Web: http://arxiv.org/abs/2004.12063

Jan. 27, 2022, 2:10 a.m. | David Gamarnik, Aukosh Jagannath, Alexander S. Wein

stat.ML updates on arXiv.org arxiv.org

We consider the problem of finding nearly optimal solutions of optimization
problems with random objective functions. Two concrete problems we consider are
(a) optimizing the Hamiltonian of a spherical or Ising $p$-spin glass model,
and (b) finding a large independent set in a sparse Erd\H{o}s-R\'{e}nyi graph.
The following families of algorithms are considered: (a) low-degree polynomials
of the input; (b) low-depth Boolean circuits; (c) the Langevin dynamics
algorithm. We show that these families of algorithms fail to produce nearly
optimal …

arxiv optimization random

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