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＜電子ブック＞
Optimization : Algorithms and Consistent Approximations / edited by Elijah Polak
(Applied Mathematical Sciences. ISSN:2196968X ; 124)

版	1st ed. 1997.
出版者	(New York, NY : Springer New York : Imprint: Springer)
出版年	1997
本文言語	英語
大きさ	XX, 782 p : online resource
著者標目	Polak, Elijah editor SpringerLink (Online service)
件　名	LCSH:Mathematical optimization LCSH:Calculus of variations LCSH:Mathematics LCSH:System theory LCSH:Control theory LCSH:Operations research FREE:Calculus of Variations and Optimization FREE:Applications of Mathematics FREE:Systems Theory, Control FREE:Operations Research and Decision Theory
一般注記	Contents: Unconstrained Optimization -- Optimality Conditions -- Algorithm Models and Convergence Conditions I -- Gradient Methods -- Newton's Method -- Methods of Conjugate Directions -- Quasi-Newton Methods -- One Dimensional Optimization -- Newton's Method for Equations and Inequalities -- Finite Minimax and Constrained Optimization -- Optimality Conditions for Minimax -- Optimality Conditions for Constrained Optimization -- Algorithm Models and Convergence Conditions II -- First-Order Minimax Algorithms -- Newton's Method for Minimax Problems -- Phase I. Phase II Methods of Centers -- Penalty Function Algorithms -- An Augmented Lagrangian Method This book deals with optimality conditions, algorithms, and discretization tech­ niques for nonlinear programming, semi-infinite optimization, and optimal con­ trol problems. The unifying thread in the presentation consists of an abstract theory, within which optimality conditions are expressed in the form of zeros of optimality junctions, algorithms are characterized by point-to-set iteration maps, and all the numerical approximations required in the solution of semi-infinite optimization and optimal control problems are treated within the context of con­ sistent approximations and algorithm implementation techniques. Traditionally, necessary optimality conditions for optimization problems are presented in Lagrange, F. John, or Karush-Kuhn-Tucker multiplier forms, with gradients used for smooth problems and subgradients for nonsmooth prob­ lems. We present these classical optimality conditions and show that they are satisfied at a point if and only if this point is a zero of an upper semicontinuous optimality junction. The use of optimality functions has several advantages. First, optimality functions can be used in an abstract study of optimization algo­ rithms. Second, many optimization algorithms can be shown to use search directions that are obtained in evaluating optimality functions, thus establishing a clear relationship between optimality conditions and algorithms. Third, estab­ lishing optimality conditions for highly complex problems, such as optimal con­ trol problems with control and trajectory constraints, is much easier in terms of optimality functions than in the classical manner. In addition, the relationship between optimality conditions for finite-dimensional problems and semi-infinite optimization and optimal control problems becomestransparent HTTP:URL=https://doi.org/10.1007/978-1-4612-0663-7

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