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Fundamentals of optimization techniques with algorithms

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Fundamentals of optimization techniques with algorithms/ Sukanta Nayak.
作者:	Nayak, Sukanta.
出版者:	London, United Kingdom ;Academic Press is an imprint of Elsevier, : 2020.,
面頁冊數:	1 online resource (xv, 305 p.) :ill. :
標題:	Mathematical optimization. -
電子資源:	https://www.sciencedirect.com/science/book/9780128211267
ISBN:	9780128224922

Fundamentals of optimization techniques with algorithms
Nayak, Sukanta.

Fundamentals of optimization techniques with algorithms[electronic resource] /Sukanta Nayak. - London, United Kingdom ;Academic Press is an imprint of Elsevier,2020. - 1 online resource (xv, 305 p.) :ill.

Includes bibliographical references and index.

Front Cover -- Fundamentals of Optimization Techniques With Algorithms -- Copyright Page -- Dedication -- Contents -- Preface -- Acknowledgments -- 1. Introduction to optimization -- 1.1 Optimal problem formulation -- 1.1.1 Design variables -- 1.1.2 Constraints -- 1.1.3 Objective function -- 1.1.4 Variable bounds -- 1.2 Engineering applications of optimization -- 1.3 Optimization techniques -- Further reading -- 2. Linear programming -- 2.1 Formulation of the problem -- Practice set 2.1 -- 2.2 Graphical method -- 2.2.1 Working procedure -- Practice set 2.2 -- 2.3 General LPP

ISBN: 9780128224922Subjects--Topical Terms:

527675
Mathematical optimization.
Index Terms--Genre/Form:

554714
Electronic books.

LC Class. No.: QA402.5

Dewey Class. No.: 519.6

Fundamentals of optimization techniques with algorithms
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245 1 0 $a Fundamentals of optimization techniques with algorithms $h [electronic resource] / $c Sukanta Nayak.
260 $a London, United Kingdom ; $a San Diego, CA, United States : $b Academic Press is an imprint of Elsevier, $c 2020.
300 $a 1 online resource (xv, 305 p.) : $b ill.
504 $a Includes bibliographical references and index.
505 0 $a Front Cover -- Fundamentals of Optimization Techniques With Algorithms -- Copyright Page -- Dedication -- Contents -- Preface -- Acknowledgments -- 1. Introduction to optimization -- 1.1 Optimal problem formulation -- 1.1.1 Design variables -- 1.1.2 Constraints -- 1.1.3 Objective function -- 1.1.4 Variable bounds -- 1.2 Engineering applications of optimization -- 1.3 Optimization techniques -- Further reading -- 2. Linear programming -- 2.1 Formulation of the problem -- Practice set 2.1 -- 2.2 Graphical method -- 2.2.1 Working procedure -- Practice set 2.2 -- 2.3 General LPP
505 8 $a 2.3.1 Canonical and standard forms of LPP -- Practice set 2.3 -- 2.4 Simplex method -- 2.4.1 Reduction of feasible solution to a basic feasible solution -- 2.4.2 Working procedure of the simplex method -- Practice set 2.4 -- 2.5 Artificial variable techniques -- 2.5.1 Big M method -- 2.5.2 Two-phase method -- Practice set 2.5 -- 2.6 Duality Principle -- 2.6.1 Formulation of a dual problem -- 2.6.1.1 Formulation of a dual problem when the primal has equality constraints -- 2.6.1.2 Duality principle -- Practice set 2.6 -- 2.7 Dual simplex method -- 2.7.1 Working procedure for a dual simplex method
505 8 $a Practice set 2.7 -- Further reading -- 3. Single-variable nonlinear optimization -- 3.1 Classical method for single-variable optimization -- 3.2 Exhaustive search method -- 3.3 Bounding phase method -- 3.4 Interval halving method --3.5 Fibonacci search method -- 3.6 Golden section search method -- 3.7 Bisection method -- 3.8 Newton-Raphson method -- 3.9 Secant method -- 3.10 Successive quadratic point estimation method -- Further reading -- 4. Multivariable unconstrained nonlinear optimization -- 4.1 Classical method for multivariable optimization
505 8 $a 4.1.1 Definition: rth differential of a function f(X) -- 4.1.2 Necessary condition -- 4.1.3 Sufficient condition -- 4.2 Unidirectional search method -- 4.3 Evolutionary search method -- 4.3.1 Box's evolutionary optimization method -- 4.4 Simplex search method -- 4.5 Hooke-Jeeves pattern search method -- 4.5.1 Exploratory move -- 4.5.2 Pattern move -- 4.6 Conjugate direction method -- 4.6.1 Parallel subspace property -- 4.6.2 Extended parallel subspace property -- 4.7 Steepest descent method -- 4.7.1 Cauchy's (steepest descent) method -- 4.8 Newton's method -- 4.9 Marquardt's method
505 8 $a Practice set -- Further reading -- 5. Multivariable constrained nonlinear optimization -- 5.1 Classical methods for equality constrained optimization -- 5.1.1 Solution by direct substitution -- 5.1.2 Solution by the method of constrained variation -- 5.1.3 Solution by the method of Lagrange multipliers -- 5.1.3.1 Necessary conditions -- 5.1.3.2 Sufficient condition -- 5.2 Classical methods for inequality constrained optimization -- 5.3 Random search method -- 5.4 Complex method -- 5.4.1 Iterative procedure -- 5.5 Sequential linear programming -- 5.6 Zoutendijk's method of feasible directions
650 0 $a Mathematical optimization. $3 527675
650 0 $a Computer algorithms. $3 528448
650 0 $a Algorithms. $3 527865
655 4 $a Electronic books. $2 local $3 554714
856 4 0 $u https://www.sciencedirect.com/science/book/9780128211267