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Optimization

A collection of Optimization Algorithms.

  • Mathematical foundations of optimization: Optimization problems, local/global minima, optimality conditions, convexity
  • Unconstrained optimization: gradient descent, conjugate gradients, Newton's method, quasi-Newton methods
  • Constrained optimization: Karush-Kuhn-Tucker conditions, Lagrange multipliers
  • Linear programming: Simplex method, interior point methods

Build Status

Directores:

  1. Shortest Path as Linear Programming Result
  2. Proves of the convexity or concavity of mathematical functions https://github.com/computeVision/optimization/blob/master/ocs_hw2/lorenz.pdf
  3. Neuronal Network Result
  4. Logistic Regression Result
  5. Shortest Path in a Labyrinth Result Result

Installation

Python 2.7 and Numpy

Usage

These are simple python scripts. Can be executed in a shell.

History

This file started with LinkerScript Parser v0.0 and tracks the feature of this tool. Each line will describe a single addition/removement/change and adhere to the following format:

<Author> <Type> : <Textual_description_without_linebreak>

Authors (so far):

  • PL Peter Lorenz

Type:

  • + Addition
  • - Removement
  • # Modification
  • ~ Fix
  • ! Misc.
  • (very significant entries should be in upper case and prefixed with "-----" )

Table of changes:

  • PL + The initial setup is done. Version 0.0
  • todo add changes

Credits

  • Peter Lorenz.

License

GNU General Public License v3.0