Eigenvalue Bounds for Symmetric Markov Chains on Multislices With Applications
Prashanth Amireddy, Amik Raj Behera, Srikanth Srinivasan, Madhu Sudan

TL;DR
This paper establishes eigenvalue bounds for symmetric Markov chains on multislices, demonstrating their spectral expansion properties and applying these results to error-correcting codes and junta-sum algorithms.
Contribution
It introduces new eigenvalue bounds for symmetric Markov chains on multislices and applies them to coding theory and junta-sum correction algorithms.
Findings
Broad class of multislice walks are good spectral expanders
Derived analog of Schwartz-Zippel lemma for multislice polynomials
Developed local list-correction algorithm for junta-sums
Abstract
We consider random walks on ``balanced multislices'' of any ``grid'' that respects the ``symmetries'' of the grid, and show that a broad class of such walks are good spectral expanders. (A grid is a set of points of the form for finite , and a balanced multi-slice is the subset that contains an equal number of coordinates taking every value in . A walk respects symmetries if the probability of going from to is invariant under simultaneous permutations of the coordinates of and .) Our main theorem shows that, under some technical conditions, every such walk where a single step leads to an almost -wise independent distribution on the next state, conditioned on the previous state, satisfies a non-trivially small singular value bound. We give two applications of our theorem to…
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Taxonomy
TopicsMarkov Chains and Monte Carlo Methods
