Using optimparallel.minimize_parallel() can significantly reduce the
optimization time. For an objective function with an execution time
of more than 0.1 seconds and p parameters the optimization speed
increases by up to factor 1+p when no analytic gradient is specified
and 1+p processor cores with sufficient memory are available.
A similar extension of the L-BFGS-B optimizer exists in the R package optimParallel:
To install the package run:
$ pip install optimparallelReplace scipy.optimize.minimize(method='L-BFGS-B') by optimparallel.minimize_parallel()
to execute the minimization in parallel:
from optimparallel import minimize_parallel
from scipy.optimize import minimize
import numpy as np
import time
# objective function
def f(x, sleep_secs=0.5):
print('fn')
time.sleep(sleep_secs)
return sum((x - 14)**2)
# start value
x0 = np.array([10, 20])
# minimize with parallel evaluation of 'fun' and
# its approximate gradient.
o1 = minimize_parallel(fun=f, x0=x0, args=0.5)
print(o1)
# test against scipy.optimize.minimize()
o2 = minimize(fun=f, x0=x0, args=0.5, method='L-BFGS-B')
print(all(np.isclose(o1.x, o2.x, atol=1e-10)),
np.isclose(o1.fun, o2.fun, atol=1e-10),
all(np.isclose(o1.jac, o2.jac, atol=1e-10)))The evaluated x values, fun(x), and jac(x) can be returned:
o1 = minimize_parallel(fun=f, x0=x0, args=0.5, parallel={'loginfo': True})
print(o1.loginfo)More examples are given in example.py and the Jupyter Notebook example_extended.ipynb.
Note for Windows users: It may be necessary to run minimize_parallel() in the main scope. See example_windows_os.py.
When using this package please cite:
- Gerber, F. optimparallel - A parallel version of `scipy.optimize.minimize(method='L-BFGS-B'), 2020, http://doi.org/10.5281/zenodo.3888570
- Gerber, F., and Furrer, R. optimParallel: An R package providing a parallel version of the L-BFGS-B optimization method. The R Journal, 11(1):352–358, 2019. http://doi.org/10.32614/RJ-2019-030
- Florian Gerber, [email protected], https://user.math.uzh.ch/gerber.
- Lewis Blake, https://github.com/lewisblake
Contributions via pull requests are welcome.