Whiplash Gradient Descent Algorithm

First Order Gradient Descent Algorithm for Stiff-systems.

https://github.com/1ssb/Whiplash

Ahora está siguiendo esta publicación

Whiplash Gradient Descent: A Closed Loop Gradient Descent Algorithm applied to Rosenbrock's function. Please find the paper here: https://arxiv.org/abs/2108.12883.
We introduce a novel adaptive damping technique for an inertial gradient system which finds application as a gradient descent algorithm for unconstrained optimisation. In an example using the non-convex Rosenbrock's function, we show an improvement on existing momentum-based gradient optimisation methods. Also using Lyapunov stability analysis, we demonstrate the performance of the continuous-time version of the algorithm. Using numerical simulations, we consider the performance of its discrete-time counterpart obtained by using the symplectic Euler method of discretisation.
This file contains a live MATLAB example and a Simulink simulation by Mr. Subhransu Sekhar Bhattacharjee, U7143478, ANU, developed under the supervision of Prof. Dr. Ian R. Petersen FAA, College of Engineering and Computer Science, ANU. Please direct any queries regarding the code to Mr. Subhransu Bhattacharjee at u7143478@anu.edu.au. Please use MATLAB version 2021a for running the .mlx file.

Citar como

Subhransu Sekhar Bhattacharjee & Ian R Petersen, A Closed Loop Gradient Descent Algorithm applied to Rosenbrock's function, Proceedings of the ANZCC 2021, IEEE Xplore, https://github.com/SubhransuSekharBhattacharjee-01/Whiplash, GitHub.

Información general

Compatibilidad con la versión de MATLAB

  • Compatible con cualquier versión

Compatibilidad con las plataformas

  • Windows
  • macOS
  • Linux

No se pueden descargar versiones que utilicen la rama predeterminada de GitHub

Versión Publicado Notas de la versión Action
2.3

citation updated

2.2

Note

2.1

paper link added

2.0.3

Description

2.0.2

Summary

2.0.1

Notes

2.0.0

Fixed README

1.0.0

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Para consultar o notificar algún problema sobre este complemento de GitHub, visite el repositorio de GitHub.