Large-scale Sparse Optimization
Large-Scale Sparse Optimization is a mathematical optimization paradigm characterized by the minimization of objective functions involving a large number of variables or constraints with an inherent sparsity structure, where the majority of variables or coefficients are zero, enabling scalable and efficient computational solutions.
185
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26.6K
Citations
422
Authors
246
Institutions
Publications per year
2017–2026
70
Parents
185
Ciyou Zhu, Richard H. Byrd, Peihuang Lu et al. · ACM Transactions on Mathematical Software · 1997 · 3.4K citations · Full text
Relational learning via collective matrix factorization
Ajit Singh, Geoffrey J. Gordon · 2008 · 1.2K citations
Arne Drud · INFORMS Journal on Computing · 1994 · 696 citations
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246

Anhui University
Hefei, China
9 h-index
26 publications
3.3K citations

University of Edinburgh
Edinburgh, United Kingdom
7 h-index
11 publications
1.9K citations
University at Buffalo, State University of New York
Buffalo, United States
5 h-index
7 publications
378 citations

Sapienza University of Rome
Rome, Italy
5 h-index
9 publications
667 citations
National University of Singapore
Singapore, Singapore
5 h-index
10 publications
745 citations

Hunan University
Changsha, China
4 h-index
5 publications
163 citations
Carnegie Mellon University
Pittsburgh, United States
4 h-index
8 publications
2.9K citations
University of California, Berkeley
Berkeley, United States
4 h-index
7 publications
161 citations
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Repository · 14 publications · 405 citations
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11 publications · 1.7K citations
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10 publications · 1.4K citations
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6 publications · 7.9K citations
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6 publications · 263 citations
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