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:
- Shortest Path as Linear Programming
- Proves of the convexity or concavity of mathematical functions https://github.com/computeVision/optimization/blob/master/ocs_hw2/lorenz.pdf
- Neuronal Network
- Logistic Regression
- Shortest Path in a Labyrinth
Python 2.7 and Numpy
These are simple python scripts. Can be executed in a shell.
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
- Peter Lorenz.
GNU General Public License v3.0