Improvements for JuliaNLSolvers could be made in three parts: documentation, benchmarks and functionality.

Currently, LsqFit.jl, and NLsolve.jl only have example codes in their READMEs. Documentation for these projects will be good references for users. Beginner’s guide would dramatically reduce the learning curve for new users. Examples are also needed for Optim.jl, LsqFit.jl and NLsolve.jl to show people the Julia “pipeline” in areas such as Machine Learning, Statistics and Economics. Meanwhile, codes in documentation and examples can be used for testing.

Benchmarks are essential to show the advantage of Julia and therefore may persuade outside users to switch. By comparing with SciPy, it will also help guide development and find bugs.

LsqFit.jl is still on an early development stage and has large potential to improve. For example, allowing non-vectorized functions for LsqFit.jl will help it apply to more problems.

Organization

Student

Jiawei Li

Mentors

  • Mike Innes
  • Christopher Rackauckas
  • Patrick Kofod Mogensen
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2018