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

Directores:

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

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