https://github.com/kodedninja/optimizations
educational implementations of numerical optimization methods
https://github.com/kodedninja/optimizations
Last synced: 4 months ago
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educational implementations of numerical optimization methods
- Host: GitHub
- URL: https://github.com/kodedninja/optimizations
- Owner: kodedninja
- Created: 2021-06-09T06:59:36.000Z (about 5 years ago)
- Default Branch: main
- Last Pushed: 2021-07-20T19:06:17.000Z (almost 5 years ago)
- Last Synced: 2025-02-22T07:23:38.655Z (over 1 year ago)
- Language: Julia
- Size: 44.9 KB
- Stars: 0
- Watchers: 3
- Forks: 1
- Open Issues: 0
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Metadata Files:
- Readme: readme.md
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README
# optimizations
A few optimization methods implemented for those (like me) who understand and learn through code. Homework for a Numerical Optimizations course, following the numerical optimization bible by Nocedal & Wright.
The `unconstrained` contains the unconstrained minimization methods gradient descent, Newton's method, conjugate gradient, and the quasi-Newton methods, BFGS and SR1. Each file contains the algorithm implementation and some code to run it on some problems. `unconstrained/utils` contains `functions.jl`, a set of problems and helpers to generate these, and `utils.jl`, utilities shared across the methods: backtracking line search, gradient & Hessian approximation.