Computing in Operations Research Using Julia

The state of numerical computing is currently characterized by a divide between highly efficient yet typically cumbersome low-level languages such as C, C++, and Fortran and highly expressive yet typically slow high-level languages such as Python and MATLAB. This paper explores how Julia, a modern p...

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Bibliographic Details
Published inINFORMS journal on computing Vol. 27; no. 2; pp. 238 - 248
Main Authors Lubin, Miles, Dunning, Iain
Format Journal Article
LanguageEnglish
Published INFORMS 22.03.2015
Institute for Operations Research and the Management Sciences
Subjects
algebraic modeling
Algorithms
Computer programming
Engineering research
Mathematical optimization
metaprogramming
Methods
Programming languages
scientific computing
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ISSN1091-9856
1526-5528
DOI10.1287/ijoc.2014.0623

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Summary:The state of numerical computing is currently characterized by a divide between highly efficient yet typically cumbersome low-level languages such as C, C++, and Fortran and highly expressive yet typically slow high-level languages such as Python and MATLAB. This paper explores how Julia, a modern programming language for numerical computing that claims to bridge this divide by incorporating recent advances in language and compiler design (such as just-in-time compilation), can be used for implementing software and algorithms fundamental to the field of operations research, with a focus on mathematical optimization. In particular, we demonstrate algebraic modeling for linear and nonlinear optimization and a partial implementation of a practical simplex code. Extensive cross-language benchmarks suggest that Julia is capable of obtaining state-of-the-art performance. Data, as supplemental material, are available at http://dx.doi.org/10.1287/ijoc.2014.0623 .
ISSN:1091-9856
1526-5528
DOI:10.1287/ijoc.2014.0623