scholarly journals Study on Generalized Directional Differentiability Problems of Fuzzy Mappings

2020 ◽  
Author(s):  
Yu-e Bao ◽  
Tingting Li ◽  
Linfen Zhang
Keyword(s):  
Partial Derivative ◽  
Directional Derivatives ◽  
Fuzzy Mapping ◽  
Directional Differentiability ◽  
Coordinate Axis ◽  
Derivatives Of ◽  
Interval Valued

This paper discusses the gH-directional differentiability of fuzzy mappings, and proposes the concept of gH-directional differentiability of fuzzy mappings. Based on the concept of gH-directional differentiability of interval-valued mappings and its related properties, two properties of gH-directional differentiability fuzzy mappings are proposed. At the same time, the relation between gH-differentiability and gH-directional differentiability for a fuzzy mapping is discussed, and it is proved that both gH-derivative and gH-partial derivative are directional derivatives of fuzzy mappings in the direction of the coordinate axis.

On second-order directional derivatives of value functions

Optimization ◽  
2013 ◽  
Vol 64 (2) ◽  
pp. 389-407 ◽  
Author(s):  
L. Minchenko ◽  
A. Tarakanov
Keyword(s):  
Second Order ◽  
Directional Derivatives ◽  
Value Functions ◽  
Derivatives Of

scholarly journals ASYMPTOTICALLY EFFICIENT ESTIMATION OF WEIGHTED AVERAGE DERIVATIVES WITH AN INTERVAL CENSORED VARIABLE

2016 ◽  
Vol 33 (5) ◽  
pp. 1218-1241 ◽  
Author(s):  
Hiroaki Kaido
Keyword(s):  
Support Function ◽  
Weighted Average ◽  
Interval Data ◽  
Efficient Estimation ◽  
Outcome Variable ◽  
Nonparametric Bounds ◽  
Regression Functions ◽  
Semiparametric Efficiency Bound ◽  
Derivatives Of ◽  
Interval Valued

This paper studies the identification and estimation of weighted average derivatives of conditional location functionals including conditional mean and conditional quantiles in settings where either the outcome variable or a regressor is interval-valued. Building on Manski and Tamer (2002, Econometrica 70(2), 519–546) who study nonparametric bounds for mean regression with interval data, we characterize the identified set of weighted average derivatives of regression functions. Since the weighted average derivatives do not rely on parametric specifications for the regression functions, the identified set is well-defined without any functional-form assumptions. Under general conditions, the identified set is compact and convex and hence admits characterization by its support function. Using this characterization, we derive the semiparametric efficiency bound of the support function when the outcome variable is interval-valued. Using mean regression as an example, we further demonstrate that the support function can be estimated in a regular manner by a computationally simple estimator and that the efficiency bound can be achieved.

A Method of Solving a Class of CIV Boundary Value Problems

1992 ◽  
Vol 35 (3) ◽  
pp. 371-375
Author(s):  
Nezam Iraniparast
Keyword(s):  
Green’S Function ◽  
Boundary Value Problems ◽  
Green's Function ◽  
Boundary Value ◽  
Directional Derivatives ◽  
Derivatives Of

AbstractA method will be introduced to solve problems utt — uss = h(s, t), u(t,t) - u(1+t,1 - t), u(s,0) = g(s), u(1,1) = 0 and for (s, t) in the characteristic triangle R = {(s,t) : t ≤ s ≤ 2 — t, 0 ≤ t ≤ 1}. Here represent the directional derivatives of u in the characteristic directions e1 = (— 1, — 1) and e2 = (1, — 1), respectively. The method produces the symmetric Green's function of Kreith [1] in both cases.

On Derivatives and Norms of Generalized Matrix Functions and Respective Symmetric Powers

2015 ◽  
Vol 30 ◽  
Author(s):  
Sonia Carvalho ◽  
Pedro Freitas
Keyword(s):  
Symmetric Group ◽  
Symmetric Tensor ◽  
Directional Derivatives ◽  
Matrix Functions ◽  
Tensor Power ◽  
Symmetric Powers ◽  
Generalized Matrix ◽  
Derivatives Of

In recent papers, S. Carvalho and P. J. Freitas obtained formulas for directional derivatives, of all orders, of the immanant and of the m-th $\xi$-symmetric tensor power of an operator and a matrix, when $\xi$ is a character of the full symmetric group. The operator bound norm of these derivatives was also calculated. In this paper similar results are established for generalized matrix functions and for every symmetric tensor power.

The second-order directional derivatives of singular values

2013 ◽  
Vol 43 (2) ◽  
pp. 121-136
Author(s):  
LiWei ZHANG ◽  
XianTao XIAO ◽  
Ning ZHANG
Keyword(s):  
Singular Values ◽  
Second Order ◽  
Directional Derivatives ◽  
Derivatives Of
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