Semidefinite Relaxation
Semidefinite Relaxation is a convex optimization technique that approximates a hard combinatorial problem, particularly in integer programming and global optimization, by replacing non-convex quadratic constraints with positive semidefinite matrix constraints, thereby providing a tractable bound on the original problem.
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Citations
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Authors
846
Institutions
Publications per year
2017–2026
548
Variants
Sdr
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Semidefinite Relaxation of Quadratic Optimization Problems
Zhi‐Quan Luo, Wing‐Kin Ma, Anthony Man–Cho So et al. · IEEE Signal Processing Magazine · 2010 · 3.6K citations
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Laboratoire d'Analyse et d'Architecture des Systèmes, France
28 publications · 2.3K citations
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Chinese University of Hong Kong
Hong Kong, Hong Kong
32 h-index
86 publications
7.7K citations
Princeton University
Princeton, United States
31 h-index
78 publications
9.7K citations

University of Minnesota
Minneapolis, United States
29 h-index
81 publications
9K citations

Xidian University
Xi'an, China
28 h-index
85 publications
8.2K citations

Southeast University
Nanjing, China
26 h-index
90 publications
5.3K citations
University of California, Berkeley
Berkeley, United States
25 h-index
65 publications
4.9K citations
Massachusetts Institute of Technology
Cambridge, United States
25 h-index
77 publications
7.7K citations

Centre National de la Recherche Scientifique
Paris, France
23 h-index
39 publications
3.7K citations
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93 publications · 12.4K citations
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84 publications · 9.6K citations
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71 publications · 6.8K citations
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58 publications · 4.9K citations
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55 publications · 3.5K citations
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