What is the reason for numpy not throwing an error when a singular matrix is inverted?

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The reason for numpy not throwing an error when a singular matrix is inverted is that numpy uses a numerical method called "pseudoinverse" to compute the inverse of a matrix. The pseudoinverse works even when the matrix is singular or close to singular, by computing a generalized inverse that still satisfies some of the properties of a true inverse. This is useful in many applications where the matrix may not have a true inverse, but we still need to solve linear equations or perform other matrix operations. However, it's important to note that the result of inverting a singular matrix using the pseudoinverse may not be unique or may contain numerical errors, so it's always a good idea to check the condition number of the matrix and use other methods if necessary to avoid numerical issues.