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Computing in Operations Research using Julia

Miles Lubin (mlubin***at***mit.edu)
Iain Dunning (idunning***at***mit.edu)

Abstract: 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 which 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.

Keywords: algebraic modeling; scientific computing; programming languages; metaprogramming; domain-specific languages

Category 1: Optimization Software and Modeling Systems (Optimization Software Design Principles )

Category 2: Optimization Software and Modeling Systems (Optimization Software Benchmark )

Category 3: Optimization Software and Modeling Systems (Modeling Languages and Systems )

Citation: Published in INFORMS Journal on Computing: http://dx.doi.org/10.1287/ijoc.2014.0623

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Entry Submitted: 05/19/2013
Entry Accepted: 05/20/2013
Entry Last Modified: 03/18/2015

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