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On Extensions of Limited Memory Steepest Descent Method

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Paper breakdownThe authors' claims, conditions and measurements, as JSON Signed receiptProof this review has not been altered
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Qinmeng Zou, Frederic Magoules

Numerical Analysis · Submitted 3 Dec 2019

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

The authors' main claim Numerical results are reported which indicate that our new methods are better than the original version.

how it is stated · hedged possibility 0.99 read the record →

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arXiv ID1912.01222 (version 1)
Pith IDNYCJCWFU, Pith's permanent record of this paper and its review
CitationFrederic Magoules, Qinmeng Zou. "On Extensions of Limited Memory Steepest Descent Method." arXiv:1912.01222 (2019). https://pith.science/paper/NYCJCWFU
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The compiled record

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, 3,458 input tokens.

What the abstract itself states, as probabilities: claims first 0.04 · states limitation 0.16 · human participants 0.02 · controlled comparison 0.69 · randomized assignment 0.01 · states quantitative result 0.06 · compares with prior results 0.91 · mentions code or data availability 0.03.

The abstract, sentence by sentence

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

We present some extensions to the limited memory steepest descent method based on spectral properties and cyclic iterations. Our aim is to show that it is possible to combine sweep and delayed strategies for improving the performance of gradient methods. Numerical results are reported which indicate that our new methods are better than the original version. Some remarks on the stability and parallel implementation are shown in the end.

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 present some extensions to the limited memory steepest descent method based on spectral properties and cyclic iterations. contribution 1.00 0.90 0.09 asserted 0.99
s1 Our aim is to show that it is possible to combine sweep and delayed strategies for improving the performance of gradient methods. contribution 0.90 0.26 0.11 hedged possibility 0.89
s2 Numerical results are reported which indicate that our new methods are better than the original version. result 0.99 0.95 0.75 hedged possibility 0.99
s3 Some remarks on the stability and parallel implementation are shown in the end. contribution 0.79 0.71 0.04 asserted 0.87

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from simulation or computation 0.99.

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
levels run
  • 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 present some extensions to the limited memory steepest descent method based on spectral properties and cyclic iterations. Our aim is to show that it is possible to combine sweep and delayed strategies for improving the performance of gradient methods. Numerical results are reported which indicate that our new methods are better than the original version. Some remarks on the stability and parallel implementation are shown in the end.

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What this paper does with the arXiv papers it cites

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

What it cites · 2 sentences without a confident label

Cites as background 1 paper

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