Titlebook: Generalized Convexity and Vector Optimization; Shashi Kant Mishra,Shou-Yang Wang,Kin Keung Lai Book 2009 Springer-Verlag Berlin Heidelberg

劉興旺

書目名稱Generalized Convexity and Vector Optimization影響因子(影響力)




書目名稱Generalized Convexity and Vector Optimization影響因子(影響力)學(xué)科排名




書目名稱Generalized Convexity and Vector Optimization網(wǎng)絡(luò)公開度




書目名稱Generalized Convexity and Vector Optimization網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Generalized Convexity and Vector Optimization被引頻次




書目名稱Generalized Convexity and Vector Optimization被引頻次學(xué)科排名




書目名稱Generalized Convexity and Vector Optimization年度引用




書目名稱Generalized Convexity and Vector Optimization年度引用學(xué)科排名




書目名稱Generalized Convexity and Vector Optimization讀者反饋




書目名稱Generalized Convexity and Vector Optimization讀者反饋學(xué)科排名

Delirium

dyspareunia

Incorporate

Second and Higher Order Duality,can be developed further in two ways: one is in a more general setting of a modified dual (namely, a second order and a higher order dual), the other is in the generalized convexity. The benefit of doing this not only that results obtained by these kinds of duals under generalized convexity extend s

民間傳說

Symmetric Duality,nd its dual are symmetric in this sense. However, this is not the case in nonlinear programs in general. Following Dorn (1960) many authors have contributed to symmetric duality, see Dantzig et al. (1965), Bazaraa and Goode (1973), Chandra et al. (1985), Cottle (1963), Hou and Yang (2001), Kim et al

STAT

Vector Variational-like Inequality Problems,ons of α— invex functions. We will identify the vector critical points, the weakly efficient solutions and the solutions of the weak vector variational-like inequality problems, under pseudo-α— invexity assumptions. These conditions are more general than those of existing ones in the literature. In

STAT

Emmenagogue

VARIATION 3: Portraits, Psychogramme,ome well-known classical results of (first order) duality for convex optimization problems, but also that higher order duality can provide a lower bound to the infimum of a primal optimization problem when it is difficult to find a feasible solution for the first order dual.

Injunction

實(shí)施生效