example.py

"""
Example of `minimize_parallel()`.

On a Windows OS it might be necessary to run `minimize_parallel()`
in the main scope. See `example_windows_os.py`.
"""

from optimparallel import minimize_parallel
from scipy.optimize import minimize
import numpy as np
import time
import matplotlib.pyplot as plt


# 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(method='L-BFGS-B')
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)),
)

# timing results
o1_start = time.time()
_ = minimize_parallel(fun=f, x0=x0, args=0.5)
o1_end = time.time()
o2_start = time.time()
_ = minimize(fun=f, x0=x0, args=0.5, method="L-BFGS-B")
o2_end = time.time()
print(
    "Time parallel {:2.2}\nTime standard {:2.2} ".format(
        o1_end - o1_start, o2_end - o2_start
    )
)

# loginfo -------------------------------------
o1 = minimize_parallel(fun=f, x0=x0, args=0.5, parallel={"loginfo": True})
print(o1.loginfo)

x1, x2 = o1.loginfo["x"][:, 0], o1.loginfo["x"][:, 1]
plt.plot(x1, x2, "-o")
for i, _ in enumerate(x1):
    plt.text(x1[i] + 0.2, x2[i], "f = {a:3.3f}".format(a=o1.loginfo["fun"][i, 0]))
plt.xlabel("x[0]")
plt.ylabel("x[1]")
plt.xlim(right=x1[-1] + 1)
plt.show()


# example with gradient -----------------------
def g(x, sleep_secs=0.5):
    print("gr")
    time.sleep(sleep_secs)
    return 2 * (x - 14)


o3 = minimize_parallel(fun=f, x0=x0, jac=g, args=0.5)
o4 = minimize(fun=f, x0=x0, jac=g, args=0.5, method="L-BFGS-B")

print(
    all(np.isclose(o3.x, o4.x, atol=1e-10)),
    np.isclose(o3.fun, o4.fun, atol=1e-10),
    all(np.isclose(o3.jac, o4.jac, atol=1e-10)),
)