Asymptotic analysis of a structure-preserving integrator for damped Hamiltonian systems
AbstractThe present work deals with the numerical long-time integration of damped Hamiltonian systems. The method that we analyze combines a…
Optimization
The long time behavior and the rate of convergence of symplectic convex algorithms obtained via splitting discretizations of inertial damping systems
AbstractIn this paper we propose new numerical algorithms in the setting of unconstrained optimization problems and we give proof for…
An extension of the second order dynamical system that models Nesterov’s convex gradient method
Abstract In this paper we deal with a general second order continuous dynamical system associated to a convex minimization problem…
New optimization algorithms for neural network training using operator splitting techniques
AbstractWe present a new type of optimization algorithms, adapted for neural network training. These algorithms are based upon sequential operator…
A gradient-type algorithm with backward inertial steps associated to a nonconvex minimization problem
AbstractWe investigate an algorithm of gradient type with a backward inertial step in connection with the minimization of a nonconvex…
Publications
Kantorovich type operators associated with Jain-Markov operators
AbstractThis note focuses on a sequence of linear positive operators of integral type in the sense of Kantorovich. The construction is based on a class of discrete operators representing a…
Read More Asymptotic analysis of a structure-preserving integrator for damped Hamiltonian systems
AbstractThe present work deals with the numerical long-time integration of damped Hamiltonian systems. The method that we analyze combines a specific Strang splitting, that separates linear dissipative effects from conservative…
Read More Approximation of some classes of functions by Landau type operators
Abstract This paper aims to highlight a class of integral linear and positive operators of Landau type which have affine functions as fixed points. We focus to reveal approximation properties…
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