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This article mainly introduces the relevant knowledge of how to use np.linalg in numpy, the content is detailed and easy to understand, the operation is simple and fast, and it has a certain reference value. I believe you will gain something after reading this article on how to use np.linalg in numpy. Let's take a look at it.

Np.linalg.norm

As the name implies, linalg=linear+algebra linalg=linear+algebra\ mathrm {linalg=linear+algebra}, norm norm\ mathrm {norm} represents the norm. The first thing to note is that the norm is a measure of a vector (or matrix), which is a scalar:

First, help (np.linalg.norm) looks at its document:

Norm (x, ord=None, axis=None, keepdims=False) 1

Here we only explain the commonly used settings, x x\ mathrm {x} represents the vector to be measured, and ord ord\ mathrm {ord} indicates the type of norm

> x = np.array ([3,4]) > np.linalg.norm (x) 5. > > np.linalg.norm (x, ord=2) 5. > np.linalg.norm (x, ord=1) 7. > np.linalg.norm (x, ord=np.inf) 4123456789

A small corollary of norm theory tells us that ℓ 1 ≥ℓ 2 ≥ℓ ∞ ℓ 1 ≥ℓ 2 ≥ℓ∞

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