Almost Optimal Sublinear Time Algorithm for Semidefinite Programming
Dan Garber, Elad Hazan
TL;DR
This paper introduces a nearly optimal sublinear time algorithm for approximating semidefinite programs, significantly reducing computational complexity for large instances.
Contribution
It provides a novel sublinear time algorithm for semidefinite programming along with matching lower bounds demonstrating near-optimal efficiency.
Findings
Algorithm achieves sublinear running time in the size of the SDP instance.
Lower bounds indicate the algorithm's near-optimality.
Potential for large-scale SDP applications with reduced computational resources.
Abstract
We present an algorithm for approximating semidefinite programs with running time that is sublinear in the number of entries in the semidefinite instance. We also present lower bounds that show our algorithm to have a nearly optimal running time.
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Taxonomy
TopicsAdvanced Bandit Algorithms Research · Advanced Optimization Algorithms Research · Sparse and Compressive Sensing Techniques