Leandro da Fonseca Prudente

Orcid: 0000-0003-1098-4317

Affiliations:
  • Federal University of Goias, Institute of Mathematics and Statistics, Brazil


According to our database1, Leandro da Fonseca Prudente authored at least 17 papers between 2012 and 2023.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

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Bibliography

2023
Inexact gradient projection method with relative error tolerance.
Comput. Optim. Appl., March, 2023

2022
A Quasi-Newton Method with Wolfe Line Searches for Multiobjective Optimization.
J. Optim. Theory Appl., 2022

Globally convergent Newton-type methods for multiobjective optimization.
Comput. Optim. Appl., 2022

On the inexact scaled gradient projection method.
Comput. Optim. Appl., 2022

A study of Liu-Storey conjugate gradient methods for vector optimization.
Appl. Math. Comput., 2022

2021
Alternating conditional gradient method for convex feasibility problems.
Comput. Optim. Appl., 2021

Conditional gradient method for multiobjective optimization.
Comput. Optim. Appl., 2021

2020
Iteration-Complexity and Asymptotic Analysis of Steepest Descent Method for Multiobjective Optimization on Riemannian Manifolds.
J. Optim. Theory Appl., 2020

On the extension of the Hager-Zhang conjugate gradient method for vector optimization.
Comput. Optim. Appl., 2020

2019
A Wolfe Line Search Algorithm for Vector Optimization.
ACM Trans. Math. Softw., 2019

Gradient Method for Optimization on Riemannian Manifolds with Lower Bounded Curvature.
SIAM J. Optim., 2019

2018
Nonlinear Conjugate Gradient Methods for Vector Optimization.
SIAM J. Optim., 2018

2016
On the global convergence of the inexact semi-smooth Newton method for absolute value equation.
Comput. Optim. Appl., 2016

2015
Augmented Lagrangian methods for nonlinear programming with possible infeasibility.
J. Glob. Optim., 2015

Optimality properties of an Augmented Lagrangian method on infeasible problems.
Comput. Optim. Appl., 2015

2014
Augmented Lagrangians with possible infeasibility and finite termination for global nonlinear programming.
J. Glob. Optim., 2014

2012
Handling infeasibility in a large-scale nonlinear optimization algorithm.
Numer. Algorithms, 2012


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