scholarly journals On the domain of oddness of an infimal convolution

2011 ◽  
Vol 12 (1) ◽  
pp. 31 ◽  
Author(s):  
Tamás Glavosits ◽  
Csaba Kézi
Keyword(s):  
Infimal Convolution

scholarly journals A formula on the subdifferential of the deconvolution of convex functions

1993 ◽  
Vol 47 (2) ◽  
pp. 333-340 ◽  
Author(s):  
M. Volle
Keyword(s):  
Convex Functions ◽  
Infimal Convolution ◽  
Parallel Sum ◽  
Similar Formula

It is known that, under suitable assumptions, the subdifferential ∂(f □ g) of the infimal convolution of two convex functions f and g coincides with the parallel sum of ∂ f and ∂ g. We prove that a similar formula holds for the subdifferential of the deconvolution of two convex functions: under appropriate hypothesis it coincides with the parallel star-difference of the sub-differentials of the functions.

scholarly journals Infimal Convolution of Data Discrepancies for Mixed Noise Removal

2017 ◽  
Vol 10 (3) ◽  
pp. 1196-1233 ◽  
Author(s):  
Luca Calatroni ◽  
Juan Carlos De Los Reyes ◽  
Carola-Bibiane Schönlieb
Keyword(s):  
Noise Removal ◽  
Infimal Convolution ◽  
Mixed Noise ◽  
Mixed Noise Removal

scholarly journals Infimal convolution, c-subdifferentiability, and Fenchel duality in evenly convex optimization

Top ◽  
2011 ◽  
Vol 20 (2) ◽  
pp. 375-396 ◽  
Author(s):  
M. D. Fajardo ◽  
J. Vicente-Pérez ◽  
M. M. L. Rodríguez
Keyword(s):  
Convex Optimization ◽  
Fenchel Duality ◽  
Infimal Convolution

scholarly journals Subdifferentiability of infimal convolution on Banach couples

2015 ◽  
Vol 52 (2) ◽  
pp. 311-326
Author(s):  
Natan Kruglyak ◽  
Japhet Niyobuhungiro
Keyword(s):  
Infimal Convolution

scholarly journals Stable Zero Lagrange Duality for DC Conic Programming

2012 ◽  
Vol 2012 ◽  
pp. 1-17
Author(s):  
D. H. Fang
Keyword(s):  
Constraint Qualification ◽  
Duality Gap ◽  
Conic Programming ◽  
Locally Convex Spaces ◽  
Conjugate Functions ◽  
Infimal Convolution ◽  
Lagrange Duality ◽  
Convex Constraint ◽  
Dc Function ◽  
Locally Convex

We consider the problems of minimizing a DC function under a cone-convex constraint and a set constraint. By using the infimal convolution of the conjugate functions, we present a new constraint qualification which completely characterizes the Farkas-type lemma and the stable zero Lagrange duality gap property for DC conical programming problems in locally convex spaces.

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