Yu-Hong Dai: h-index, Total Citations, and Citation Map
Yu-Hong Dai's h-index is 49 (123 i10-index, 12,941+ total citations across 279+ publications) according to Google Scholar as of May 2026. Yu-Hong Dai is affiliated with chinese academy of sciences.
Yu-Hong Dai is a researcher affiliated with chinese academy of sciences, specializing in nonlinear optimization, integer programming. Their work has been cited 12,941 times. This profile visualizes their global influence, highlighting strong citation networks in China.
Yu-Hong Dai's Citation Metrics
Bibliometric impact based on 279 indexed publications.
- H-Index
- 49
- i10-Index
- 123
- Total Citations
- 12,941
- Citing Countries
- 65
As of May 2026.
Yu-Hong Dai has an h-index of 49 and 12,941 total citations across 279 publications, with research cited by institutions in 65 countries.
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We've mapped 5,000 of 12,941 citations for Yu-Hong Dai
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Top Cited Works
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A nonlinear conjugate gradient method with a strong global convergence property
19992,130
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Top Citing Institutions
Visa Evidence Package
Views and exports tuned for EB-1A, O-1A, and EB-2 NIW petitions. Sustained acclaim, geographic reach, and independent-citation filtering are the strongest evidence categories immigration adjudicators look for.
Significant Contributions
Auto-detected research lines — a seminal paper and the follow-up work building on it. Review and edit before using in a petition. Each Free PDF opens in a new tab — EB-1A organises this into the structure USCIS applies to Criterion 5 of 8 CFR § 204.5(h)(3)(v); EB-1B re-frames it under § 204.5(i)(3) (outstanding researcher); NIW presents it under prong 2 of Matter of Dhanasar.
1428 citing papers could not be classified (no author data) — excluded from the percentages above.
The researcher developed a foundational nonlinear conjugate gradient method with strong global convergence, establishing a durable framework for unconstrained optimization that has driven subsequent algorithmic refinements.
The researcher established the theoretical convergence of the Barzilai-Borwein method and extended its application to stochastic and manifold optimization, significantly advancing numerical optimization techniques.
The researcher developed foundational nonmonotone line search techniques and advanced gradient methods, establishing a highly cited framework for unconstrained optimization algorithms.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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