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Research PaperResearchia:202602.26018

Complexity of Classical Acceleration for $\ell_1$-Regularized PageRank

Kimon Fountoulakis

Abstract

We study the degree-weighted work required to compute $\ell_1$-regularized PageRank using the standard one-gradient-per-iteration accelerated proximal-gradient method (FISTA). For non-accelerated local methods, the best known worst-case work scales as $\widetilde{O} ((αρ)^{-1})$, where $α$ is the teleportation parameter and $ρ$ is the $\ell_1$-regularization parameter. A natural question is whether FISTA can improve the dependence on $α$ from $1/α$ to $1/\sqrtα$ while preserving the $1/ρ$ locali...

Submitted: February 26, 2026Subjects: Machine Learning; Data Science

Description / Details

We study the degree-weighted work required to compute 1\ell_1-regularized PageRank using the standard one-gradient-per-iteration accelerated proximal-gradient method (FISTA). For non-accelerated local methods, the best known worst-case work scales as O~((αρ)1)\widetilde{O} ((αρ)^{-1}), where αα is the teleportation parameter and ρρ is the 1\ell_1-regularization parameter. A natural question is whether FISTA can improve the dependence on αα from 1/α1/α to 1/α1/\sqrtα while preserving the 1/ρ1/ρ locality scaling. The challenge is that acceleration can break locality by transiently activating nodes that are zero at optimality, thereby increasing the cost of gradient evaluations. We analyze FISTA on a slightly over-regularized objective and show that, under a checkable confinement condition, all spurious activations remain inside a boundary set B\mathcal{B}. This yields a bound consisting of an accelerated (ρα)1log(α/ε)(ρ\sqrtα)^{-1}\log(α/\varepsilon) term plus a boundary overhead vol(B)/(ρα3/2)\sqrt{vol(\mathcal{B})}/(ρα^{3/2}). We provide graph-structural conditions that imply such confinement. Experiments on synthetic and real graphs show the resulting speedup and slowdown regimes under the degree-weighted work model.


Source: arXiv:2602.21138v1 - http://arxiv.org/abs/2602.21138v1 PDF: https://arxiv.org/pdf/2602.21138v1 Original Link: http://arxiv.org/abs/2602.21138v1

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Date:
Feb 26, 2026
Topic:
Data Science
Area:
Machine Learning
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