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# In[1]:
import numpy as np
import scipy.linalg as spla
import scipy as sp
import matplotlib.pyplot as pt
from time import time
np.alterdot()
# In[2]:
n_values = (10**np.linspace(1, 3.25, 15)).astype(np.int32)
n_values
# In[3]:
def mat_mul(A):
return A.dot(A)
for name, f in [
("mat_mul", mat_mul),
("lu", spla.lu_factor),
]:
times = []
print("----->", name)
for n in n_values:
print(n)
A = np.random.randn(n, n)
start_time = time()
f(A)
times.append(time() - start_time)
pt.plot(n_values, times, label=name)
pt.grid()
pt.legend(loc="best")
pt.xlabel("Matrix size $n$")
pt.ylabel("Wall time [s]")
# * The faster algorithms make the slower ones look bad. But... it's all relative.
# * Is there a better way of plotting this?
# * Can we see the asymptotic cost ($O(n^3)$) of these algorithms from the plot?
# In[3]: