A Faster Interior Point Method for Semidefinite Programming
Published: Nov 1, 2020
Abstract
Semidefinite programs (SDPs) are a fundamental class of optimization problems with important recent applications in approximation algorithms, quantum complexity, robust learning, algorithmic rounding, and adversarial deep learning. This paper presents a faster interior point method to solve generic SDPs with variable size n ×n and m constraints in time Õ(√n(mn
Paper Details
Title
A Faster Interior Point Method for Semidefinite Programming
Published Date
Nov 1, 2020
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