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Resilient Distributed Optimization

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Jingxuan Zhu, Yixuan Lin, Alvaro Velasquez, Ji Liu

Optimization and Control · Submitted 27 Sep 2022

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim It is shown that the algorithm causes all non-Byzantine agents' states to asymptotically converge to the same optimal point under appropriate assumptions.

how it is stated · asserted 1.00 read the record →

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arXiv ID2209.13095 (version 2)
Pith IDHQWRHMAR, Pith's permanent record of this paper and its review
CitationAlvaro Velasquez, Ji Liu, Jingxuan Zhu, Yixuan Lin. "Resilient Distributed Optimization." arXiv:2209.13095 (2022). https://pith.science/paper/HQWRHMAR
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The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 3,485 input tokens.

What the abstract itself states, as probabilities: claims first 0.03 · states limitation 0.66 · human participants 0.02 · controlled comparison 0.03 · randomized assignment 0.01 · states quantitative result 0.11 · compares with prior results 0.05 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v2. Results and the main claim are underlined; .

This paper considers a distributed optimization problem in the presence of Byzantine agents capable of introducing untrustworthy information into the communication network. A resilient distributed subgradient algorithm is proposed based on graph redundancy and objective redundancy. It is shown that the algorithm causes all non-Byzantine agents' states to asymptotically converge to the same optimal point under appropriate assumptions. A partial convergence rate result is also provided.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 This paper considers a distributed optimization problem in the presence of Byzantine agents capable of introducing untrustworthy information into the communication network. background 1.00 0.24 0.03 asserted 0.89
s1 A resilient distributed subgradient algorithm is proposed based on graph redundancy and objective redundancy. method 0.65 0.92 0.06 asserted 0.99
s2 It is shown that the algorithm causes all non-Byzantine agents' states to asymptotically converge to the same optimal point under appropriate assumptions. result 1.00 0.93 0.85 asserted 1.00
s3 A partial convergence rate result is also provided. result 0.75 0.91 0.04 asserted 0.97

What kind of paper the classifier reports this is: method or tool 0.93 · evidence from proof or derivation 1.00.

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  • references and citing sentences
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  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

This paper considers a distributed optimization problem in the presence of Byzantine agents capable of introducing untrustworthy information into the communication network. A resilient distributed subgradient algorithm is proposed based on graph redundancy and objective redundancy. It is shown that the algorithm causes all non-Byzantine agents' states to asymptotically converge to the same optimal point under appropriate assumptions. A partial convergence rate result is also provided.

Human review

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How the literature uses it

1 arXiv paper cite this in 14 sentences

Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

Papers citing it · 11 sentences without a confident label

Uses its method or tool 1 paper
  • We will specialize a constrained consensus approach, combining a multi-dimensional resilient consensus idea in [ref] , to the resilient problem of interest here and appeal to a distributed linear equation analysis tool developed in [ref] .

    A Resilient Distributed Algorithm for Solving Linear Equations · Redundancy · 0.92

What it cites · 12 sentences without a confident label

Builds on 2 papers
Uses the method or tool of 1 paper
Compares with 1 paper
Cites as support 1 paper
  • Since each agent is assumed to have at most $β$ Byzantine neighbors, the lemma immediately implies that at least $|N_i|-(d+1)β$ among all $w_ik(t)$, $k∈N_i∩H$, are bounded below by $η$, which has been reported in [ref] .

    Resilience for Distributed Consensus with Constraints · Dynamics of Normal Agents · 0.97

Cites as background 3 papers

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