World's Best Scientists 2026 revealed!

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Computer Science

D-Index
39
Citations
7442
World Ranking
9670
National Ranking
280

Mathematics

D-Index
39
Citations
7466
World Ranking
2163
National Ranking
60

Overview

Stefano Lucidi is affiliated with Sapienza University of Rome in Italy. Their research spans several intersecting fields including Computer Science, Mathematics, and Engineering, with a significant focus on Numerical Analysis, Computational Theory and Mathematics, and Control and Systems Engineering alongside Artificial Intelligence and Computational Mechanics.

Their primary research topics include Advanced Optimization Algorithms Research, Optimization and Variational Analysis, Advanced Multi-Objective Optimization Algorithms, Sparse and Compressive Sensing Techniques, Advanced Control Systems Optimization, Iterative Methods for Nonlinear Equations, and Metaheuristic Optimization Algorithms Research.

Lucidi has published in a variety of academic venues. Frequent publication outlets include:

  • arXiv (Cornell University)
  • Computational Optimization and Applications
  • Journal of Optimization Theory and Applications
  • Optimization Letters
  • Mathematics

Some recent papers authored or co-authored by Lucidi include:

  • "On the convergence of steepest descent methods for multiobjective optimization" (2020), Computational Optimization and Applications
  • "Hybridization of Multi-Objective Deterministic Particle Swarm with Derivative-Free Local Searches" (2020), Mathematics
  • "An algorithmic framework based on primitive directions and nonmonotone line searches for black-box optimization problems with integer variables" (2020), Mathematical Programming Computation
  • "Machine Learning-Based Classification to Disentangle EEG Responses to TMS and Auditory Input" (2023), Brain Sciences
  • "A simulation-based optimization approach for the calibration of a discrete event simulation model of an emergency department" (2022), Annals of Operations Research

Lucidi frequently collaborates with a selected group of co-authors, including:

  • Giampaolo Liuzzi
  • Francesco Rinaldi
  • Andrea Cristofari
  • Alberto De Santis
  • Marianna De Santis

The scientist's work primarily contributes to the development and improvement of optimization algorithms, including deterministic particle swarm techniques, multi-objective algorithms, and novel algorithmic frameworks for black-box optimization problems. Their research also explores applications of machine learning in neuroscience as well as simulation-based optimization for healthcare systems.

Best Publications

  • A nonmonotone line search technique for Newton's method

    L Grippo;F Lampariello;S Lucidi

  • Solving the Trust-Region Subproblem using the Lanczos Method

    Nicholas I. M. Gould;Stefano Lucidi;Massimo Roma;Philippe L. Toint

  • A globally convergent version of the Polak-Ribière conjugate gradient method

    L. Grippo;S. Lucidi

  • A truncated Newton method with nonmonotone line search for unconstrained optimization

    L. Grippo;F. Lampariello;S. Lucidi

  • A class of nonmonotone stabilization methods in unconstrained optimization

    L. Grippo;F. Lampariello;S. Lucidi

  • A Mathematical Programming Approach for the Solution of the Railway Yield Management Problem

    A. Ciancimino;G. Inzerillo;S. Lucidi;L. Palagi

  • Finite-Element-Based Multiobjective Design Optimization Procedure of Interior Permanent Magnet Synchronous Motors for Wide Constant-Power Region Operation

    F. Parasiliti;M. Villani;S. Lucidi;F. Rinaldi

  • Quadratically and superlinearly convergent algorithms for the solution of inequality constrained minimization problems

    F. Facchinei;S. Lucidi

  • On the Global Convergence of Derivative-Free Methods for Unconstrained Optimization

    Stefano Lucidi;Marco Sciandrone

  • New global optimization methods for ship design problems

    Emilio Fortunato Campana;Giampaolo Liuzzi;Stefano Lucidi;Daniele Peri

  • A Derivative-Free Algorithm for Bound Constrained Optimization

    Stefano Lucidi;Marco Sciandrone

  • New Classes of Globally Convexized Filled Functions for Global Optimization

    S. Lucidi;V. Piccialli

  • A smooth method for the finite minimax problem

    G. Di Pillo;L. Grippo;S. Lucidi

  • A New Version of the Price‘s Algorithm for Global Optimization

    P. Brachetti;M. De Felice Ciccoli;G. Di Pillo;S. Lucidi

  • Multiobjective optimization techniques for the design of induction motors

    G. Liuzzi;S. Lucidi;F. Parasiliti;M. Villani

  • Objective-derivative-free methods for constrained optimization

    Stefano Lucidi;Marco Sciandrone;Paul Tseng

  • An Augmented Lagrangian Function with Improved Exactness Properties

    Gianni Di Pillo;Stefano Lucidi

  • Random tunneling by means of acceptance-rejection sampling for global optimization

    S. Lucidi;M. Piccioni

  • Sequential Penalty Derivative-Free Methods for Nonlinear Constrained Optimization

    Giampaolo Liuzzi;Stefano Lucidi;Marco Sciandrone

  • New Results on a Continuously Differentiable Exact Penalty Function

    Stefano Lucidi

  • SOLVING THE TRUST-REGION SUBPROBLEM USING THE

    Nicholas I. M. Gould;Stefano Lucidi;Massimo Roma;Philippe L. Toint

Frequent Co-Authors

Francisco Facchinei
Francisco Facchinei Sapienza University of Rome
Nicholas I. M. Gould
Nicholas I. M. Gould University of Oxford
Philippe L. Toint
Philippe L. Toint University of Namur
Michael C. Ferris
Michael C. Ferris University of Wisconsin–Madison
Chih-Jen Lin
Chih-Jen Lin National Taiwan University
Luís Nunes Vicente
Luís Nunes Vicente Lehigh University
Paul Tseng
Paul Tseng University of Washington
Maria Antonietta Ricci
Maria Antonietta Ricci Roma Tre University

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