期刊
INFORMS JOURNAL ON COMPUTING
卷 27, 期 2, 页码 238-248出版社
INFORMS
DOI: 10.1287/ijoc.2014.0623
关键词
algebraic modeling; scientific computing; programming languages; metaprogramming
资金
- DOE Computational Science Graduate Fellowship [DE-FG02-97ER25308]
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.
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