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Complexity of Classical Acceleration for ℓ1-Regularized PageRank

arXiv 2602.21138 ↗First posted 24 Feb 2026math.OC cs.DS cs.LGcited by 0

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Abstract (arXiv)

We study the degree-weighted work required to compute ℓ1-regularized PageRank using the standard accelerated proximal-gradient method (FISTA). For non-accelerated methods (ISTA), the best known worst-case work is widetildeO((αρ)-1), where α is the teleportation parameter and ρ is the ℓ1-regularization parameter. It is not known whether classical acceleration methods can improve 1/α to 1/√α while preserving the 1/ρ locality scaling, or whether they can be asymptotically worse. For FISTA, we show a negative result by constructing a family of instances for which standard FISTA is asymptotically worse than ISTA. On the positive side, we analyze FISTA on a slightly over-regularized objective and show that, under a confinement condition, all spurious activations remain inside a boundary set B. This yields a bound consisting of an accelerated (ρ√α)-1log(α/varepsilon) term plus a boundary overhead √vol(B)/(ρα3/2). We also provide graph-structural sufficient conditions that imply such confinement.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 7 of its 20 references that are in the map. First posted on arXiv 24 Feb 2026.

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References · 7

  1. 2016Variational Perspective on Local Graph Clusteringcited by 14
  2. 2020Revisiting Frank-Wolfe for Polytopes: Strict Complementarity and Sparsitycited by 9
  3. 2017"Active-set complexity" of proximal gradient: How long does it take to find the sparsity pattern?cited by 7
  4. 2019Statistical guarantees for local graph clusteringcited by 6
  5. 2024Iterative Methods via Locally Evolving Set Processcited by 5
  6. 2023Accelerated and Sparse Algorithms for Approximate Personalized PageRank and Beyondcited by 3
  7. 2019On the Interplay between Acceleration and Identification for the Proximal Gradient algorithmcited by 1

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