Local max-cut in smoothed polynomial time
Not on the map. Why?
Abstract (arXiv)
In 1988, Johnson, Papadimitriou and Yannakakis wrote that "Practically all the empirical evidence would lead us to conclude that finding locally optimal solutions is much easier than solving NP-hard problems". Since then the empirical evidence has continued to amass, but formal proofs of this phenomenon have remained elusive. A canonical (and indeed complete) example is the local max-cut problem, for which no polynomial time method is known. In a breakthrough paper, Etscheid and R"oglin proved that the smoothed complexity of local max-cut is quasi-polynomial, i.e., if arbitrary bounded weights are randomly perturbed, a local maximum can be found in nO(log n) steps. In this paper we prove smoothed polynomial complexity for local max-cut, thus confirming that finding local optima for max-cut is much easier than solving it.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). First posted on arXiv 16 Oct 2016.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2022: 1; 2023: 0; 2024: 1.
2 papers.
- 2024Mini-batch Submodular Maximizationcited by 1
- 2022Pattern reconstruction with restricted Boltzmann machinescited by 0
- 2024Mini-batch Submodular Maximization23 Jan 2024
- 2022Pattern reconstruction with restricted Boltzmann machines14 May 2022