Created
October 18, 2019 19:29
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binary search of maximum on concave function
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import matplotlib.pyplot as plt | |
import numpy as np | |
import math | |
def find_maximum(f, min_x, max_x, epsilon=1e-3): | |
def binary_search(l, r, fl, fr, epsilon): | |
mid = l + (r - l) / 2 | |
fm = f(mid) | |
binary_search.eval_count += 1 | |
if (fm == fl and fm == fr) or r - l < epsilon: | |
return mid, fm | |
if fl > fm >= fr: | |
return binary_search(l, mid, fl, fm, epsilon) | |
if fl <= fm < fr: | |
return binary_search(mid, r, fm, fr, epsilon) | |
p1, f1 = binary_search(l, mid, fl, fm, epsilon) | |
p2, f2 = binary_search(mid, r, fm, fr, epsilon) | |
if f1 > f2: | |
return p1, f1 | |
else: | |
return p2, f2 | |
binary_search.eval_count = 0 | |
best_th, best_value = binary_search(min_x, max_x, f(min_x), f(max_x), epsilon) | |
print("Found maximum %f at x = %f in %d evaluations" % (best_value, best_th, binary_search.eval_count)) | |
return best_th, best_value | |
def function1(x): | |
return (5 -(x + 3)**2 - 0.1 * (x - 3) ** 4) / 100 + math.cos(1.0 + x / 3.0) * 10.0 - x * 2 | |
mx, my = find_maximum(function1, -10, 10) | |
x = np.arange(-10, 10, 0.2) | |
plt.plot(x, np.vectorize(function1)(x)) | |
plt.annotate('max', xy=(mx, my), xytext=(mx + 1, my + 0.5), | |
arrowprops=dict(facecolor='black', shrink=0.05), | |
) | |
plt.show() |
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