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E-book
An Introduction to Optimization, 2nd EditionISBN: 978-0-471-65400-1
E-book
496 pages
April 2004
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Preface.
MATHEMATICAL REVIEW.
Methods of Proof and Some Notation.
Vector Spaces and Matrices.
Transformations.
Concepts from Geometry.
Elements of Calculus.
UNCONSTRAINED OPTIMIZATION.
Basics of Set-Constrained and Unconstrained Optimization.
One-Dimensional Search Methods.
Gradient Methods.
Newton's Method.
Conjugate Direction Methods.
Quasi-Newton Methods.
Solving Ax = b.
Unconstrained Optimization and Neural Networks.
Genetic Algorithms.
LINEAR PROGRAMMING.
Introduction to Linear Programming.
Simplex Method.
Duality.
Non-Simplex Methods.
NONLINEAR CONSTRAINED OPTIMIZATION.
Problems with Equality Constraints.
Problems with Inequality Constraints.
Convex Optimization Problems.
Algorithms for Constrained Optimization.
References.
Index.
MATHEMATICAL REVIEW.
Methods of Proof and Some Notation.
Vector Spaces and Matrices.
Transformations.
Concepts from Geometry.
Elements of Calculus.
UNCONSTRAINED OPTIMIZATION.
Basics of Set-Constrained and Unconstrained Optimization.
One-Dimensional Search Methods.
Gradient Methods.
Newton's Method.
Conjugate Direction Methods.
Quasi-Newton Methods.
Solving Ax = b.
Unconstrained Optimization and Neural Networks.
Genetic Algorithms.
LINEAR PROGRAMMING.
Introduction to Linear Programming.
Simplex Method.
Duality.
Non-Simplex Methods.
NONLINEAR CONSTRAINED OPTIMIZATION.
Problems with Equality Constraints.
Problems with Inequality Constraints.
Convex Optimization Problems.
Algorithms for Constrained Optimization.
References.
Index.
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