Fast algorithms for approximate semidefinite programming using the multiplicative weights update method
2013 IEEE International Conference on Cluster Computing (CLUSTER) (2005)
Pittsburgh, PA USA
Oct. 23, 2005 to Oct. 25, 2005
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/SFCS.2005.35
Semidefinite programming (SDP) relaxations appear in many recent approximation algorithms but the only general technique for solving such SDP relaxations is via interior point methods. We use a Lagrangian-relaxation based technique (modified from the papers of Plotkin, Shmoys, and Tardos (PST), and Klein and Lu) to derive faster algorithms for approximately solving several families of SDP relaxations. The algorithms are based upon some improvements to the PST ideas - which lead to new results even for their framework - as well as improvements in approximate eigenvalue computations by using random sampling.
Approximation algorithms, Algorithm design and analysis, Frequency estimation, Computer science, Polynomials, Lagrangian functions, Eigenvalues and eigenfunctions, Sampling methods, Ellipsoids, NP-hard problem,
"Fast algorithms for approximate semidefinite programming using the multiplicative weights update method", 2013 IEEE International Conference on Cluster Computing (CLUSTER), vol. 00, no. , pp. 339-348, 2005, doi:10.1109/SFCS.2005.35