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Title: Structured Semidefinite Programming for Recovering Structured Preconditioners
We develop a general framework for finding approximately-optimal preconditioners for solving linear systems. Leveraging this framework we obtain improved runtimes for fundamental preconditioning and linear system solving problems including the following. \begin{itemize} \item \textbf{Diagonal preconditioning.} We give an algorithm which, given positive definite $$\mathbf{K} \in \mathbb{R}^{d \times d}$$ with $$\mathrm{nnz}(\mathbf{K})$$ nonzero entries, computes an $$\epsilon$$-optimal diagonal preconditioner in time $$\widetilde{O}(\mathrm{nnz}(\mathbf{K}) \cdot \mathrm{poly}(\kappa^\star,\epsilon^{-1}))$$, where $$\kappa^\star$$ is the optimal condition number of the rescaled matrix. \item \textbf{Structured linear systems.} We give an algorithm which, given $$\mathbf{M} \in \mathbb{R}^{d \times d}$$ that is either the pseudoinverse of a graph Laplacian matrix or a constant spectral approximation of one, solves linear systems in $$\mathbf{M}$$ in $$\widetilde{O}(d^2)$$ time. \end{itemize} Our diagonal preconditioning results improve state-of-the-art runtimes of $$\Omega(d^{3.5})$$ attained by general-purpose semidefinite programming, and our solvers improve state-of-the-art runtimes of $$\Omega(d^{\omega})$$ where $$\omega > 2.3$$ is the current matrix multiplication constant. We attain our results via new algorithms for a class of semidefinite programs (SDPs) we call \emph{matrix-dictionary approximation SDPs}, which we leverage to solve an associated problem we call \emph{matrix-dictionary recovery}.  more » « less
Award ID(s):
2045590
PAR ID:
10495901
Author(s) / Creator(s):
; ; ; ; ;
Publisher / Repository:
Curran Associates, Inc.
Date Published:
Journal Name:
Advances in Neural Information Processing Systems
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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