  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.
