We consider regularized stochastic learning and online optimization problems, where the objective function is the sum of two convex terms: one is the loss function of the learning...
This paper proposes the use of a new symmetry property based on proximity of the median moments in the wavelet domain. The method divides a given frame into 16 equally sized blocks...
Convex optimization problems arising in applications, possibly as approximations of intractable problems, are often structured and large scale. When the data are noisy, it is of i...
This paper is concerned with the study of a class of prox-penalization methods for solving variational inequalities of the form Ax + NC (x) 0 where H is a real Hilbert space, A : H...
Hedy Attouch, Marc-Olivier Czarnecki, Juan Peypouq...
We propose using the proximity distribution of vectorquantized local feature descriptors for object and category recognition. To this end, we introduce a novel "proximity dis...