1 citations · 1 across the 5 of their papers we have counts for
5 papers
SLiSeS: Subsampled Line Search Spectral Gradient Method for Finite Sums
Stefania Bellavia, Nataša Krejić, Nataša Krklec Jerinkić +1
The spectral gradient method is known to be a powerful low-cost tool for solving large-scale optimization problems. In this paper, our goal is to exploit its advantages in the stoc…
A low-cost alternating projection approach for a continuous formulation of convex and cardinality constrained optimization
Nataša Krejić, Evelin H. M. Krulikovski, Marcos Raydan
We consider convex constrained optimization problems that also include a cardinality constraint. In general, optimization problems with cardinality constraints are difficult mathem…
Using first-order information in Direct Multisearch for multiobjective optimization
R. Andreani, A. L. Custódio, M. Raydan
Derivatives are an important tool for single-objective optimization. In fact, it is commonly accepted that derivative-based methods present a better performance than derivative-fre…
Geometrical inverse matrix approximation for least-squares problems and acceleration strategies
Jean-Paul Chehab, Marcos Raydan
We extend the geometrical inverse approximation approach for solving linear least-squares problems. For that we focus on the minimization of , where is a giv…
Geometrical inverse preconditioning for symmetric positive definite matrices
Jean-Paul Chehab, Marcos Raydan
We focus on inverse preconditioners based on minimizing , where is the preconditioned matrix and is symmetric and positive definite. We present and an…