https://github.com/philippeller/spherical_opt
Module providing spherically aware optimization routines
https://github.com/philippeller/spherical_opt
Last synced: 4 months ago
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Module providing spherically aware optimization routines
- Host: GitHub
- URL: https://github.com/philippeller/spherical_opt
- Owner: philippeller
- License: apache-2.0
- Created: 2019-04-12T17:05:31.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2021-11-02T16:46:50.000Z (over 3 years ago)
- Last Synced: 2025-02-13T16:18:26.698Z (4 months ago)
- Language: Jupyter Notebook
- Homepage:
- Size: 652 KB
- Stars: 0
- Watchers: 3
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# spherical_opt
Module providing spherically aware optimization routines
## Installation
`pip install spherical-opt`
## Usage
We minimize as an exmple a function `f` that takes as input a 4d array and outputs a scalar. Two of the 4 input dimesnions (indices 2 & 3) are angles on a sphere.
```python
from spherical_opt import spherical_opt
initial_points = np.zeros(4)
result = spherical_opt.spherical_opt(f,
method="CRS2",
initial_points=initial_points,
spherical_indices=((2,3),))
```
Available methods are "Nelder-Mead" and "CRS2", for local and global optimization, respectively. More options, convergence criteria, and batch parallelization are available. This is the list of parameters the `spherical_opt` function accepts:
```
Parameters:
-----------
func : callable
objective function
if batch_size == 1, func should be a scalar function
if batch_size > 1, func should be a vector function
method : string
choices of 'Nelder-Mead' and 'CRS2'
inital_points : array
providing the initial points for the algorithm, shape (N_points, N_dim)
spherical_indices : iterable of tuples
indices of spherical coordinates in pairs of (azimuth, zenith) e.g.
`[[0,1], [7,8]]` would identify indices 0 as azimuth and 1 as zenith as
spherical coordinates and 7 and 8 another pair of independent spherical
coordinates
param_boundaries : iterable of tuples
hard param boundaries. For Cartesian coordinates only. Tuples should be of form
`(param_index, (low_limit, high_limit))`. Use `None` for high or low limit
to omit the check on that side. The optimizer will clip the param at param_index to
the specified limits.
batch_size : int, optional
the number of new points proposed at each algorithm iteration
batch_size > 1 is only supported for the CRS2 method
max_iter : int
maximum number of iterations
max_calls : int
maximum number of function calls
max_noimprovement : int
break condition, maximum iterations without improvement
fstdthresh : float
break condition, if std(f(p_i)) for all current points p_i drops below
fstdthresh, minimization terminates
cstdthresh : array
break condition, if std(p_i) for all non-spherical coordinates current
points p_i drops below cstdthresh, minimization terminates, for
negative values, coordinate will be ignored
sstdthresh : array
break condition, if std(p_i) for all spherical coordinates current
points p_i drops below sstdthresh, minimization terminates, for
negative values, coordinate will be ignored
verbose : bool
rand : numpy random state or generator (optional)```