Under independence and tail conditions on random symmetric matrices, the DNN relaxation of the standard quadratic program is exact with probability tending to 1, the optimizer is unique and rank one, and recoverable in O(n^2) time.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
math.OC 2verdicts
UNVERDICTED 2representative citing papers
An inexact subgradient algorithm achieves O(ε^{-2}) iteration complexity for ε-accurate solutions to copositive programs while allowing inexact solves of NP-hard quadratic subproblems and providing a sufficient condition for non-complete positivity.
citing papers explorer
-
Exactness of the DNN Relaxation for Random Standard Quadratic Programs
Under independence and tail conditions on random symmetric matrices, the DNN relaxation of the standard quadratic program is exact with probability tending to 1, the optimizer is unique and rank one, and recoverable in O(n^2) time.
-
Inexact subgradient algorithm with a non-asymptotic convergence guarantee for copositive programming problems
An inexact subgradient algorithm achieves O(ε^{-2}) iteration complexity for ε-accurate solutions to copositive programs while allowing inexact solves of NP-hard quadratic subproblems and providing a sufficient condition for non-complete positivity.