SimplexBenchmarks

Miles Lubin and Iain Dunning gave an excellent presentation at MIT's IAP tutorial on Julia in January where they benchmarked rather non-trival code for doing linear programming in a variety of languages: https://github.com/JuliaLang/julia-tutorial/blob/master/NumericalOptimization/pres… http://bit.ly/VQ9rDd (shortened version since that's cut off) See page 7, "Simplex Benchmarks". Essentially, they implemented the basics you need to do a fast simplex linear programming algorithm in each of C++, Julia, Matlab and Python. Their results were in line with the Julia home page benchmarks: Julia is between 1-2x slower than good C/C++ code. PyPy and Matlab are 10-20x slower. Python is 80-400x slower. The code can be found here: https://github.com/mlubin/SimplexBenchmarks These results are fairly typical.