https://github.com/jfdev001/gradient-ascent-and-simulated-annealing

Gradient ascent and simulated annealing optimization algorithms for multivariate Gaussian space from scratch.
https://github.com/jfdev001/gradient-ascent-and-simulated-annealing

gradient-ascent python simulated-annealing

Last synced: 7 days ago
JSON representation

Gradient ascent and simulated annealing optimization algorithms for multivariate Gaussian space from scratch.

• Host: GitHub
• URL: https://github.com/jfdev001/gradient-ascent-and-simulated-annealing
• Owner: jfdev001
• Created: 2021-11-15T16:18:28.000Z (about 3 years ago)
• Default Branch: main
• Last Pushed: 2021-11-15T16:20:37.000Z (about 3 years ago)
• Last Synced: 2024-10-28T12:07:50.693Z (about 2 months ago)
• Topics: gradient-ascent, python, simulated-annealing
• Language: Python
• Homepage:
• Size: 3.13 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
# Gradient Ascent and Simulated Annealing for Multivariate Gaussian

Implementation of and comparison for [Hill Climbing (aka Gradient Ascent, aka greedy best first search)](https://en.wikipedia.org/wiki/Hill_climbing) and [Simulated Annealing](https://en.wikipedia.org/wiki/Simulated_annealing) on a known function space modeled by a [multivariate Gaussian distribution](https://en.wikipedia.org/wiki/Multivariate_normal_distribution).

For a summary of results, see `reoprt/report.pdf`.

# Installation

This repository makes use of Python's scientific computing/data analysis libraries NumPy and Pandas.

`pip install numpy pandas`

# Testing

Both simulated annealing and hill climbing can be tested on the generated multivariate gaussian space. Both algorithms make use of a command line interface which is available as a function named `cli` in `greedy.py`.

The input for both scripts can be shown using `python greedy.py -h` or `python sa.py -h`.

```

positional arguments:

  seed                  random number seed. (default: 0)

  d_dimensions          number of dimensions for exploration space. (default: 3)

  n_gaussians           number of gaussians. (default: 1)

optional arguments:

  -h, --help            show this help message and exit

  --iter_threshold ITER_THRESHOLD

                        max number of iterations before termination. (default: 100000)

logging:

  params for logging outputs.

  --print_last_only {True,False}

                        true to log optimization output at last step and iterations, false to log position only at all steps. (default: False)

greedy:

  params for greedy local search only.

  --step_size STEP_SIZE

                        step size for gradient ascent. (default: 0.01)

```

# Analysis

After running `sa.py` and `greedy.py` with the same arguments related to the Gaussian space (`seed `, `d_dimensions `, `n_gaussians`), the last two arguments being in combos.txt, `process.py` can be run to compare the performance of the algorithms.

# Future Work

Arbitrary function spaces and numerical differentiation might be implemented to avoid strictly traversing the multivariate Gaussian space.