Angeles Martinez

According to our database, Angeles Martinez authored at least 39 papers between 2003 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

A non-monotone trust-region method with noisy oracles and additional sampling.

Natasa Krejic, Natasa Krklec Jerinkic, Angeles Martinez, Mahsa Yousefi

Comput. Optim. Appl., September, 2024

Some preconditioning techniques for a class of double saddle point problems.

Fariba Balani Bakrani, Luca Bergamaschi, Ángeles Martínez, Masoud Hajarian

Numer. Linear Algebra Appl., August, 2024

Parallel-in-Time Solver for the All-at-Once Runge-Kutta Discretization.

Santolo Leveque, Luca Bergamaschi, Ángeles Martínez, John W. Pearson

SIAM J. Matrix Anal. Appl., 2024

Spectral analysis of block preconditioners for double saddle-point linear systems with application to PDE-constrained optimization.

Luca Bergamaschi, Angeles Martinez, John W. Pearson, Andreas Potschka

CoRR, 2024

2023

Parallel matrix-free polynomial preconditioners with application to flow simulations in discrete fracture networks.

Luca Bergamaschi, Massimiliano Ferronato, Giovanni Isotton, Carlo Janna, Angeles Martinez

Comput. Math. Appl., September, 2023

Fast Iterative Solver for the All-at-Once Runge-Kutta Discretization.

Santolo Leveque, Luca Bergamaschi, Ángeles Martínez, John W. Pearson

CoRR, 2023

Deep Neural Networks Training by Stochastic Quasi-Newton Trust-Region Methods.

Mahsa Yousefi, Angeles Martinez

Algorithms, 2023

2022

Strategies of Preconditioner Updates for Sequences of Linear Systems Associated with the Neutron Diffusion.

Amanda Carreño, Luca Bergamaschi, Angeles Martinez, Damián Ginestar, Antoni Vidal-Ferràndiz, Gumersindo Verdú

Comput. Math. Methods, September, 2022

On the efficiency of Stochastic Quasi-Newton Methods for Deep Learning.

Mahsa Yousefi, Angeles Martinez

CoRR, 2022

A stochastic nonmonotone trust-region training algorithm for image classification.

Mahsa Yousefi, Ángeles Martínez Calomardo

Proceedings of the 16th International Conference on Signal-Image Technology & Internet-Based Systems, 2022

A Stochastic Modified Limited Memory BFGS for Training Deep Neural Networks.

Mahsa Yousefi, Ángeles Martínez Calomardo

Proceedings of the Intelligent Computing, 2022

2021

Parallel Newton-Chebyshev polynomial preconditioners for the conjugate gradient method.

Luca Bergamaschi, Ángeles Martínez Calomardo

Comput. Math. Methods, November, 2021

A new preconditioning approach for an interior point-proximal method of multipliers for linear and convex quadratic programming.

Luca Bergamaschi, Jacek Gondzio, Ángeles Martínez, John W. Pearson, Spyridon Pougkakiotis

Numer. Linear Algebra Appl., 2021

2020

Quadrature-based polynomial optimization.

Angeles Martinez, Federico Piazzon, Alvise Sommariva, Marco Vianello

Optim. Lett., 2020

Compact quasi-Newton preconditioners for symmetric positive definite linear systems.

Luca Bergamaschi, José Marín, Ángeles Martínez

Numer. Linear Algebra Appl., 2020

Compact Quasi-Newton preconditioners for SPD linear systems.

Luca Bergamaschi, José Marín, Angeles Martinez

CoRR, 2020

2019

RBF-Based Partition of Unity Methods for Elliptic PDEs: Adaptivity and Stability Issues Via Variably Scaled Kernels.

Stefano De Marchi, Ángeles Martínez, Emma Perracchione, M. Rossini

J. Sci. Comput., 2019

Fast and stable rational RBF-based partition of unity interpolation.

Stefano De Marchi, Ángeles Martínez, Emma Perracchione

J. Comput. Appl. Math., 2019

Spectral preconditioners for the efficient numerical solution of a continuous branched transport model.

Luca Bergamaschi, Enrico Facca, Angeles Martinez, Mario Putti

J. Comput. Appl. Math., 2019

2018

BFGS-like updates of constraint preconditioners for sequences of KKT linear systems in quadratic programming.

