Massimo Roma

904 total citations
36 papers, 580 citations indexed

About

Massimo Roma is a scholar working on Numerical Analysis, Computational Theory and Mathematics and Computational Mechanics. According to data from OpenAlex, Massimo Roma has authored 36 papers receiving a total of 580 indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Numerical Analysis, 24 papers in Computational Theory and Mathematics and 10 papers in Computational Mechanics. Recurrent topics in Massimo Roma's work include Advanced Optimization Algorithms Research (24 papers), Matrix Theory and Algorithms (22 papers) and Iterative Methods for Nonlinear Equations (10 papers). Massimo Roma is often cited by papers focused on Advanced Optimization Algorithms Research (24 papers), Matrix Theory and Algorithms (22 papers) and Iterative Methods for Nonlinear Equations (10 papers). Massimo Roma collaborates with scholars based in Italy, United States and Oman. Massimo Roma's co-authors include Stefano Lucidi, Philippe L. Toint, Nicholas I. M. Gould, Giovanni Fasano, Laura Palagi, Michael C. Ferris, Luca Paulon, Massimo Maurici, Francesco Rinaldi and Stephen G. Nash and has published in prestigious journals such as Composite Structures, Applied Mathematics and Computation and Annals of Operations Research.

In The Last Decade

Massimo Roma

34 papers receiving 510 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Massimo Roma Italy 13 372 337 174 67 57 36 580
Juan Peypouquet Chile 17 483 1.3× 753 2.2× 536 3.1× 105 1.6× 169 3.0× 32 1.2k
Giampaolo Liuzzi Italy 17 350 0.9× 452 1.3× 118 0.7× 219 3.3× 151 2.6× 58 836
Silvia Villa Italy 13 126 0.3× 139 0.4× 288 1.7× 164 2.4× 46 0.8× 48 564
Giovanni Fasano Italy 16 249 0.7× 333 1.0× 136 0.8× 159 2.4× 74 1.3× 61 684
Min Sun China 15 336 0.9× 231 0.7× 230 1.3× 59 0.9× 105 1.8× 76 655
M.K.H. Fan United States 12 144 0.4× 255 0.8× 47 0.3× 44 0.7× 819 14.4× 28 1.0k
Keyvan Amini Iran 16 490 1.3× 268 0.8× 206 1.2× 84 1.3× 90 1.6× 44 657
Ana Friedlander Brazil 17 567 1.5× 492 1.5× 268 1.5× 72 1.1× 98 1.7× 25 752
Shih-Ping Han United States 8 433 1.2× 396 1.2× 179 1.0× 50 0.7× 157 2.8× 13 688
Jan-J. Rückmann Germany 14 403 1.1× 459 1.4× 72 0.4× 30 0.4× 206 3.6× 33 683

Countries citing papers authored by Massimo Roma

Since Specialization
Citations

This map shows the geographic impact of Massimo Roma'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 Massimo Roma with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Massimo Roma more than expected).

Fields of papers citing papers by Massimo Roma

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Massimo Roma. 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 Massimo Roma. The network helps show where Massimo Roma may publish in the future.

Co-authorship network of co-authors of Massimo Roma

This figure shows the co-authorship network connecting the top 25 collaborators of Massimo Roma. A scholar is included among the top collaborators of Massimo Roma 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 Massimo Roma. Massimo Roma is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Fasano, Giovanni, et al.. (2025). Exploiting effective negative curvature directions via SYMMBK algorithm, in Newton–Krylov methods. Computational Optimization and Applications. 91(2). 617–647.
2.
Roma, Massimo, et al.. (2024). Managing low–acuity patients in an Emergency Department through simulation–based multiobjective optimization using a neural network metamodel. Health Care Management Science. 27(3). 415–435. 4 indexed citations
3.
Pillo, Gianni Di, et al.. (2023). A two-objective optimization of ship itineraries for a cruise company. 4OR. 21(4). 683–709. 2 indexed citations
4.
5.
Santis, Alberto De, et al.. (2022). A simulation-based optimization approach for the calibration of a discrete event simulation model of an emergency department. Annals of Operations Research. 320(2). 727–756. 10 indexed citations
6.
Leone, Renato De, Giovanni Fasano, Massimo Roma, & Yaroslav D. Sergeyev. (2020). Iterative Grossone-Based Computation of Negative Curvature Directions in Large-Scale Optimization. Journal of Optimization Theory and Applications. 186(2). 554–589. 12 indexed citations
7.
Fasano, Giovanni, et al.. (2020). Issues on the use of a modified Bunch and Kaufman decomposition for large scale Newton’s equation. Computational Optimization and Applications. 77(3). 627–651. 1 indexed citations
8.
Al‐Baali, Mehiddin, et al.. (2020). A Class of Approximate Inverse Preconditioners Based on Krylov-Subspace Methods for Large-Scale Nonconvex Optimization. SIAM Journal on Optimization. 30(3). 1954–1979. 1 indexed citations
9.
10.
Fasano, Giovanni, et al.. (2017). Preconditioned Nonlinear Conjugate Gradient methods based on a modified secant equation. Applied Mathematics and Computation. 318. 196–214. 13 indexed citations
11.
Fasano, Giovanni, et al.. (2017). An adaptive truncation criterion, for linesearch-based truncated Newton methods in large scale nonconvex optimization. Operations Research Letters. 46(1). 7–12. 13 indexed citations
12.
Scotta, Roberto, et al.. (2016). Membranes with embedded photovoltaic flexible cells: Structural and electrical performances under uniaxial and biaxial stresses. Composite Structures. 157. 111–120. 12 indexed citations
13.
Fasano, Giovanni & Massimo Roma. (2015). An estimation of the condition number for a class of indefinite preconditioned matrices. ARCA (Università Ca' Foscari Venezia). 1–19. 1 indexed citations
14.
Fasano, Giovanni & Massimo Roma. (2015). A novel class of approximate inverse preconditioners for large positive definite linear systems in optimization. Computational Optimization and Applications. 65(2). 399–429. 7 indexed citations
15.
Fasano, Giovanni & Massimo Roma. (2013). Preconditioning Newton–Krylov methods in nonconvex large scale optimization. Computational Optimization and Applications. 56(2). 253–290. 17 indexed citations
16.
Fasano, Giovanni & Massimo Roma. (2011). A Class of Preconditioners for Large Indefinite Linear Systems, as By-Product of Krylov Subspace Methods: Part II. SSRN Electronic Journal. 2 indexed citations
17.
Fasano, Giovanni & Massimo Roma. (2007). An Approximate Inverse Preconditioner in Truncated Newton Methods forLarge Scale Optimization. 1.
18.
Fasano, Giovanni & Massimo Roma. (2007). Iterative computation of negative curvature directions in large scale optimization. Computational Optimization and Applications. 38(1). 81–104. 17 indexed citations
19.
Ferris, Michael C., et al.. (1996). Nonmonotone curvilinear line search methods for unconstrained optimization. Computational Optimization and Applications. 6(2). 117–136. 30 indexed citations
20.
Piccioni, Mauro & Massimo Roma. (1990). Stochastic regularization of linear equations and the realization of Gaussian fields. Journal of Multivariate Analysis. 33(1). 143–150. 1 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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