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An integrated platform for intuitive mathematical programming modeling using LaTeX.

Charalampos P Triantafyllidis1,2, Lazaros G Papageorgiou1

  • 1Centre for Process Systems Engineering, Department of Chemical Engineering, University College London, London, United Kingdom.

Peerj. Computer Science
|April 5, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a novel platform that uses LaTeX for mathematical programming modeling. It simplifies model development, enhances error detection, and reduces programming knowledge requirements for optimization problems.

Keywords:
Algebraic Modeling LanguagesLaTeXMathematical programmingOptimizationPyomoPython

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Area of Science:

  • Optimization Theory
  • Computational Mathematics
  • Scientific Computing

Background:

  • Mathematical programming modeling traditionally requires specialized programming knowledge and algebraic modeling languages (AMLs).
  • Existing methods can be cumbersome, leading to lengthy development cycles and potential for errors.
  • Reproducibility in scientific research is often hindered by complex modeling setups.

Purpose of the Study:

  • To present a novel prototype platform for mathematical programming modeling.
  • To enable the use of LaTeX as an input language for optimization problems.
  • To simplify and accelerate the model design and development process.

Main Methods:

  • Development of a parsing engine in Python to convert LaTeX models to an Algebraic Modeling Language (AML) representation using Pyomo.
  • Integration with a Graphical User Interface (GUI) for user-friendly interaction.
  • Support for solving optimization problems via the NEOS server or locally installed solvers.

Main Results:

  • A functional platform that accepts LaTeX as input for optimization models.
  • Demonstrated simplification and speed-up in model design and development.
  • Enhanced capabilities for detecting typos and logic errors in model descriptions.
  • Reduced need for extensive programming and AML expertise.

Conclusions:

  • The developed platform offers a workable scheme for using LaTeX in mathematical programming.
  • The approach enhances the simplification, speed, and accessibility of optimization modeling.
  • This facilitates improved reproducibility and replication of scientific work in mathematical programming.