Paving Property for Real Stable Polynomials and Strongly Rayleigh Processes
Kasra Alishahi, Milad Barzegar

TL;DR
This paper extends the paving theorem to real stable polynomials and applies it to strongly Rayleigh processes, showing they can be partitioned into weakly correlated subsets, revealing limitations on their repulsive interactions.
Contribution
It generalizes the paving property to real stable polynomials and introduces the kernel polynomial framework for strongly Rayleigh processes.
Findings
Partitioning of variables leads to small roots in restrictions of polynomials.
Strongly Rayleigh processes can be partitioned into weakly correlated subsets.
Entropy bounds are established in terms of kernel polynomial roots.
Abstract
One of the equivalent formulations of the Kadison-Singer problem which was resolved in 2013 by Marcus, Spielman and Srivastava, is the "paving conjecture". Roughly speaking, the paving conjecture states that every positive semi-definite contraction with small diagonal entries can be "paved" by a small number of principal submatrices with small operator norms. We extend this result to real stable polynomials. We will prove that assuming mild conditions on the leading coefficients of a multi-affine real stable polynomial, it is possible to partition the set of variables to a small number of subsets such that the roots of the "restrictions" of the polynomial to each set of variables are small. We will use this generalized paving theorem to show that for every strongly Rayleigh point process, it is possible to partition the underlying space into a small number of subsets such that the…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMarkov Chains and Monte Carlo Methods · Stochastic processes and statistical mechanics · Random Matrices and Applications