Luca Bergamaschi, Valentina De Simone, Daniela di Serafino, Ángeles Martínez

Numer. Linear Algebra Appl., 2018

2017

Two-stage spectral preconditioners for iterative eigensolvers.

Luca Bergamaschi, Angeles Martinez

Numer. Linear Algebra Appl., 2017

Spectral Acceleration of Parallel Iterative Eigensolvers for Large Scale Scientific Computing.

Luca Bergamaschi, Ángeles Martínez

Proceedings of the Parallel Computing is Everywhere, 2017

2016

Tuned preconditioners for the eigensolution of large SPD matrices arising in engineering problems.

Angeles Martinez

Numer. Linear Algebra Appl., 2016

2015

Efficiently preconditioned inexact Newton methods for large symmetric eigenvalue problems.

Luca Bergamaschi, Angeles Martinez

Optim. Methods Softw., 2015

2013

Low-rank update of preconditioners for the nonlinear Richards equation.

Luca Bergamaschi, Rafael Bru, Angeles Martinez, José Mas, Mario Putti

Math. Comput. Model., 2013

Parallel RFSAI-BFGS Preconditioners for Large Symmetric Eigenproblems.

Luca Bergamaschi, Angeles Martinez

J. Appl. Math., 2013

2012

Banded target matrices and recursive FSAI for parallel preconditioning.

Luca Bergamaschi, Angeles Martinez

Numer. Algorithms, 2012

Parallel Rayleigh Quotient Optimization with FSAI-Based Preconditioning.

Luca Bergamaschi, Angeles Martinez, Giorgio Pini

J. Appl. Math., 2012

Quasi-Newton acceleration of ILU preconditioners for nonlinear two-phase flow equations in porous media.

Luca Bergamaschi, Rafael Bru, Angeles Martinez, Mario Putti

Adv. Eng. Softw., 2012

2011

Low-rank update of preconditioners for the inexact Newton method with SPD Jacobian.

Luca Bergamaschi, Rafael Bru, Angeles Martinez

Math. Comput. Model., 2011

FSAI-based parallel Mixed Constraint Preconditioners for saddle point problems arising in geomechanics.

Luca Bergamaschi, Angeles Martinez

J. Comput. Appl. Math., 2011

Parallel Inexact Constraint Preconditioners for Saddle Point Problems.

Luca Bergamaschi, Angeles Martinez

Proceedings of the Euro-Par 2011 Parallel Processing - 17th International Conference, 2011

2009

A massively parallel exponential integrator for advection-diffusion models.

Angeles Martinez, Luca Bergamaschi, Marco Caliari, Marco Vianello

J. Comput. Appl. Math., 2009

2006

Parallel preconditioned conjugate gradient optimization of the Rayleigh quotient for the solution of sparse eigenproblems.

Luca Bergamaschi, Angeles Martinez, Giorgio Pini

Appl. Math. Comput., 2006

Comparing Leja and Krylov Approximations of Large Scale Matrix Exponentials.

Luca Bergamaschi, Marco Caliari, Angeles Martinez, Marco Vianello

Proceedings of the Computational Science, 2006

2005

A Parallel Exponential Integrator for Large-Scale Discretizations of Advection-Diffusion Models.

Luca Bergamaschi, Marco Caliari, Angeles Martinez, Marco Vianello

Proceedings of the Recent Advances in Parallel Virtual Machine and Message Passing Interface, 2005

2004

Parallel Acceleration of Krylov Solvers by Factorized Approximate Inverse Preconditioners.

Luca Bergamaschi, Angeles Martinez

Proceedings of the High Performance Computing for Computational Science, 2004

2003

Eigenanalysis of Finite Element 3D Flow Models by Parallel Jacobi-Davidson.

Luca Bergamaschi, Angeles Martinez, Giorgio Pini, Flavio Sartoretto

Proceedings of the Recent Advances in Parallel Virtual Machine and Message Passing Interface,10th European PVM/MPI Users' Group Meeting, Venice, Italy, September 29, 2003

Parallel Solution of Sparse Eigenproblems by Simultaneous Rayleigh Quotient Optimization with FSAI preconditioning.

Luca Bergamaschi, Angeles Martinez, Giorgio Pini

Proceedings of the Parallel Computing: Software Technology, 2003