Large-scale Semidefinite Programming
Large-Scale Semidefinite Programming is a specialized form of convex optimization that involves the minimization of a linear objective function over the intersection of the cone of positive semidefinite matrices with an affine subspace, specifically characterized by high-dimensional matrix variables and constraints that render traditional solution methods computationally infeasible.
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8.7K
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119
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74
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2017–2026
26
Variants
Large-scale Sdp
Parents
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Tokyo Institute of Technology
Tokyo, Japan
7 h-index
19 publications
1.5K citations
University of Oxford
Oxford, United Kingdom
6 h-index
17 publications
716 citations
Chuo University
Hachiōji, Japan
5 h-index
6 publications
156 citations
National University of Singapore
Singapore, Singapore
5 h-index
8 publications
383 citations
University of California, Berkeley
Berkeley, United States
4 h-index
5 publications
98 citations

Linköping University
Linköping, Sweden
3 h-index
5 publications
441 citations
Courant Institute of Mathematical Sciences
New York, United States
3 h-index
4 publications
434 citations
New York University
New York, United States
3 h-index
4 publications
434 citations
1–8 of 74
9 publications · 882 citations
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Repository · 5 publications · 103 citations
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5 publications · 173 citations
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5 publications · 371 citations
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