Proximal Gradient Methods vs Subgradient Methods

Developers should learn proximal gradient methods when working on optimization problems involving non-smooth functions, such as L1 regularization in machine learning (e meets developers should learn subgradient methods when working with optimization problems involving non-differentiable convex functions, such as in training support vector machines or solving large-scale linear programs. Here's our take.

Proximal Gradient Methods

Nice Pick

Developers should learn proximal gradient methods when working on optimization problems involving non-smooth functions, such as L1 regularization in machine learning (e

Pros

• g
• Related to: optimization-algorithms, convex-optimization

Cons

• Specific tradeoffs depend on your use case

Subgradient Methods

Developers should learn subgradient methods when working with optimization problems involving non-differentiable convex functions, such as in training support vector machines or solving large-scale linear programs

Pros

• They are particularly useful in machine learning for handling L1 regularization (e
• Related to: convex-optimization, gradient-descent

Cons

• Specific tradeoffs depend on your use case

The Verdict

Use Proximal Gradient Methods if: You want g and can live with specific tradeoffs depend on your use case.

Use Subgradient Methods if: You prioritize they are particularly useful in machine learning for handling l1 regularization (e over what Proximal Gradient Methods offers.

The Bottom Line

Proximal Gradient Methods wins

Developers should learn proximal gradient methods when working on optimization problems involving non-smooth functions, such as L1 regularization in machine learning (e