Stochastic optimization on matrices and a graphon McKean-Vlasov limit

We consider stochastic gradient descents on the space of large symmetric matrices of suitable functions that are invariant under permuting the rows and columns using the same permutation. We establish deterministic limits of these random curves as the dimensions of the matrices go to infinity while...

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Bibliographic Details
Published in:arXiv.org 2022-11
Main Authors: Harchaoui, Zaid, Oh, Sewoong, Pal, Soumik, Somani, Raghav, Tripathi, Raghavendra
Format: Article
Language:English
Subjects:
Brownian motion
Differential equations
Electrical noise
Gradient flow
Mathematical analysis
Matrices (mathematics)
Optimization
Permutations
Online Access:Get full text
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Summary:We consider stochastic gradient descents on the space of large symmetric matrices of suitable functions that are invariant under permuting the rows and columns using the same permutation. We establish deterministic limits of these random curves as the dimensions of the matrices go to infinity while the entries remain bounded. Under a "small noise" assumption the limit is shown to be the gradient flow of functions on graphons whose existence was established in arXiv:2111.09459. We also consider limits of stochastic gradient descents with added properly scaled reflected Brownian noise. The limiting curve of graphons is characterized by a family of stochastic differential equations with reflections and can be thought of as an extension of the classical McKean-Vlasov limit for interacting diffusions. The proofs introduce a family of infinite-dimensional exchangeable arrays of reflected diffusions and a novel notion of propagation of chaos for large matrices of interacting diffusions.
ISSN:2331-8422