Marco Sciandrone

2.5k citations
69 papers · 1.7k indexed · h-index 25

Marco Sciandrone

66 papers receiving 1.6k citations

Peers

Marco Sciandrone
Comparison fields: 5 of 152
  • Numerical Analysis 503
  • Computational Theory and Mathematics 470
  • Artificial Intelligence 424
  • Computational Mechanics 268
  • Signal Processing 137
Replace Taiyong Li with:
Taiyong Li China
Dominique Orban Canada
B.E. Stuckman United States
C.D. Perttunen United States
Janusz S. Kowalik United States
Zhiwen Zhang China
Yi‐Fei Pu China
Guoqiang Han China
Fengmin Xu China
Anthony T. Chronopoulos United States
Marco Sciandrone relative to Taiyong Li China Taiyong Li's profile →
Citations per field
00.5×3.4×
Taiyong Li · 1×
Citations per year

Countries citing papers authored by Marco Sciandrone

Since Specialization
Citations

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

Fields of papers citing papers by Marco Sciandrone

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 25 scholars most cited alongside Marco Sciandrone, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Marco Sciandrone Line = papers co-authored together Marco Sciandrone links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20250
2 20251
3 202310
4 20232
5
A Two-Level Decomposition Framework Exploiting First and Second Order Information for SVM Training Problems
20213
6 20219
7 2019117
8 201812
9 20189
10 201831
11 201719
12 201723
13 20172
14 201654
15 2010189
16 200916
17 200850
18 200536
19 200137
20 20011

About Marco Sciandrone

Marco Sciandrone is a scholar working on Numerical Analysis, Computational Theory and Mathematics, Computational Mechanics, Artificial Intelligence and Transportation, having authored 69 papers that have together received 1.7k indexed citations. Recurring topics across this work include Advanced Optimization Algorithms Research (34 papers), Sparse and Compressive Sensing Techniques (16 papers), Optimization and Variational Analysis (13 papers), Iterative Methods for Nonlinear Equations (12 papers), Neural Networks and Applications (10 papers), Advanced Multi-Objective Optimization Algorithms (8 papers), Face and Expression Recognition (7 papers) and Advanced Control Systems Optimization (6 papers). The work is most often cited by research in Numerical Analysis (503 citations), Computational Theory and Mathematics (470 citations), Artificial Intelligence (424 citations), Computational Mechanics (268 citations) and Signal Processing (137 citations). Marco Sciandrone has collaborated with scholars based in Italy, United States and Germany. Frequent co-authors include Stefano Lucidi, Luigi Grippo, Veronica Piccialli, F. Lampariello, Giampaolo Liuzzi, Carmelo Anile, Francesco Rinaldi, Francisco Facchinei, Fabio Schoen and Laura Palagi. Their work appears in journals such as Computational Optimization and Applications, Optimization methods & software, Journal of Optimization Theory and Applications, SIAM Journal on Optimization and Optimization Letters.

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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