# Computing in Operations Research using Julia

arXiv: https://arxiv.org/abs/1312.1431 · HTML: https://mlsanity.com/paper/1312.1431/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Dec 2013.

## Built on by (most cited first, 2 of 2)

- [Constrained Optimization of Rank-One Functions with Indicator Variables](https://mlsanity.com/paper/2303.18158/) (2023)
- [Randomized Policy Optimization for Optimal Stopping](https://mlsanity.com/paper/2203.13446/) (2022)

## Abstract (arXiv)

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.