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Autograder demo: Using NumPy arrays

This question uses the built-in methods in code_feedback.py to check NumPy arrays. In addition, specific library functions can be disabled by hooking them in the setup autograder code.

Given a full rank matrix ${\bf A} \in \mathbb{R}^{n \times n}$ and vector ${\bf b} \in \mathbb{R}^n$, compute ${\bf x}$ such that ${\bf x} = {\bf A}^{-1}{\bf b}$. You may use any NumPy function except for numpy.linalg.inv() and numpy.linalg.pinv().

The setup code gives the following variables:

Name	Type	Description
`n`	integer	Dimensionality of $\mathbf{A}$ and $\mathbf{b}$.
`A`	numpy array ($n \times n$)	Matrix $\mathbf{A}$.
`b`	numpy array (length $n$)	Vector $\mathbf{b}$.

Your code snippet should define the following variables:

Name	Type	Description
`x`	numpy array (length $n$)	Solution to $\mathbf{Ax}=\mathbf{b}$.

user_code.py

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QID:

demo/autograder/python/numpy

Title:

Autograder demo: Using NumPy arrays

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Started at:

2026-09-19 11:10:13 (CDT)

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