Safe Haskell | Safe-Inferred |
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- mirrorDescent :: (Num a, Additive f) => LineSearch f a -> (f a -> f a) -> (f a -> f a) -> (f a -> f a) -> f a -> [f a]
- module Optimization.LineSearch

# Documentation

:: (Num a, Additive f) | |

=> LineSearch f a | line search method |

-> (f a -> f a) | strongly convex function, |

-> (f a -> f a) | dual of |

-> (f a -> f a) | gradient of function |

-> f a | starting point |

-> [f a] | iterates |

Mirror descent method.

Originally described by Nemirovsky and Yudin and later elucidated
by Beck and Teboulle, the mirror descent method is a generalization of
the projected subgradient method for convex optimization.
Mirror descent requires the gradient of a strongly
convex function `psi`

(and its dual) as well as a way to get a
subgradient for each point of the objective function `f`

.

# Step size methods

module Optimization.LineSearch