems ask for the best way to pair-up a set of objects, and so on.
Discrete-optimization models, such as these, are typically deﬁned on discrete structures, including networks, graphs, and matrices. As a ﬁeld of mathematics, discrete optimization is both broad and File Size: KB.
Nonlinear Optimization: Discrete optimization INSEAD, Spring Jean-Philippe Vert Ecole des Mines de Paris @ Nonlinear optimization c Jean-Philippe Vert, (@) – p.1/ I have a simple (indeed standard in economics) nonlinear constrained discrete maximisation problem to solve in R and am having trouble.
I found solutions for parts of the problem (nonlinear maximisation; discrete maximisation) but not for the union of all the problems. Here is the problem. A consumer wants to buy three products (ananas, banana, cookie), knows the prices and has a budget of 20€.
This book on unconstrained and bound constrained optimization can be used as a tutorial for self-study or a reference by those who solve such problems in their work.
It can also serve as a textbook in an introductory optimization course. As in my earlier book  on linear and nonlinear equations, we treat a File Size: 1MB.
In most practical applications for nonlinear discrete problems, the latter version of the BBM has been used most often, where functions are assumed to be differentiable and design variables can have nondiscrete values during the solution process.
Different methods have been used to solve nonlinear optimization subproblems to generate the nodes. A Brief Introduction to Discrete Optimization Discrete or Combinatorial Optimization deals mainly with problems where we have to choose an optimal solution from a finite (or sometimes countable) number of possibilities.
In this short introduction we shall visit a sample of Discrete Optimization problems, step through the thinking process of. Offered by The University of Melbourne. Tired of solving Sudokus by hand.
This class teaches you how to solve complex search problems with discrete optimization concepts and algorithms, including constraint programming, local search, and mixed-integer programming.
Optimization technology is ubiquitous in our society. It schedules planes and their crews, coordinates the production of steel, and. In discrete optimization, some or all of the variables in a model are required to belong to a discrete set; this is in contrast to continuous optimization in which the variables are allowed to take on any value within a range of values.
Here, we consider two branches of discrete optimization. In integer programming, the discrete set is a subset of integers. Framework for Nonlinear Discrete Optimization The set of feasible points is a subset S of Z n suitably presented, e.g. The objective function is parameterized as f(w 1 x, w d x), whereFile Size: KB.Introduction to Applied Optimization is intended for advanced undergraduate and graduate students and will benefit scientists from diverse areas, including engineers.
Keywords Theorie algorithms development global optimization linear optimization multi-objective .It is designed keeping in mind that it can be used as a graduate textbook in one or two semester courses in nonlinear and mixed-integer optimization and applications in process synthesis, as well as a reference for any engineering student or researcher concerned with continuous and discrete nonlinear optimization issues in engineering design.