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170. Matrix inverse
easy
Generalsenior
Compute the inverse of a square matrix (when it exists) using basic linear algebra operations, which is a common building block in solving linear systems. The inverse (A^{-1}) is defined such that:
Requirements
Implement the function
python
Rules:
- Compute and return the inverse of
Aas a NumPy array. - Do not implement Gaussian elimination manually; use NumPy linear algebra operations.
- Keep the output values as floats (even if the inverse has integer-looking entries).
- No printing; just return the result.
Example
python
Output:
python
Input Signature
| Argument | Type |
|---|---|
| A | np.ndarray |
Output Signature
| Return Name | Type |
|---|---|
| value | np.ndarray |
Constraints
Input is np.ndarray
Return inverse as np.ndarray
Use np.linalg.inv; no manual elimination
Hint 1
Use NumPy’s built-in linear algebra inversion (np.linalg.inv) to compute the inverse.
Hint 2
The function should accept and return a np.ndarray directly.
Roles
ML Engineer
AI Engineer
Companies
GeneralLevels
senior
entry
Tags
numpy
matrix-inverse
linear-algebra
type-conversion
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Python EditorLn 1, Col 1