Julia 0.6 20180804 AbstractAlgebra: tests_pass → tests_fail. Arrow: tests_pass → tests_fail. BasisFunctionExpansions: tests_pass → tests_fail. BayesianNonparametrics: tests_pass → tests_fail. BlackBoxOptim: tests_fail → tests_pass. ChaosTools: tests_fail → tests_pass. DataDeps: tests_fail → tests_pass. DrakeVisualizer: tests_pass → tests_fail. Flux: tests_pass → tests_fail. HORIZONS: tests_pass →
A Lisper's first impression of Julia
I have recently looked at Julia, a new programming language developed at MIT that promises to be a dynamic programming language that is suitable for scientific computing with a high-performance implementation. It is an interesting project that heavily borrows from Common Lisp, Dylan, and Scheme, and you can rightfully argue that Julia itself is actually a Lisp dialect. While I wasn’t very impresse
I was introduced to Julia recently after hearing of Stefan Karpinski while attending HackerSchool. Julia is marketed as a super fast high performance scientific computing language that can reach speeds close to native C code. After attending a conference for Python quants in NYC and heard Dr. Yves J. Hilpisch speak on the speed of Python for financial analytics I decided to put Julia up against th
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