optimization: Numerical 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 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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|Versions [faq]||0.1, 0.1.1, 0.1.2, 0.1.3, 0.1.4, 0.1.5, 0.1.6, 0.1.7, 0.1.9|
|Dependencies||ad (>=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]|
|Copyright||Copyright (C) 2013 Ben Gamari|
|Maintainer||Ben Gamari <firstname.lastname@example.org>|
|Source repo||head: git clone git://github.com/bgamari/optimization.git|
|Uploaded||by BenGamari at 2018-01-24T16:37:59Z|
|Downloads||5875 total (22 in the last 30 days)|
|Rating||(no votes yet) [estimated by Bayesian average]|
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Last success reported on 2018-01-24 [all 1 reports]
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