Approximate Duality vs Strong Duality

Developers should learn approximate duality when working on optimization problems in fields such as machine learning (e meets developers should learn strong duality when working on optimization problems in areas such as machine learning (e. Here's our take.

Approximate Duality

Nice Pick

Developers should learn approximate duality when working on optimization problems in fields such as machine learning (e

Pros

• g
• Related to: linear-programming, convex-optimization

Cons

• Specific tradeoffs depend on your use case

Strong Duality

Developers should learn strong duality when working on optimization problems in areas such as machine learning (e

Pros

• g
• Related to: linear-programming, convex-optimization

Cons

• Specific tradeoffs depend on your use case

The Verdict

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

Use Strong Duality if: You prioritize g over what Approximate Duality offers.

The Bottom Line

Approximate Duality wins

Developers should learn approximate duality when working on optimization problems in fields such as machine learning (e