Journal article icon

Journal article

Efficient semidefinite programming with approximate ADMM

Abstract:

Tenfold improvements in computation speed can be brought to the alternating direction method of multipliers (ADMM) for Semidefinite Programming with virtually no decrease in robustness and provable convergence simply by projecting approximately to the Semidefinite cone. Instead of computing the projections via “exact” eigendecompositions that scale cubically with the matrix size and cannot be warm-started, we suggest using state-of-the-art factorization-free, approximate eigensolvers, thus ac...

Expand abstract
Publication status:
Published
Peer review status:
Peer reviewed

Actions


Access Document


Files:
Publisher copy:
10.1007/s10957-021-01971-3

Authors


More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Oxford college:
St Edmund Hall
Role:
Author
ORCID:
0000-0002-0456-4124
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Mathematical Institute
Oxford college:
Christ Church
Role:
Author
ORCID:
0000-0001-7911-1501
Publisher:
Springer Publisher's website
Journal:
Journal of Optimization Theory and Applications Journal website
Volume:
192
Pages:
292-320
Publication date:
2021-11-27
Acceptance date:
2021-10-31
DOI:
EISSN:
1573-2878
ISSN:
0022-3239
Language:
English
Keywords:
Pubs id:
1218925
Local pid:
pubs:1218925
Deposit date:
2022-01-03

Terms of use


Views and Downloads






If you are the owner of this record, you can report an update to it here: Report update to this record

TO TOP