Numerical Methods and Optimization

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A01=Panos M. Pardalos
A01=Sergiy Butenko
advanced numerical optimization methods
algorithm analysis
Analysis Of Algorithms And Computational Complexity
Author_Panos M. Pardalos
Author_Sergiy Butenko
Basic Feasible Solution
Basic Theory And Algorithms For Linear And Nonlinear Optimization
Cabernet Sauvignon Grapes
Category=UYA
Composite Trapezoidal Rule
computational mathematics
Convex Quadratic Function
Dual LP
El Em
Elementary Row Operations
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error propagation
Euler's Method
Global Minimizer
graduate level optimization
Interior Point Methods
Introductory Numerical Methods And Introductory Optimization
linear algebra techniques
Linear Independence
MATLAB programming
Maximum Clique Problem
Newton's Method
Nonbasic Variable
Nonnegativity Constraints
Optimal Tableau
Original LP
Polynomial Time Reducible
Regula Falsi Method
RK Method
Simplex Method
Slack Variable
Solve Typical Problems Using Numerical Methods
Strict Local Minimum
Students In Industrial And Systems Engineering (Ise) And Operations Research (Or)
Underlying Fundamental Ideas Behind Numerical Methods
Vice Versa
X2 S1 S2 S3 S4

Product details

  • ISBN 9781466577770
  • Weight: 714g
  • Dimensions: 156 x 234mm
  • Publication Date: 11 Mar 2014
  • Publisher: Taylor & Francis Inc
  • Publication City/Country: US
  • Product Form: Hardback
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For students in industrial and systems engineering (ISE) and operations research (OR) to understand optimization at an advanced level, they must first grasp the analysis of algorithms, computational complexity, and other concepts and modern developments in numerical methods. Satisfying this prerequisite, Numerical Methods and Optimization: An Introduction combines the materials from introductory numerical methods and introductory optimization courses into a single text. This classroom-tested approach enriches a standard numerical methods syllabus with optional chapters on numerical optimization and provides a valuable numerical methods background for students taking an introductory OR or optimization course.

The first part of the text introduces the necessary mathematical background, the digital representation of numbers, and different types of errors associated with numerical methods. The second part explains how to solve typical problems using numerical methods. Focusing on optimization methods, the final part presents basic theory and algorithms for linear and nonlinear optimization.

The book assumes minimal prior knowledge of the topics. Taking a rigorous yet accessible approach to the material, it includes some mathematical proofs as samples of rigorous analysis but in most cases, uses only examples to illustrate the concepts. While the authors provide a MATLAB® guide and code available for download, the book can be used with other software packages.

Sergiy Butenko, Panos M. Pardalos

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