SciPy - Sparse Linear Algebra
You have a large sparse symmetric adjacency matrix of a graph. To efficiently compute the three smallest eigenvalues, which method is best?
You have a large sparse symmetric adjacency matrix of a graph. To efficiently compute the three smallest eigenvalues, which method is best?
hard📝 Application Q8 of 15
Step-by-Step Solution
Solution:
Step 1: Matrix properties
Matrix is symmetric and sparse.Step 2: Choose solver and parameters
eigshis optimized for symmetric sparse matrices;which='SM'finds smallest magnitude eigenvalues.Final Answer:
Use eigsh with which='SM' -> Option AQuick Check:
eigsh + which='SM' for smallest eigenvalues [OK]
Quick Trick: eigsh with which='SM' for smallest eigenvalues [OK]
Common Mistakes:
- Using eigs for symmetric matrices
- Using which='LA' to find smallest eigenvalues
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