Fast algorithms for approximate semidefinite programming using the multiplicative weights update method

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.