Giampaolo Liuzzi

1.2k total citations
58 papers, 836 citations indexed

About

Giampaolo Liuzzi is a scholar working on Computational Theory and Mathematics, Numerical Analysis and Control and Systems Engineering. According to data from OpenAlex, Giampaolo Liuzzi has authored 58 papers receiving a total of 836 indexed citations (citations by other indexed papers that have themselves been cited), including 37 papers in Computational Theory and Mathematics, 31 papers in Numerical Analysis and 10 papers in Control and Systems Engineering. Recurrent topics in Giampaolo Liuzzi's work include Advanced Optimization Algorithms Research (31 papers), Advanced Multi-Objective Optimization Algorithms (19 papers) and Optimization and Variational Analysis (8 papers). Giampaolo Liuzzi is often cited by papers focused on Advanced Optimization Algorithms Research (31 papers), Advanced Multi-Objective Optimization Algorithms (19 papers) and Optimization and Variational Analysis (8 papers). Giampaolo Liuzzi collaborates with scholars based in Italy, United States and Austria. Giampaolo Liuzzi's co-authors include Stefano Lucidi, Veronica Piccialli, Francesco Rinaldi, Marco Sciandrone, Emilio F. Campana, Matteo Diez, Andrea Serani, Marco Villani, Giovanni Fasano and F. Parasiliti and has published in prestigious journals such as BMC Bioinformatics, Mathematical Programming and IEEE Transactions on Magnetics.

In The Last Decade

Giampaolo Liuzzi

53 papers receiving 805 citations

Peers

Giampaolo Liuzzi

CTM

CSE

Giovanni Fasano Italy

Dmitri E. Kvasov Italy

Joerg Gablonsky United States

Günter Mayer Germany

Davide Serafini United States

Frank E. Curtis United States

J. E. Dennis United States

C. J. Price New Zealand

Hande Y. Benson United States

Bart G. van Bloemen Waanders United States

Giovanni Fasano Italy

Giampaolo Liuzzi

333 ×0.7

CTM

249 ×0.7

159 ×0.7

74 ×0.5

CSE

120 ×1.0

Citations per year, relative to Giampaolo Liuzzi Giampaolo Liuzzi (= 1×) peers Giovanni Fasano

Countries citing papers authored by Giampaolo Liuzzi

Since Specialization

Citations

This map shows the geographic impact of Giampaolo Liuzzi's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Giampaolo Liuzzi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Giampaolo Liuzzi more than expected).

Fields of papers citing papers by Giampaolo Liuzzi

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Giampaolo Liuzzi. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Giampaolo Liuzzi. The network helps show where Giampaolo Liuzzi may publish in the future.

Co-authorship network of co-authors of Giampaolo Liuzzi

This figure shows the co-authorship network connecting the top 25 collaborators of Giampaolo Liuzzi. A scholar is included among the top collaborators of Giampaolo Liuzzi based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Giampaolo Liuzzi. Giampaolo Liuzzi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Dilaghi, Emanuele, Elisabetta Cesaroni, Michela Silvestri, et al.. (2025). Exploring the potential of artificial intelligence in assessing the risk of gastric neoplastic lesions in patients with corpus atrophic gastritis. Gastric Cancer. 29(1). 159–168.

Liuzzi, Giampaolo, et al.. (2025). Convergence of ease-controlled random reshuffling gradient algorithms under Lipschitz smoothness. Computational Optimization and Applications. 91(2). 933–971.

Liuzzi, Giampaolo, et al.. (2025). An interior point method for nonlinear constrained derivative-free optimization. Optimization methods & software. 40(3). 611–649.

Liuzzi, Giampaolo, et al.. (2024). Worst Case Complexity Bounds for Linesearch-Type Derivative-Free Algorithms. Journal of Optimization Theory and Applications. 203(1). 419–454. 1 indexed citations

Gaudioso, Manlio, Giampaolo Liuzzi, & Stefano Lucidi. (2023). A clustering heuristic to improve a derivative-free algorithm for nonsmooth optimization. Optimization Letters. 18(1). 57–71. 1 indexed citations

Liuzzi, Giampaolo, et al.. (2022). Derivative-free methods for mixed-integer nonsmooth constrained optimization. Computational Optimization and Applications. 82(2). 293–327. 8 indexed citations

Liuzzi, Giampaolo, et al.. (2021). Computing mixed strategies equilibria in presence of switching costs by the solution of nonconvex QP problems. IRIS Research product catalog (Sapienza University of Rome). 7 indexed citations

Bianchi, Luigi, et al.. (2021). Improving P300 Speller performance by means of optimization and machine learning. Cineca Institutional Research Information System (Tor Vergata University). 9 indexed citations

Liuzzi, Giampaolo, Stefano Lucidi, Francesco Rinaldi, & L. N. Vicente. (2019). Trust-Region Methods for the Derivative-Free Optimization of Nonsmooth Black-Box Functions. SIAM Journal on Optimization. 29(4). 3012–3035. 13 indexed citations

10.

Liuzzi, Giampaolo, et al.. (2017). An implicit filtering algorithm for derivative-free multiobjective optimization with box constraints. Computational Optimization and Applications. 69(2). 267–296. 23 indexed citations

11.

Liuzzi, Giampaolo & Klaus Truemper. (2017). Parallelized hybrid optimization methods for nonsmooth problems using NOMAD and linesearch. Computational and Applied Mathematics. 37(3). 3172–3207. 2 indexed citations

12.

Pillo, Gianni Di, Giampaolo Liuzzi, Stefano Lucidi, Veronica Piccialli, & Francesco Rinaldi. (2016). A DIRECT-type approach for derivative-free constrained global optimization. Computational Optimization and Applications. 65(2). 361–397. 19 indexed citations

13.

Lampariello, F. & Giampaolo Liuzzi. (2015). A filling function method for unconstrained global optimization. IRIS Research product catalog (Sapienza University of Rome). 3 indexed citations

14.

Liuzzi, Giampaolo, Stefano Lucidi, & Veronica Piccialli. (2015). Exploiting derivative-free local searches in DIRECT-type algorithms for global optimization. Computational Optimization and Applications. 65(2). 449–475.

15.

Bertolazzi, Paola, Concettina Guerra, & Giampaolo Liuzzi. (2014). Predicting protein-ligand and protein-peptide interfaces. IRIS Research product catalog (Sapienza University of Rome). 11 indexed citations

16.

Fasano, Giovanni, Giampaolo Liuzzi, Stefano Lucidi, & Francesco Rinaldi. (2013). A Linesearch-based Derivative-free Approach for Nonsmooth Optimization. ARCA (Università Ca' Foscari Venezia). 1–32. 1 indexed citations

17.

Bertolazzi, Paola, Concettina Guerra, & Giampaolo Liuzzi. (2010). A global optimization algorithm for protein surface alignment. BMC Bioinformatics. 11(1). 488–488. 13 indexed citations

18.

Pillo, Gianni Di, Giampaolo Liuzzi, & Stefano Lucidi. (2010). An exact penalty-Lagrangian approach for large-scale nonlinear programming. Optimization. 60(1-2). 223–252. 7 indexed citations

19.

Liuzzi, Giampaolo, et al.. (2010). A decomposition algorithm for unconstrained optimization problems with partial derivative information. Optimization Letters. 6(3). 437–450. 1 indexed citations

20.

Liuzzi, Giampaolo, Stefano Lucidi, & Veronica Piccialli. (2008). A DIRECT-based approach exploiting local minimizations for the solution of large-scale global optimization problems. Computational Optimization and Applications. 45(2). 353–375. 49 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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