Introduction to Global Optimization
Springer Science & Business Media, 30 thg 6, 1995 - 320 trang
Global optimization concerns the computation and characterization of global optima of nonlinear functions. Such problems are widespread in the mathematical modelling of real systems in a very wide range of applications and the last 30 years have seen the development of many new theoretical, algorithmic and computational contributions which have helped to solve globally multiextreme problems in important practical applications.
Most of the existing books on optimization focus on the problem of computing locally optimal solutions. Introduction to Global Optimization, however, is a comprehensive textbook on constrained global optimization that covers the fundamentals of the subject, presenting much new material, including algorithms, applications and complexity results for quadratic programming, concave minimization, DC and Lipschitz problems, and nonlinear network flow. Each chapter contains illustrative examples and ends with carefully selected exercises, designed to help students grasp the material and enhance their knowledge of the methods involved.
Audience: Students of mathematical programming, and all scientists, from whatever discipline, who need global optimization methods in such diverse areas as economic modelling, fixed charges, finance, networks and transportation, databases, chip design, image processing, nuclear and mechanical design, chemical engineering design and control, molecular biology, and environmental engineering.
Nội dung mọi người đang nói đến - Viết bài đánh giá
Chúng tôi không t́m thấy bài đánh giá nào ở các vị trí thông thường.
Ấn bản in khác - Xem tất cả
7-extension affine function assume bisection bound algorithm branch and bound Compute concave function concave minimization problem Consider constraints convex combination convex envelope convex functions convex set corresponding d.c. functions d.c. program defined denote edge equivalent example feasible domain feasible point feasible set finite number formulation function f given global minimum global optimization hence Horst and Tuy hyperplane H implies inequality integer intersection iteration Kuhn-Tucker Let f linear program linearly independent Lipschitz constant Lipschitzian lower bound matrix maximum clique maximum clique problem MCCFP n-simplex node nonempty nonlinear NP-complete NP-hard objective function objective function value obtain optimal solution optimal value optimization problem partition sets piecewise linear polyhedron polynomial polytope programming problem Proof Proposition quadratic problem quadratic programming rectangle satisfying Section sequence simplex simplices subdivision subproblem Theorem upper bound variables vector vertex vertex set vertices xTQx zero-one
Trang 309 - EISNER, MJ; AND SEVERANCE, D. G. "Mathematical techniques for efficient record segmentation in large shared data bases,
Trang 313 - A Design Centering Algorithm for Nonconvex Regions of Acceptability, IEEE Transactions on Computer- Aided- Design of Integrated Circuits and Systems CAD-1, 13-24.
Trang 308 - Balas, E. and Burdet, CA, Maximizing a Convex Quadratic Function subject to Linear Constraints, Management Science Research Report No.
Trang 312 - H., DC Optimization: Theory, Methods and Algorithms, in: Horst, R. and Pardalos, PM, editors, Handbook of Global Optimization, Kluwer, Dordrecht (1994).
Tất cả kết quả T́m kiếm Sách »
Nondifferentiable Optimization and Polynomial Problems
Xem trước bị giới hạn - 1998