Global Convergence Analysis
Global Convergence Analysis is a theoretical framework in mathematical optimization and numerical analysis that characterizes the conditions under which an iterative algorithm is guaranteed to converge to a globally optimal solution or a global root, regardless of the initial starting point.
2.2K
Publications
136.9K
Citations
3.8K
Authors
1.3K
Institutions
Publications per year
2017–2026
632
2.2K
Global convergence in the vulnerability of forests to drought
Brendan Choat, Steven Jansen, Timothy J. Brodribb et al. · Nature · 2012 · 2.7K citations · Full text
Natural Resources, Ecology, Forestry +12
On the Convergence of Pattern Search Algorithms
Virginia Torczon · SIAM Journal on Optimization · 1997 · 1.3K citations
1–5 of 2.2K
3.8K
19
h-index
18
h-index
15
h-index
15
h-index
14
h-index
1–5 of 3.8K
1.3K

Chinese Academy of Sciences
Beijing, China
25 h-index
70 publications
4.2K citations
Chinese University of Hong Kong
Hong Kong, Hong Kong
23 h-index
44 publications
3.8K citations

Centre National de la Recherche Scientifique
Paris, France
21 h-index
51 publications
1.9K citations

Bayero University Kano
Kano, Nigeria
20 h-index
46 publications
1.3K citations
King Mongkut's University of Technology Thonburi
Bangkok, Thailand
20 h-index
67 publications
2.1K citations
University of Illinois Urbana-Champaign
Urbana, United States
19 h-index
37 publications
2.5K citations
Guangxi University
Nanning, China
18 h-index
68 publications
2.2K citations
Massachusetts Institute of Technology
Cambridge, United States
18 h-index
33 publications
2.1K citations
1–8 of 1.3K
146 publications · 19K citations
64
h-index
75 publications · 7.9K citations
38
h-index
51 publications · 5.2K citations
35
h-index
Repository · 125 publications · 5.1K citations
34
h-index
40 publications · 3K citations
31
h-index
1–5