Advances in Convex Analysis and Global Optimization: Honoring the Memory of C. Caratheodory (1873-1950)
Springer Science & Business Media, 30 thg 6, 2001 - 594 trang
There has been much recent progress in global optimization algo rithms for nonconvex continuous and discrete problems from both a theoretical and a practical perspective. Convex analysis plays a fun damental role in the analysis and development of global optimization algorithms. This is due essentially to the fact that virtually all noncon vex optimization problems can be described using differences of convex functions and differences of convex sets. A conference on Convex Analysis and Global Optimization was held during June 5 -9, 2000 at Pythagorion, Samos, Greece. The conference was honoring the memory of C. Caratheodory (1873-1950) and was en dorsed by the Mathematical Programming Society (MPS) and by the Society for Industrial and Applied Mathematics (SIAM) Activity Group in Optimization. The conference was sponsored by the European Union (through the EPEAEK program), the Department of Mathematics of the Aegean University and the Center for Applied Optimization of the University of Florida, by the General Secretariat of Research and Tech nology of Greece, by the Ministry of Education of Greece, and several local Greek government agencies and companies. This volume contains a selective collection of refereed papers based on invited and contribut ing talks presented at this conference. The two themes of convexity and global optimization pervade this book. The conference provided a forum for researchers working on different aspects of convexity and global opti mization to present their recent discoveries, and to interact with people working on complementary aspects of mathematical programming.
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Advances in Convex Analysis and Global Optimization: Honoring the Memory of ...
Nicolas Hadjisavvas,Panos M. Pardalos
Xem trước bị giới hạn - 2013
addition algorithm analysis angle applied approach approximation assume assumptions bound called closed combinatorial optimization complexity computational condition conformations consider constraints contains continuous convergence convex corresponding defined definition denote described developed differentiable direction duality energy equal equation equivalent example exists feasible formulation function given global minimum global optimization graph hence holds increasing inequality initial interval introduced iteration known learning Lemma linear lower Mathematics matrix maximal maximum measure method minimization monotone nonconvex normal Note objective obtained operator optimization problem parameters polynomial positive potential present problem programming Proof Proposition protein quadratic random relaxation respect satisfies semidefinite programming sequence solution solving space Step structure technique Theorem theory tion University upper variables variational vector weight