An integrated platform for intuitive mathematical programming modeling using LaTeX

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- The initial submission of this article was received on June 3rd, 2018 and was peer-reviewed by 2 reviewers and the Academic Editor.
- The Academic Editor made their initial decision on June 26th, 2018.
- The first revision was submitted on July 19th, 2018 and was reviewed by 2 reviewers and the Academic Editor.
- The article was Accepted by the Academic Editor on August 6th, 2018.

Accept

The reviewers are satisfied with your corrections,

No comment.

No comment.

No comment.

The revised article, as well as the authors' comments, satisfactorily address my concerns from the first review.

Cite this review as

Abreu Calfa B (2018) Peer Review #1 of "An integrated platform for intuitive mathematical programming modeling using LaTeX (v0.2)". *PeerJ Computer Science*
https://doi.org/10.7287/peerj-cs.161v0.2/reviews/1

The authors have addressed all the reviewers' comments in the revised manuscript.

The authors have addressed all the reviewers' comments in the revised manuscript.

The authors have addressed all the reviewers' comments in the revised manuscript.

The authors have addressed all the reviewers' comments in the revised manuscript.

Cite this review as

Anonymous Reviewer (2018) Peer Review #2 of "An integrated platform for intuitive mathematical programming modeling using LaTeX (v0.2)". *PeerJ Computer Science*
https://doi.org/10.7287/peerj-cs.161v0.2/reviews/2

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Version 0.2 (PDF)
Download author's rebuttal letter
- submitted
Jul 19, 2018

Minor Revisions

The two reviewers have made some valid comments and raised a few issues that require your attention. Consequently, I would invite you to revise the paper taking into account their observations.

No comment (see reviewed PDF).

No comment (see reviewed PDF).

No comment (see reviewed PDF).

Cite this review as

Abreu Calfa B (2018) Peer Review #1 of "An integrated platform for intuitive mathematical programming modeling using LaTeX (v0.1)". *PeerJ Computer Science*
https://doi.org/10.7287/peerj-cs.161v0.1/reviews/1

The paper is well-organized, and the use of the English language is generally good.

In line 84, the authors state that “this is the first prototype workable scheme to address how LaTeX could be used as an input language to perform mathematical programming modeling.” This statement is not necessarily true. I encourage the authors to take a closer look at MOSAICmodeling (www.mosaic-modeling.de), which is a modeling and optimization framework based on a LaTeX-style syntax for inputting algebraic and differential equations. A comparison between MOSAICmodeling and the proposed framework would be helpful in terms of evaluating the advantages of this contribution.

In lines 92-97, 22 references are listed with no further details, which is not very useful for the reader. Also, this seems to be a fairly biased list, with contributions mostly from the process systems engineering (PSE) community and many papers co-authored by the authors of this work. I recommend selecting a more representative set of papers from a wider range of fields, considering that the topic addressed here is of interest to more than just the PSE community.

The proposed approach is sound and it makes natural sense to divide the work process of the parser into three parts: objective function, constraints, and variables. However, I have some questions that I hope the authors could answer in their response or address in the revised manuscript:

1. The authors state that the parser can handle Greek letters. How about variables that consist of a Greek and a Latin letter, e.g. \Delta t?

2. How does the parser handle superscripts? Does the parser differentiate between superscripts and exponents?

3. In the literature, summations are often defined over sets instead of explicit conditions, e.g. \sum\limits_{i \in K} with K being a subset of the full-cardinality set I. Can the parser handle this case?

4. Looking at the example in line 347, it seems to be possible to use subsets for constraint generation. How are these subsets defined as these definitions usually do not explicitly appear in the LaTeX formulation of an optimization model? Is this information possibly directly extracted from the .dat file?

The validity of the proposed approach is nicely demonstrated in an illustrative example. However, some information on the platform’s ability to detect errors in the LaTeX formulation would be useful. For example, in the illustrative parsing example, what happens if in one of the constraints, the user forgot to write one of the two indices of the variable x?

Moreover, I personally would welcome a discussion on the challenges of extending this platform to the nonlinear case.

Cite this review as

Anonymous Reviewer (2018) Peer Review #2 of "An integrated platform for intuitive mathematical programming modeling using LaTeX (v0.1)". *PeerJ Computer Science*
https://doi.org/10.7287/peerj-cs.161v0.1/reviews/2

Download
Original Submission (PDF)
- submitted
Jun 3, 2018

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