https://github.com/lucasbotang/gradient_descent_for_convex_quadratic
Implement the classical gradient descent method using different step size rules
https://github.com/lucasbotang/gradient_descent_for_convex_quadratic
gradient-descent nonlinear-optimization
Last synced: about 1 year ago
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Implement the classical gradient descent method using different step size rules
- Host: GitHub
- URL: https://github.com/lucasbotang/gradient_descent_for_convex_quadratic
- Owner: LucasBoTang
- License: mit
- Created: 2020-03-15T19:27:08.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2020-03-15T19:47:49.000Z (over 6 years ago)
- Last Synced: 2025-04-12T05:37:50.828Z (about 1 year ago)
- Topics: gradient-descent, nonlinear-optimization
- Language: Python
- Size: 29.3 KB
- Stars: 5
- Watchers: 0
- Forks: 1
- Open Issues: 0
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Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Gradient Descent for Convex Quadratic Function
Gradient descent is a first-order iterative optimization algorithm for finding a local minimum of a differentiable function.
## Method
3 different gradient descent method are implmented:
- Constant Step Size: Gradient descent with fixed step size 0.3
- Exact Line Search: Gradient descent with exact line search is a variant of gradient descent where we perform an exact line search along the line of the gradient vector to move to the point of global minimum along that line.
- Armijo: Gradient descent with Armijo rule
## Performence
