Daniel Gabay

3.4k citations
8 papers · 2.4k · 1 hit paper · h-index 6

Impact in

Papers in

Daniel Gabay

8 papers receiving 2.2k citations

Daniel Gabay's Hit Papers

A dual algorithm for the solution of nonlinear variational problems via finite element approximation 1976 · 1.8k citations
1.8k0+16+33Years since publication50010001.5k

Peers

Daniel Gabay
Comparison fields: 5 of 103
  • Numerical Analysis 719
  • Computational Mathematics 41
  • Computational Mechanics 1.3k
  • Computational Theory and Mathematics 713
  • Mathematical Physics 283
Replace Bertrand Mercier with:
Bertrand Mercier France
George A. Watson United Kingdom
Lev M. Bregman Russia
Li‐Zhi Liao Hong Kong
Klaus Höllig Germany
Ming‐Jun Lai United States
P-C Tseng United States
Albert Cohen France
Deren Han China
Valérie R. Wajs France
Daniel Gabay relative to Bertrand Mercier France Bertrand Mercier's profile →
Citations per field
00.5×1.5×
Bertrand Mercier · 1×
Citations per year

Countries citing papers authored by Daniel Gabay

Since Specialization
Citations

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

Fields of papers citing papers by Daniel Gabay

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 4 scholars most cited alongside Daniel Gabay, 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 Daniel Gabay Line = papers co-authored together Daniel Gabay links everyone, so they are left out of the graph.

All Works

8 of 8 papers shown
#Work
1
A dual algorithm for the solution of nonlinear variational problems via finite element approximation
Hit paper breakdown →
19761782
2 1983341
3 1982180
4 198280
5 197623
6 19847
7 20115
8 19831

About Daniel Gabay

Daniel Gabay is a scholar working on Numerical Analysis, Computational Theory and Mathematics, Finance, Computational Mechanics and Demography, having authored 8 papers that have together received 2.4k indexed citations. Recurring topics across this work include Advanced Optimization Algorithms Research (5 papers), Iterative Methods for Nonlinear Equations (3 papers), Advanced Numerical Analysis Techniques (2 papers), Optimization and Variational Analysis (2 papers), Stochastic processes and financial applications (2 papers), Insurance, Mortality, Demography, Risk Management (2 papers), Topology Optimization in Engineering (1 paper) and Probability and Risk Models (1 paper). The work is most often cited by research in Numerical Analysis (719 citations), Computational Mathematics (41 citations), Computational Mechanics (1.3k citations), Computational Theory and Mathematics (713 citations) and Mathematical Physics (283 citations). Daniel Gabay has collaborated with scholars based in France and Italy. Frequent co-authors include Bertrand Mercier, David G. Luenberger, J. Frédéric Bonnans and Martino Grasselli. Their work appears in journals such as Journal of Economic Dynamics and Control, Journal of Optimization Theory and Applications, Lecture notes in control and information sciences, Computers & Mathematics with Applications and SIAM Journal on Control and Optimization.

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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