Recent Advances in DC Programming and DCA

被引：136
|
作者：
Tao Pham Dinh [1 ]
Hoai An Le Thi [2 ]
机构：
[1] Natl Inst Appl Sci Rouen, Math Lab, F-76801 St Etienne, France
[2] Univ Lorraine, Lab Theoret & Appl Comp Sci LITA, UFR MIM, F-57045 Metz, France
来源：
TRANSACTIONS ON COMPUTATIONAL COLLECTIVE INTELLIGENCE XIII | 2014年 / 8342卷
关键词：
DC programming and DCA; rate convergence of DCA with subanalytic data; exact penalty and error-bounds in DC programming; DCA for general DC programs; exact penalty in mixed integer DC programming; approximation and penalization of the l(0)-norm; CONVERGENCE;
D O I：
10.1007/978-3-642-54455-2_1
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Difference of Convex functions (DC) Programming and DC Algorithm (DCA) constitute the backbone of Nonconvex Programming and Global Optimization. The paper is devoted to the State of the Art with recent advances of DC Programming and DCA to meet the growing need for nonconvex optimization and global optimization, both in terms of mathematical modeling as in terms of efficient scalable solution methods. After a brief summary of these theoretical and algorithmic tools, we outline the main results on convergence of DCA in DC programming with subanalytic data, exact penalty techniques with/without error bounds in DC programming including mixed integer DC programming, DCA for general DC programs, and DC programming involving the l(0)-norm via its approximation and penalization.
引用
收藏
页码：1 / 37
页数：37
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