A globally convergent fast iterative shrinkage-thresholding algorithm with a new momentum factor for single and multi-objective convex optimization
Project description
zfista : A globally convergent fast iterative shrinkage-thresholding algorithm with a new momentum factor for single and multi-objective (convex) optimization
This code repository provides a solver for the proximal gradient method (ISTA) and its acceleration (FISTA) for both single and multi-objective optimization problems, including the experimental code for the Paper1 and Paper2.
An accelerated proximal gradient method for multiobjective optimization
Hiroki Tanabe, Ellen H. Fukuda, and Nobuo Yamashita
A globally convergent fast iterative shrinkage-thresholding algorithm with a new momentum factor for single and multi-objective convex optimization
Hiroki Tanabe, Ellen H. Fukuda, and Nobuo Yamashita
The solver can deal with the unconstrained problem written by $$\min_{x \in \mathbf{R}^n} \quad F(x) \coloneqq f(x) + g(x),$$ where $f$ and $g$ are scalar or vector valued function, $f$ is continuously differentiable, $g$ is closed, proper and convex. Note that FISTA also requires $f$ to be convex.
- Documentation: https://zalgo3.github.io/zfista/
Requirements
- Python 3.8 or later
Install
pip install zfista
Quickstart
from zfista import minimize_proximal_gradient
help(minimize_proximal_gradient)
Examples
You can run some examples on jupyter notebooks.
jupyter notebook
Testing
You can run all tests by
python -m unittest discover
Benchmark
You can run the benchmark by
python runtests.py
Project details
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