| 期刊全称 | Approximation and Complexity in Numerical Optimization | | 期刊简称 | Continuous and Discr | | 影响因子2023 | Panos M. Pardalos | | 视频video | | | 学科分类 | Nonconvex Optimization and Its Applications | | 图书封面 |  | | 影响因子 | There has been much recent progress in approximation algorithms for nonconvex continuous and discrete problems from both a theoretical and a practical perspective. In discrete (or combinatorial) optimization many approaches have been developed recently that link the discrete universe to the continuous universe through geomet ric, analytic, and algebraic techniques. Such techniques include global optimization formulations, semidefinite programming, and spectral theory. As a result new ap proximate algorithms have been discovered and many new computational approaches have been developed. Similarly, for many continuous nonconvex optimization prob lems, new approximate algorithms have been developed based on semidefinite pro gramming and new randomization techniques. On the other hand, computational complexity, originating from the interactions between computer science and numeri cal optimization, is one of the major theories that have revolutionized the approach to solving optimization problems and to analyzing their intrinsic difficulty. The main focus of complexity is the study of whether existing algorithms are efficient for the solution of problems, and which problems are lik | | Pindex | Book 2000 |
The information of publication is updating
|
|
发表于 2025-3-21 18:09:59
