Eigenvalue Problems vs QR Decomposition

Developers should learn eigenvalue problems when working on applications involving linear transformations, data analysis (e meets developers should learn qr decomposition when working on applications involving linear algebra, such as machine learning algorithms (e. Here's our take.

Eigenvalue Problems

Nice Pick

Developers should learn eigenvalue problems when working on applications involving linear transformations, data analysis (e

Pros

• g
• Related to: linear-algebra, numerical-methods

Cons

• Specific tradeoffs depend on your use case

QR Decomposition

Developers should learn QR decomposition when working on applications involving linear algebra, such as machine learning algorithms (e

Pros

• g
• Related to: linear-algebra, matrix-factorization

Cons

• Specific tradeoffs depend on your use case

The Verdict

Use Eigenvalue Problems if: You want g and can live with specific tradeoffs depend on your use case.

Use QR Decomposition if: You prioritize g over what Eigenvalue Problems offers.

The Bottom Line

Eigenvalue Problems wins

Developers should learn eigenvalue problems when working on applications involving linear transformations, data analysis (e