An integrated platform for intuitive mathematical programming modeling using LaTeX

Charalampos P Triantafyllidis; Lazaros G Papageorgiou

doi:10.7717/peerj-cs.161

An integrated platform for intuitive mathematical programming modeling using LaTeX

PeerJ Comput Sci. 2018 Sep 10:4:e161. doi: 10.7717/peerj-cs.161. eCollection 2018.

Authors

Charalampos P Triantafyllidis^{1

2}, Lazaros G Papageorgiou¹

Affiliations

¹ Centre for Process Systems Engineering, Department of Chemical Engineering, University College London, London, United Kingdom.
² Smith School of Enterprise and the Environment, University of Oxford, Oxford, United Kingdom.

Abstract

This paper presents a novel prototype platform that uses the same LaTeX mark-up language, commonly used to typeset mathematical content, as an input language for modeling optimization problems of various classes. The platform converts the LaTeX model into a formal Algebraic Modeling Language (AML) representation based on Pyomo through a parsing engine written in Python and solves by either via NEOS server or locally installed solvers, using a friendly Graphical User Interface (GUI). The distinct advantages of our approach can be summarized in (i) simplification and speed-up of the model design and development process (ii) non-commercial character (iii) cross-platform support (iv) easier typo and logic error detection in the description of the models and (v) minimization of working knowledge of programming and AMLs to perform mathematical programming modeling. Overall, this is a presentation of a complete workable scheme on using LaTeX for mathematical programming modeling which assists in furthering our ability to reproduce and replicate scientific work.

Keywords: Algebraic Modeling Languages; LaTeX; Mathematical programming; Optimization; Pyomo; Python.

Grants and funding

This work was supported by The Leverhulme Trust under Grant (No. RPG-2015-240) and the UK Engineering and Physical Sciences Research Council (No. EP/M027856/1). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.