Comparison of np.linalg.solve

linalg_compare0.2.py

import numpy as np
import matplotlib.pyplot as plt
import timeit
import time

length = 10
ns = 2**np.arange(0,length)
t0s = np.zeros(length)
t1s = np.zeros(length)

fig, axs = plt.subplots(2,1, figsize=(6,7), tight_layout=True)

for count, n in enumerate(ns):
    
    setup = '''import numpy as np; A = np.random.random((%i,%i)); b = np.random.random((%i,%i))'''%(n,n,n,n)

    t0s[count] = timeit.timeit('Ai = np.linalg.inv(A); x0 = np.dot(Ai, b)', setup=setup, number=3)
    t1s[count] = timeit.timeit('x1 = np.linalg.solve(A, b)'               , setup=setup, number=3)
    
axs[0].plot(ns, t0s, 'ro', label='inv and dot')
axs[0].plot(ns, t1s, 'bo', label='np.linalg.solve')
axs[0].legend()
axs[0].set_yscale('log')
axs[0].set_xscale('log')
axs[0].set_ylabel('Time (sec)')
axs[0].set_xlabel('Square matrix width/height (n)')


axs[1].plot(ns, t1s/t0s, 'mo', label='Ratio of np.linalg.solve / inv and dot')
axs[1].set_xscale('log')
axs[1].set_ylabel('Ratio')
axs[1].set_xlabel('Square matrix width/height (n)')
axs[1].set_ylim(0,2)
axs[1].axhline(np.mean(t1s/t0s), color='m', lw=1, ls='dotted', label='mean: %.2f'%(np.mean(t1s/t0s)))
axs[1].axhline(1, color='k', lw=1, ls='dashed', alpha=0.5)

axs[1].legend(loc='lower right')

for ax in axs:
    ax.grid(color='k', alpha=0.05)

plt.savefig('Comparison of linalg.png', dpi=250)


plt.show()