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DOI: 10.4230/LIPIcs.ITCS.2026.89

On Solving Asymmetric Diagonally Dominant Linear Systems in Sublinear Time

Authors: Tsz Chiu Kwok, Zhewei Wei, and Mingji Yang

Published in: LIPIcs, Volume 362, 17th Innovations in Theoretical Computer Science Conference (ITCS 2026)

Abstract

We initiate a study of solving a row/column diagonally dominant (RDD/CDD) linear system 𝐌x = b in sublinear time, with the goal of estimating t^{⊤}x^{∗} for a given vector t ∈ ℝⁿ and a specific solution x^{∗}. This setting naturally generalizes the study of sublinear-time solvers for symmetric diagonally dominant (SDD) systems [Andoni-Krauthgamer-Pogrow, ITCS 2019] to the asymmetric case, which has remained underexplored despite extensive work on nearly-linear-time solvers for RDD/CDD systems. Our first contributions are characterizations of the problem’s mathematical structure. We express a solution x^{∗} via a Neumann series, prove its convergence, and upper bound the truncation error on this series through a novel quantity of 𝐌, termed the maximum p-norm gap. This quantity generalizes the spectral gap of symmetric matrices and captures how the structure of 𝐌 governs the problem’s computational difficulty. For systems with bounded maximum p-norm gap, we develop a collection of algorithmic results for locally approximating t^{⊤}x^{∗} under various scenarios and error measures. We derive these results by adapting the techniques of random-walk sampling, local push, and their bidirectional combination, which have proved powerful for special cases of solving RDD/CDD systems, particularly estimating PageRank and effective resistance on graphs. Our general framework yields deeper insights, extended results, and improved complexity bounds for these problems. Notably, our perspective provides a unified understanding of Forward Push and Backward Push, two fundamental approaches for estimating random-walk probabilities on graphs. Our framework also inherits the hardness results for sublinear-time SDD solvers and local PageRank computation, establishing lower bounds on the maximum p-norm gap or the accuracy parameter. We hope that our work opens the door for further study into sublinear solvers, local graph algorithms, and directed spectral graph theory.

Cite as

Tsz Chiu Kwok, Zhewei Wei, and Mingji Yang. On Solving Asymmetric Diagonally Dominant Linear Systems in Sublinear Time. In 17th Innovations in Theoretical Computer Science Conference (ITCS 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 362, pp. 89:1-89:25, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{kwok_et_al:LIPIcs.ITCS.2026.89,
  author =	{Kwok, Tsz Chiu and Wei, Zhewei and Yang, Mingji},
  title =	{{On Solving Asymmetric Diagonally Dominant Linear Systems in Sublinear Time}},
  booktitle =	{17th Innovations in Theoretical Computer Science Conference (ITCS 2026)},
  pages =	{89:1--89:25},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-410-9},
  ISSN =	{1868-8969},
  year =	{2026},
  volume =	{362},
  editor =	{Saraf, Shubhangi},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ITCS.2026.89},
  URN =		{urn:nbn:de:0030-drops-253768},
  doi =		{10.4230/LIPIcs.ITCS.2026.89},
  annote =	{Keywords: Spectral Graph Theory, Linear Systems, Sublinear Algorithms}
}

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DOI: 10.4230/LIPIcs.ICDT.2024.9

Approximating Single-Source Personalized PageRank with Absolute Error Guarantees

Authors: Zhewei Wei, Ji-Rong Wen, and Mingji Yang

Published in: LIPIcs, Volume 290, 27th International Conference on Database Theory (ICDT 2024)

Abstract

Personalized PageRank (PPR) is an extensively studied and applied node proximity measure in graphs. For a pair of nodes s and t on a graph G = (V,E), the PPR value π(s,t) is defined as the probability that an α-discounted random walk from s terminates at t, where the walk terminates with probability α at each step. We study the classic Single-Source PPR query, which asks for PPR approximations from a given source node s to all nodes in the graph. Specifically, we aim to provide approximations with absolute error guarantees, ensuring that the resultant PPR estimates π̂(s,t) satisfy max_{t ∈ V} |π̂(s,t)-π(s,t)| ≤ ε for a given error bound ε. We propose an algorithm that achieves this with high probability, with an expected running time of - Õ(√m/ε) for directed graphs, where m = |E|; - Õ(√{d_max}/ε) for undirected graphs, where d_max is the maximum node degree in the graph; - Õ(n^{γ-1/2}/ε) for power-law graphs, where n = |V| and γ ∈ (1/2,1) is the extent of the power law. These sublinear bounds improve upon existing results. We also study the case when degree-normalized absolute error guarantees are desired, requiring max_{t ∈ V} |π̂(s,t)/d(t)-π(s,t)/d(t)| ≤ ε_d for a given error bound ε_d, where the graph is undirected and d(t) is the degree of node t. We give an algorithm that provides this error guarantee with high probability, achieving an expected complexity of Õ(√{∑_{t ∈ V} π(s,t)/d(t)}/ε_d). This improves over the previously known O(1/ε_d) complexity.

Cite as

Zhewei Wei, Ji-Rong Wen, and Mingji Yang. Approximating Single-Source Personalized PageRank with Absolute Error Guarantees. In 27th International Conference on Database Theory (ICDT 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 290, pp. 9:1-9:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{wei_et_al:LIPIcs.ICDT.2024.9,
  author =	{Wei, Zhewei and Wen, Ji-Rong and Yang, Mingji},
  title =	{{Approximating Single-Source Personalized PageRank with Absolute Error Guarantees}},
  booktitle =	{27th International Conference on Database Theory (ICDT 2024)},
  pages =	{9:1--9:19},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-312-6},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{290},
  editor =	{Cormode, Graham and Shekelyan, Michael},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICDT.2024.9},
  URN =		{urn:nbn:de:0030-drops-197911},
  doi =		{10.4230/LIPIcs.ICDT.2024.9},
  annote =	{Keywords: Graph Algorithms, Sublinear Algorithms, Personalized PageRank}
}

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Documents authored by Yang, Mingji

On Solving Asymmetric Diagonally Dominant Linear Systems in Sublinear Time

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Approximating Single-Source Personalized PageRank with Absolute Error Guarantees

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