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Distributed Semi-Stochastic Optimization with Quantization Refinement

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Neil McGlohon, Stacy Patterson

Optimization and Control · Submitted 21 Mar 2016

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

The authors' main claim We present theoretical analysis and conditions for the algorithm to achieve a linear convergence rate.

how it is stated · asserted 0.97 read the record →

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arXiv ID1603.06306 (version 1)
Pith IDBEHGG2PP, Pith's permanent record of this paper and its review
CitationNeil McGlohon, Stacy Patterson. "Distributed Semi-Stochastic Optimization with Quantization Refinement." arXiv:1603.06306 (2016). https://pith.science/paper/BEHGG2PP
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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, 4,699 input tokens.

What the abstract itself states, as probabilities: claims first 0.03 · states limitation 0.12 · human participants 0.02 · controlled comparison 0.05 · randomized assignment 0.02 · states quantitative result 0.16 · compares with prior results 0.12 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

We consider the problem of regularized regression in a network of communication-constrained devices. Each node has local data and objectives, and the goal is for the nodes to optimize a global objective. We develop a distributed optimization algorithm that is based on recent work on semi-stochastic proximal gradient methods. Our algorithm employs iteratively refined quantization to limit message size. We present theoretical analysis and conditions for the algorithm to achieve a linear convergence rate. Finally, we demonstrate the performance of our algorithm through numerical simulations.

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 We consider the problem of regularized regression in a network of communication-constrained devices. background 1.00 0.13 0.02 asserted 0.88
s1 Each node has local data and objectives, and the goal is for the nodes to optimize a global objective. background 1.00 0.08 0.03 asserted 0.99
s2 We develop a distributed optimization algorithm that is based on recent work on semi-stochastic proximal gradient methods. contribution 0.52 0.89 0.05 asserted 1.00
s3 Our algorithm employs iteratively refined quantization to limit message size. method 0.92 0.89 0.06 asserted 1.00
s4 We present theoretical analysis and conditions for the algorithm to achieve a linear convergence rate. contribution 0.90 0.94 0.35 asserted 0.97
s5 Finally, we demonstrate the performance of our algorithm through numerical simulations. contribution 0.54 0.95 0.15 asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from proof or derivation 0.57.

What was examined, and what was not

sources read
  • arXiv listing abstract v1
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
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  • P abstract
not run
  • S sections
  • 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

We consider the problem of regularized regression in a network of communication-constrained devices. Each node has local data and objectives, and the goal is for the nodes to optimize a global objective. We develop a distributed optimization algorithm that is based on recent work on semi-stochastic proximal gradient methods. Our algorithm employs iteratively refined quantization to limit message size. We present theoretical analysis and conditions for the algorithm to achieve a linear convergence rate. Finally, we demonstrate the performance of our algorithm through numerical simulations.

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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

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