Confusion with np.linalg.solve when using non-square matrices in Python
I'm updating my dependencies and Can someone help me understand I'm confused about I'm stuck on something that should probably be simple... I'm trying to use `np.linalg.solve` to solve a system of equations represented by a non-square matrix, but I'm receiving a `LinAlgError`. I understand that `np.linalg.solve` is meant for square matrices, but Iām unsure how to handle my specific scenario. I have the following code snippet that raises the behavior: ```python import numpy as np # Coefficients of my equations A = np.array([[2, 1], [1, 1], [1, 3]]) # Non-square matrix (3x2) B = np.array([4, 2, 5]) # Right-hand side (3x1) # Attempt to solve the equations x = np.linalg.solve(A, B) # This raises LinAlgError ``` The behavior message I receive is: `LinAlgError: Last 2 dimensions of the array must be square`. I am aware that for non-square systems, I should use `np.linalg.lstsq`, but I'm confused about how to apply it correctly. I've tried the following to use `np.linalg.lstsq`, but I'm not sure if I'm handling the outputs correctly: ```python x, residuals, rank, s = np.linalg.lstsq(A, B, rcond=None) print('Solution:', x) print('Residuals:', residuals) ``` When I run this, the output is a solution array, but the `residuals` array is empty, and I'm not entirely sure what that implies. Could someone clarify how to properly interpret the output of `np.linalg.lstsq` in this context? Also, are there any best practices for handling such scenarios with non-square matrices? Any insights into why the residuals might be empty would also be appreciated. My development environment is macOS. I'd be grateful for any help. I've been using Python for about a year now. Any ideas how to fix this? My team is using Python for this mobile app. How would you solve this?