Alternating Direction Method of Multipliers vs Proximal Gradient Methods

Developers should learn ADMM when working on large-scale optimization problems that require distributed or parallel processing, such as in machine learning (e meets developers should learn proximal gradient methods when working on optimization problems involving non-smooth functions, such as l1 regularization in machine learning (e. Here's our take.

Alternating Direction Method of Multipliers

Nice Pick

Developers should learn ADMM when working on large-scale optimization problems that require distributed or parallel processing, such as in machine learning (e

Pros

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• Related to: convex-optimization, augmented-lagrangian-method

Cons

• Specific tradeoffs depend on your use case

Proximal Gradient Methods

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

The Verdict

These tools serve different purposes. Alternating Direction Method of Multipliers is a methodology while Proximal Gradient Methods is a concept. We picked Alternating Direction Method of Multipliers based on overall popularity, but your choice depends on what you're building.

The Bottom Line

Alternating Direction Method of Multipliers wins

Based on overall popularity. Alternating Direction Method of Multipliers is more widely used, but Proximal Gradient Methods excels in its own space.