F. Lampariello

2.1k total citations · 1 hit paper
26 papers, 1.5k citations indexed

About

F. Lampariello is a scholar working on Computational Theory and Mathematics, Numerical Analysis and Molecular Biology. According to data from OpenAlex, F. Lampariello has authored 26 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Computational Theory and Mathematics, 12 papers in Numerical Analysis and 5 papers in Molecular Biology. Recurrent topics in F. Lampariello's work include Advanced Optimization Algorithms Research (12 papers), Iterative Methods for Nonlinear Equations (8 papers) and Matrix Theory and Algorithms (8 papers). F. Lampariello is often cited by papers focused on Advanced Optimization Algorithms Research (12 papers), Iterative Methods for Nonlinear Equations (8 papers) and Matrix Theory and Algorithms (8 papers). F. Lampariello collaborates with scholars based in Italy, Jordan and United States. F. Lampariello's co-authors include Luigi Grippo, Stefano Lucidi, Marco Sciandrone, Osvaldo Maria Grasselli, Antonella Aiello, Giovanni Fasano, Marcello Spanò, F. Mauro, Giampaolo Liuzzi and Gianni Di Pillo and has published in prestigious journals such as Journal of the Franklin Institute, SIAM Journal on Numerical Analysis and International Journal of Control.

In The Last Decade

F. Lampariello

24 papers receiving 1.4k citations

Hit Papers

A Nonmonotone Line Search Technique for Newton’s Method 1986 2026 1999 2012 1986 250 500 750
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. A Nonmonotone Line Search Technique for Newton’s Method
    1986 838 citations Luigi Grippo, F. Lampariello et al. profile →

Peers

F. Lampariello
A. A. Goldstein United States
Luigi Grippo Italy
S.-P. Han United States
Gerald A. Shultz United States
Tjalling Ypma United States
Qingbiao Wu China
G. T. Timmer Netherlands
I. D. Coope New Zealand
David Juedes United States
M. C. Bartholomew‐Biggs United Kingdom
A. A. Goldstein United States
F. Lampariello
Citations per year, relative to F. Lampariello F. Lampariello (= 1×) peers A. A. Goldstein

Countries citing papers authored by F. Lampariello

Since Specialization
Citations

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

Fields of papers citing papers by F. Lampariello

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of F. Lampariello

This figure shows the co-authorship network connecting the top 25 collaborators of F. Lampariello. A scholar is included among the top collaborators of F. Lampariello 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 F. Lampariello. F. Lampariello 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.
Lampariello, F. & Giampaolo Liuzzi. (2015). A filling function method for unconstrained global optimization. IRIS Research product catalog (Sapienza University of Rome). 3 indexed citations
2.
Lampariello, F. & Giampaolo Liuzzi. (2014). Global Optimization of Protein–peptide Docking by a Filling Function Method. Journal of Optimization Theory and Applications. 164(3). 1090–1108. 4 indexed citations
3.
4.
Fasano, Giovanni, F. Lampariello, & Marco Sciandrone. (2006). A Truncated Nonmonotone Gauss-Newton Method for Large-Scale Nonlinear Least-Squares Problems. Computational Optimization and Applications. 34(3). 343–358. 37 indexed citations
5.
Lampariello, F. & Marco Sciandrone. (2003). Use of the Minimum-Norm Search Direction in a Nonmonotone Version of the Gauss-Newton Method. Journal of Optimization Theory and Applications. 119(1). 65–82. 11 indexed citations
6.
Lampariello, F. & Marco Sciandrone. (2001). Efficient training of RBF neural networks for pattern recognition. IRIS Research product catalog (Sapienza University of Rome). 37 indexed citations
7.
Lampariello, F. & Marco Sciandrone. (2001). Efficient training of RBF neural networks for pattern recognition. IEEE Transactions on Neural Networks. 12(5). 1235–1242. 1 indexed citations
8.
Lampariello, F. & Marco Sciandrone. (2001). Global Convergence Technique for the Newton Method with Periodic Hessian Evaluation. Journal of Optimization Theory and Applications. 111(2). 341–358. 6 indexed citations
9.
Lampariello, F.. (2000). On the use of the Kolmogorov-Smirnov statistical test for immunofluorescence histogram comparison. Cytometry. 39(3). 179–188. 66 indexed citations
10.
Lampariello, F. & Antonella Aiello. (1998). Complete mathematical modeling method for the analysis of immunofluorescence distributions composed of negative and weakly positive cells. Cytometry. 32(3). 241–254. 25 indexed citations
11.
12.
Lampariello, F., Giovanni V. Sebastiani, Eugenia Cordelli, & Marcello Spanò. (1991). Comparison of gaussian and t‐distribution densities for modeling fluorescence dispersion in flow cytometric DNA histograms. Cytometry. 12(4). 343–349. 7 indexed citations
13.
Grippo, Luigi, F. Lampariello, & Stefano Lucidi. (1991). A class of nonmonotone stabilization methods in unconstrained optimization. Numerische Mathematik. 59(1). 779–805. 139 indexed citations
14.
Grippo, Luigi, F. Lampariello, & Stefano Lucidi. (1990). A quasi-discrete Newton algorithm with a nonmonotone stabilization technique. Journal of Optimization Theory and Applications. 64(3). 495–510. 14 indexed citations
15.
Lampariello, F., et al.. (1989). Automatic analysis of flow cytometric DNA histograms from irradiated mouse male germ cells. Cytometry. 10(1). 62–69. 18 indexed citations
16.
Bruni, Carlo, Lucio Capurso, M. Koch, et al.. (1986). Automatic analysis of flow cytometrically determined dna distributions in the presence of abnormal stemlines. Mathematical Modelling. 7(9-12). 1325–1338. 2 indexed citations
17.
Pillo, Gianni Di, Luigi Grippo, & F. Lampariello. (1984). A newton‐type computing technique for optimal control problems. Optimal Control Applications and Methods. 5(2). 149–166. 5 indexed citations
18.
Pillo, Gianni Di, Luigi Grippo, & F. Lampariello. (1983). A Class of Structured Quasi-Newton Algorithms for Optimal Control Problems. IFAC Proceedings Volumes. 16(8). 101–107. 8 indexed citations
19.
Grasselli, Osvaldo Maria & F. Lampariello. (1981). Dead-beat control of linear periodic discrete-time systems†. International Journal of Control. 33(6). 1091–1106. 50 indexed citations
20.
Grippo, Luigi & F. Lampariello. (1976). Practical stability of discrete-time systems. Journal of the Franklin Institute. 302(3). 213–224. 6 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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