Linear approximation and generalized convexity
نویسندگان
چکیده
منابع مشابه
Generalized Convexity and Integral Inequalities
In this paper, we consider a very useful and significant class of convex sets and convex functions that is relative convex sets and relative convex functions which was introduced and studied by Noor [20]. Several new inequalities of Hermite-Hadamard type for relative convex functions are established using different approaches. We also introduce relative h-convex functions and is shown that rela...
متن کاملParameter Estimation in Spatial Generalized Linear Mixed Models with Skew Gaussian Random Effects using Laplace Approximation
Spatial generalized linear mixed models are used commonly for modelling non-Gaussian discrete spatial responses. We present an algorithm for parameter estimation of the models using Laplace approximation of likelihood function. In these models, the spatial correlation structure of data is carried out by random effects or latent variables. In most spatial analysis, it is assumed that rando...
متن کاملfragmentability and approximation
in chapter 1, charactrizations of fragmentability, which are obtained by namioka (37), ribarska (45) and kenderov-moors (32), are given. also the connection between fragmentability and its variants and other topics in banach spaces such as analytic space, the radone-nikodym property, differentiability of convex functions, kadec renorming are discussed. in chapter 2, we use game characterization...
15 صفحه اولEquilibrium existence under generalized convexity
We introduce, in the first part, the notion of weakly convex pair of correspondences, we give its economic interpretation, we state a fixed point and a selection theorem. Then, by using a tehnique based on a continuous selection, we prove existence theorems of quilibrium for an abstract economy. In the second part, we define the weakly biconvex correspondences, we prove a selection theorem and ...
متن کاملConvexity, Detection, and, Generalized f-divergences
The goal of multi-class classification problem is to find a discriminant function that minimizes the expectation of 0-1 loss function. However, minimizing 0-1 loss directly is often computationally intractable and practical algorithms are usually based on convex relaxations of 0-1 loss, say Φ, which is called the surrogate loss. In many applications, the covariates are either not available dire...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Approximation Theory
سال: 1968
ISSN: 0021-9045
DOI: 10.1016/0021-9045(68)90031-2