optimization: Numerical optimization

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These are a set of implementations of various numerical optimization methods in Haskell. Note that these implementations were originally written as part of a class project; while at one point they worked no attention has been given to numerical stability or the many other potential difficulties of implementing robust numerical methods. That being said, they should serve to succinctly illustrate a number of optimization techniques from the modern optimization literature.

Those seeking a high-level overview of some of these methods are referred to Stephen Wright's excellent tutorial from NIPS 2010 http://videolectures.net/nips2010_wright_oaml/. A deeper introduction can be found in Boyd and Vandenberghe's /Convex Optimization/ which available freely online, http://web.stanford.edu/~boyd/cvxbook/. Vandenberghe's lecture at the 2009 Machine Learning Summer School may also be of interest http://videolectures.net/mlss09uk_vandenberghe_co/.


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Versions0.1, 0.1.1, 0.1.2, 0.1.3, 0.1.4, 0.1.5, 0.1.6, 0.1.7, 0.1.9, 0.1.9
Change logCHANGELOG.markdown
Dependenciesad (>=3.4 && <4.4), base (>=4.4 && <5), distributive (>=0.3 && <0.6), linear (>=1.16 && <2.0), semigroupoids (>=3.0 && <6.0), vector (>=0.10 && <1.0) [details]
LicenseBSD-3-Clause
CopyrightCopyright (C) 2013 Ben Gamari
AuthorBen Gamari
MaintainerBen Gamari <bgamari@gmail.com>
CategoryMath
Home pagehttp://github.com/bgamari/optimization
Bug trackerhttp://github.com/bgamari/optimization/issues
Source repositoryhead: git clone git://github.com/bgamari/optimization.git
UploadedWed Jan 24 16:37:31 UTC 2018 by BenGamari

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Readme for optimization-0.1.9

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optimization

These are a set of implementations of various numerical optimization methods in Haskell. Note that these implementations were originally written as part of a class project; while at one point they worked no attention has been given to numerical stability or the many other potential difficulties of implementing robust numerical methods. That being said, they should serve to succinctly illustrate a number of optimization techniques from the modern optimization literature.

Those seeking a high-level overview of some of these methods are referred to Stephen Wright's excellent tutorial from NIPS

  1. A deeper introduction can be found in Boyd and Vandenberghe's Complex Optimization which available freely online.

Contact Information

Contributions and bug reports are welcome!

Please feel free to contact me through github or on the #haskell IRC channel on irc.freenode.net.