19 citations · 66 across the 12 of their papers we have counts for
7 papers · 1 filter
A Markovian Incremental Stochastic Subgradient Algorithm
Rafael Massambone, Eduardo F. Costa, Elias S. Helou
A stochastic incremental subgradient algorithm for the minimization of a sum of convex functions is introduced. The method sequentially uses partial subgradient information and the…
Fast Proximal Gradient Methods for Nonsmooth Convex Optimization for Tomographic Image Reconstruction
Elias S. Helou, Marcelo V. W. Zibetti, Gabor T. Herman
The Fast Proximal Gradient Method (FPGM) and the Monotone FPGM (MFPGM) for minimization of nonsmooth convex functions are introduced and applied to tomographic image reconstruction…
Derivative-Free Superiorization: Principle and Algorithm
Yair Censor, Edgar Garduño, Elias S. Helou +1
The superiorization methodology is intended to work with input data of constrained minimization problems, that is, a target function and a set of constraints. However, it is based…
A Fast Gradient and Function Sampling Method for Finite Max-Functions
Elias S. Helou, Sandra A. Santos, Lucas E. A. Simões
This paper tackles the unconstrained minimization of a class of nonsmooth and nonconvex functions that can be written as finite max-functions. A gradient and function-based samplin…
-Subgradient Algorithms for Bilevel Convex Optimization
Elias Salomão Helou, Lucas Eduardo Azevedo Simões
This paper introduces and studies the convergence properties of a new class of explicit -subgradient methods for the task of minimizing a convex function over the set of minimiz…
String-Averaging Incremental Subgradients for Constrained Convex Optimization with Applications to Reconstruction of Tomographic Images
Rafael Massambone de Oliveira, Elias Salomão Helou, Eduardo Fontoura Costa
We present a method for non-smooth convex minimization which is based on subgradient directions and string-averaging techniques. In this approach, the set of available data is spli…