[Python] Difference between ways to solve linear algebra equations in NumPy (inv, solve)

solve: first factorizes A using LU decomposition, then solves for xusing forward and backward substitution

inv: uses the same method to compute the inverse of A by solving for A-1 in A·A-1 = I where I is the identity*. The factorization step is exactly the same as above, but it takes more floating point operations to solve for A-1 (an n×n matrix) than for x (an n-long vector).

Reference

https://stackoverflow.com/questions/31256252/why-does-numpy-linalg-solve-offer-more-precise-matrix-inversions-than-numpy-li/31257909

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out /  Change )

Google photo

You are commenting using your Google account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s

This site uses Akismet to reduce spam. Learn how your comment data is processed.