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Found 4 papers, 0 papers with code
Fast Algorithms for Directed Graph Partitioning Using Flows and Reweighted Eigenvalues
We consider a new semidefinite programming relaxation for directed edge expansion, which is obtained by adding triangle inequalities to the reweighted eigenvalue formulation.
Experimental Design for Any $p$-Norm
We prove that a randomized local search approach provides a unified algorithm to solve this problem for all $p$.
Cheeger Inequalities for Directed Graphs and Hypergraphs Using Reweighted Eigenvalues
The first main result is a Cheeger inequality relating the vertex expansion $\vec{\psi}(G)$ of a directed graph $G$ to the vertex-capacitated maximum reweighted second eigenvalue $\vec{\lambda}_2^{v*}$: \[ \vec{\lambda}_2^{v*} \lesssim \vec{\psi}(G) \lesssim \sqrt{\vec{\lambda}_2^{v*} \cdot \log (\Delta/\vec{\lambda}_2^{v*})}.
A Local Search Framework for Experimental Design
We present a local search framework to design and analyze both combinatorial algorithms and rounding algorithms for experimental design problems.
