Tight Lower Bounds for Planted Clique in the Degree-4 SOS Program
July 18, 2015 · Declared Dead · 🏛 arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Prasad Raghavendra, Tselil Schramm
arXiv ID
1507.05136
Category
cs.DS: Data Structures & Algorithms
Cross-listed
cs.CC
Citations
27
Venue
arXiv.org
Last Checked
3 months ago
Abstract
We give a lower bound of $\tildeΩ(\sqrt{n})$ for the degree-4 Sum-of-Squares SDP relaxation for the planted clique problem. Specifically, we show that on an Erdös-Rényi graph $G(n,\tfrac{1}{2})$, with high probability there is a feasible point for the degree-4 SOS relaxation of the clique problem with an objective value of $\tildeΩ(\sqrt{n})$, so that the program cannot distinguish between a random graph and a random graph with a planted clique of size $\tilde{O}(\sqrt{n})$. This bound is tight. We build on the works of Deshpande and Montanari and Meka et al., who give lower bounds of $\tildeΩ(n^{1/3})$ and $\tildeΩ(n^{1/4})$ respectively. We improve on their results by making a perturbation to the SDP solution proposed in their work, then showing that this perturbation remains PSD as the objective value approaches $\tildeΩ(n^{1/2})$. In an independent work, Hopkins, Kothari and Potechin [HKP15] have obtained a similar lower bound for the degree-$4$ SOS relaxation.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
📜 Similar Papers
In the same crypt — Data Structures & Algorithms
📚
📚
The Cartographer
R.I.P.
👻
Ghosted
Route Planning in Transportation Networks
R.I.P.
👻
Ghosted
Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration
R.I.P.
👻
Ghosted
Hierarchical Clustering: Objective Functions and Algorithms
R.I.P.
👻
Ghosted
Graph Isomorphism in Quasipolynomial Time
📚
📚
The Cartographer
Simulation optimization: A review of algorithms and applications
Died the same way — 👻 Ghosted
R.I.P.
👻
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
👻
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
👻
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
👻
Ghosted