نتایج جستجو برای: strongly convex function

تعداد نتایج: 1435527  

Journal: :iranian journal of optimization 2010
malik zawwar hussain fareeha saadia maria hussain

the rational cubic function with three parameters has been extended to rational bi-cubic function to visualize the shape of regular convex surface data. the rational bi-cubic function involves six parameters in each rectangular patch. data dependent constraints are derived on four of these parameters to visualize the shape of convex surface data while other two are free to refine the shape of s...

2016
Sabir Hussain Shahid Qaisar

Our aim in this article is to incorporate the notion of "strongly s-convex function" and prove a new integral identity. Some new inequalities of Simpson type for strongly s-convex function utilizing integral identity and Holder's inequality are considered.

Journal: :international journal of nonlinear analysis and applications 2015
madjid eshaghi hamidreza reisi dezaki alireza moazzen

‎let $x$ be a real normed  space, then  $c(subseteq x)$  is  functionally  convex  (briefly, $f$-convex), if  $t(c)subseteq bbb r $ is  convex for all bounded linear transformations $tin b(x,r)$; and $k(subseteq x)$  is  functionally   closed (briefly, $f$-closed), if  $t(k)subseteq bbb r $ is  closed  for all bounded linear transformations $tin b(x,r)$. we improve the    krein-milman theorem  ...

Journal: :Journal of Mathematics and Statistics 2005

Journal: :Proceedings of the Japan Academy, Series A, Mathematical Sciences 1974

Journal: :Bulletin of the American Mathematical Society 1968

Journal: :Annals of Functional Analysis 2011

Journal: :Journal of The Mathematical Society of Japan 2021

A multiobjective optimization problem is $C^r$ simplicial if the Pareto set and front are diffeomorphic to a simplex and, under diffeomorphisms, each face of corresponds subproblem, where $0 \leq r \infty$. In paper titled “Topology sets strongly convex problems”, it has been shown that $C^{r-1}$ mild assumption on ranks differentials mapping for $2 On other hand, in this paper, we show $C^1$ $...

2015
İlker Bayram Ivan W. Selesnick

The Douglas-Rachford algorithm is widely used in sparse signal processing for minimizing a sum of two convex functions. In this paper, we consider the case where one of the functions is weakly convex but the other is strongly convex so that the sum is convex. We provide a condition that ensures the convergence of the same Douglas-Rachford iterations, provided that the strongly convex function i...

‎Let $X$ be a real normed  space, then  $C(subseteq X)$  is  functionally  convex  (briefly, $F$-convex), if  $T(C)subseteq Bbb R $ is  convex for all bounded linear transformations $Tin B(X,R)$; and $K(subseteq X)$  is  functionally   closed (briefly, $F$-closed), if  $T(K)subseteq Bbb R $ is  closed  for all bounded linear transformations $Tin B(X,R)$. We improve the    Krein-Milman theorem  ...

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