2024
From regression models to machine learning approaches for long term Bitcoin price forecast.
Ann. Oper. Res., May, 2024
2021
Polarity and conjugacy for quadratic hypersurfaces: A unified framework with recent advances.
J. Comput. Appl. Math., 2021
Dense conjugate initialization for deterministic PSO in applications: ORTHOinit+.
Appl. Soft Comput., 2021
A novel hybrid PSO-based metaheuristic for costly portfolio selection problems.
Ann. Oper. Res., 2021
2020
A PSO-Based Framework for Nonsmooth Portfolio Selection Problems.
Proceedings of the Neural Advances in Processing Nonlinear Dynamic Signals, 2020
A Class of Approximate Inverse Preconditioners Based on Krylov-Subspace Methods for Large-Scale Nonconvex Optimization.
SIAM J. Optim., 2020
Iterative Grossone-Based Computation of Negative Curvature Directions in Large-Scale Optimization.
J. Optim. Theory Appl., 2020
Issues on the use of a modified Bunch and Kaufman decomposition for large scale Newton's equation.
Comput. Optim. Appl., 2020
2018
An adaptive truncation criterion, for linesearch-based truncated Newton methods in large scale nonconvex optimization.
Oper. Res. Lett., 2018
Planar methods and grossone for the Conjugate Gradient breakdown in nonlinear programming.
Comput. Optim. Appl., 2018
Preconditioned Nonlinear Conjugate Gradient methods based on a modified secant equation.
Appl. Math. Comput., 2018
How Grossone Can Be Helpful to Iteratively Compute Negative Curvature Directions.
Proceedings of the Learning and Intelligent Optimization - 12th International Conference, 2018
2017
Novel preconditioners based on quasi-Newton updates for nonlinear conjugate gradient methods.
Optim. Lett., 2017
Exploiting damped techniques for nonlinear conjugate gradient methods.
Math. Methods Oper. Res., 2017
Conjugate Direction Methods and Polarity for Quadratic Hypersurfaces.
J. Optim. Theory Appl., 2017
2016
A novel class of approximate inverse preconditioners for large positive definite linear systems in optimization.
Comput. Optim. Appl., 2016
Parameter selection in synchronous and asynchronous deterministic particle swarm optimization for ship hydrodynamics problems.
Appl. Soft Comput., 2016
Dense Orthogonal Initialization for Deterministic PSO: ORTHOinit+.
Proceedings of the Advances in Swarm Intelligence, 7th International Conference, 2016
2015
Globally Convergent Hybridization of Particle Swarm Optimization Using Line Search-Based Derivative-Free Techniques.
Proceedings of the Recent Advances in Swarm Intelligence and Evolutionary Computation, 2015
A Framework of Conjugate Direction Methods for Symmetric Linear Systems in Optimization.
J. Optim. Theory Appl., 2015
2014
A Linesearch-Based Derivative-Free Approach for Nonsmooth Constrained Optimization.
SIAM J. Optim., 2014
A Proposal of PSO Particles' Initialization for Costly Unconstrained Optimization Problems: ORTHOinit.
Proceedings of the Advances in Swarm Intelligence - 5th International Conference, 2014
2013
Preconditioning Newton-Krylov methods in nonconvex large scale optimization.
Comput. Optim. Appl., 2013
Particle Swarm Optimization with non-smooth penalty reformulation, for a complex portfolio selection problem.
Appl. Math. Comput., 2013
Initial Particles Position for PSO, in Bound Constrained Optimization.
Proceedings of the Advances in Swarm Intelligence, 4th International Conference, 2013
2010
Dynamic analysis for the selection of parameters and initial population, in particle swarm optimization.
J. Glob. Optim., 2010
2009
On the geometry phase in model-based algorithms for derivative-free optimization.
Optim. Methods Softw., 2009
A nonmonotone truncated Newton-Krylov method exploiting negative curvature directions, for large scale unconstrained optimization.
Optim. Lett., 2009
2007
Iterative computation of negative curvature directions in large scale optimization.
Comput. Optim. Appl., 2007
2006
A Truncated Nonmonotone Gauss-Newton Method for Large-Scale Nonlinear Least-Squares Problems.
Comput. Optim. Appl., 2006
2004
Conjugate gradient (CG)-type method for the solution of Newton's equation within optimization frameworks.
Optim. Methods Softw., 2004