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Article
Peer-Review Record

Adaptive Reconstruction of Imperfectly Observed Monotone Functions, with Applications to Uncertainty Quantification

Algorithms 2020, 13(8), 196; https://doi.org/10.3390/a13080196
by Luc Bonnet 1,2, Jean-Luc Akian 1, Éric Savin 1,* and T. J. Sullivan 3,4
Reviewer 1: Anonymous
Reviewer 2:
Algorithms 2020, 13(8), 196; https://doi.org/10.3390/a13080196
Submission received: 10 July 2020 / Revised: 5 August 2020 / Accepted: 10 August 2020 / Published: 13 August 2020

Round 1

Reviewer 1 Report

Report:
In this paper Authors present an adaptive algorithm for reconstruction of a monotonic function. The algorithm is clearly described and illustrated with some figures. Morover, authors analyse the convergence of the algorithm and its performance against some benchmark functions. The authors proposal to use imperfect pointwise evaluations of the target function seems interesting. Overall, the paper is well written and I recommend publication in Algorithms.

Author Response

Please see the attachment for reply to comments by the second referee.

Author Response File: Author Response.pdf

Reviewer 2 Report

Report

Comments for author File: Comments.pdf

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

The authors have carefully revised the manuscript following the suggestions from the report.

The manuscript is now suitable for publication, in my opinion.

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