Implementation of linesearch Optimization Algorithms in Python

Related tags

Machine Learningnonlinear-optimizationconjugate-gradient-descentgradient-descent-algorithmnewtons-methodtrust-region-dogleg-algorithmarmijo-backtrackrosenbrock-function-optimizationbfgs-algorithm
Overview

Nonlinear Optimization Algorithms

During my time as Scientific Assistant at the Karlsruhe Institute of Technology (Germany) I implemented various Optimization Algorithms solving unrestricted nonlinear Problems; Gradient-Descent-Method, Newton-Method, Conjugate-Gradient-Descent-Method, BFGS-Method and a Trust-Region-Method in Python.

In addition, I implemented an Armijo linesearch.

The code is implemented in an object-oriented manner, whereby each method is implemented in a class (bfgs.py, cg.py, gradv.py, newtonm.py and tr.py) and executed via the ros_test.py script. In the script ros_test.py the Rosenbrock function was implemented, which is minimized to a given starting point x_0 with each method.

Owner
Paul
M.Sc student at Columbia University and Karlsruhe Institute of Technology. Passionate about Mathematical Modeling, Statistics and Optimization.
Paul
GitHub Repository
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