[Solved] What is the main difference between scipy.sparse.linalg.eigs and scipy.sparse.linalg.eigsh? — Ans: eigs works for any square matrix, while eigsh is optimized for symmetric or... | SciPy

SciPy - Sparse Linear Algebra

What is the main difference between scipy.sparse.linalg.eigs and scipy.sparse.linalg.eigsh?

A<code>eigs</code> returns eigenvectors only, while <code>eigsh</code> returns eigenvalues only.

B

<code>eigs</code> works for any square matrix, while <code>eigsh</code> is optimized for symmetric or Hermitian matrices.

C<code>eigsh</code> works for any square matrix, while <code>eigs</code> only works for diagonal matrices.

D<code>eigsh</code> is used for non-square matrices, while <code>eigs</code> is for square matrices.

Step-by-Step Solution

Solution:

Step 1: Understand the function purposes
eigs is designed to find eigenvalues and eigenvectors of any square matrix, including non-symmetric ones. eigsh is a specialized version optimized for symmetric or Hermitian matrices, which are common in many applications.
Step 2: Compare matrix types each function supports
eigsh takes advantage of symmetry to be faster and more accurate, but it requires the matrix to be symmetric. eigs has no such restriction but may be slower.
Final Answer:
eigs works for any square matrix, while eigsh is optimized for symmetric or Hermitian matrices. -> Option B
Quick Check:
Function specialization = C [OK]

Quick Trick: Remember: eigsh = symmetric only, eigs = any square matrix [OK]

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What is the main difference between scipy.sparse.linalg.eigs and scipy.sparse.linalg.eigsh?