Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 525

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Reaching approximate agreement in the presence of faults

Open access
Dolev, Danny, Lynch, Nancy A., Pinter, Shlomit S., Stark, Eugene W., Weihl, William E.·1986·Journal of the ACM

Abstract

This paper considers a variant of the Byzantine Generals problem, in which processes start with arbitrary real values rather than Boolean values or values from some bounded range, and in which approximate, rather than exact, agreement is the desired goal. Algorithms are presented to reach approximate agreement in asynchronous, as well as synchronous systems. The asynchronous agreement algorithm is an interesting contrast to a result of Fischer et al, who show that exact agreement with guaranteed termination is not attainable in an asynchronous system with as few as one faulty process. The algorithms work by successive approximation, with a provable convergence rate that depends on the ratio between the number of faulty processes and the total number of processes. Lower bounds on the convergence rate for algorithms of this form are proved, and the algorithms presented are shown to be optimal.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1145/5925.5931

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 1 references

Cited by

See all 525

Metadata

Authors
Dolev, D., Lynch, N. A., Pinter, S. S., Stark, E. W., & Weihl, W. E.
Venue
Journal of the ACM
Published
1986-05
Volume
33
Issue
3
Pages
499-516
ISSN
  • 0004-5411 (ISSN-L)
  • 1557-735X
OpenAlex
W2126906505
Cited by
525
References
1
FWCI
10.66
Type
journal article
Language
en
Open access
bronze

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Reaching approximate agreement in the presence of faults. https://app.cochat.ai/papers/10.1145/5925.5931
The paper
Dolev, D., Lynch, N. A., Pinter, S. S., Stark, E. W., & Weihl, W. E. (1986). Reaching approximate agreement in the presence of faults. Journal of the ACM. https://doi.org/10.1145/5925.5931

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.