Relative cost of matrix factorizations¶
In [1]:
import matplotlib as mpl
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
import numpy.linalg as npla
import scipy.linalg as spla
from time import time
In [2]:
# https://matplotlib.org/users/customizing.html
# print(plt.style.available) # uncomment to print all styles
import seaborn as sns
sns.set(font_scale=2)
plt.style.use('seaborn-whitegrid')
mpl.rcParams['figure.figsize'] = (10.0, 8.0)
In [3]:
n_values = (10**np.linspace(1, 3, 15)).astype(np.int32)
n_values
Out[3]:
In [6]:
def matmat(A):
A @ A
for name, f in [
("lu", spla.lu_factor),
("matmat", matmat),
("svd", npla.svd)
]:
times = []
print("----->", name)
for n in n_values:
A = np.random.randn(n, n)
start_time = time()
f(A)
delta_time = time() - start_time
times.append(delta_time)
print("%d - %f" % (n, delta_time))
plt.plot(n_values, times, label=name)
plt.legend(loc="best")
plt.xlabel("Matrix size $n$")
plt.ylabel("Wall time [s]");
