Projected Gradient Descent vs Stochastic Gradient Descent

Developers should learn PGD when dealing with optimization problems where solutions must adhere to specific constraints, such as in machine learning for training models with bounded parameters (e meets developers should learn sgd when working with large-scale machine learning problems, such as training deep neural networks on massive datasets, where computing the full gradient over all data points is computationally prohibitive. Here's our take.

Projected Gradient Descent

Nice Pick

Developers should learn PGD when dealing with optimization problems where solutions must adhere to specific constraints, such as in machine learning for training models with bounded parameters (e

Pros

• g
• Related to: gradient-descent, convex-optimization

Cons

• Specific tradeoffs depend on your use case

Stochastic Gradient Descent

Developers should learn SGD when working with large-scale machine learning problems, such as training deep neural networks on massive datasets, where computing the full gradient over all data points is computationally prohibitive

Pros

• It is particularly useful in online learning scenarios where data arrives in streams, and models need to be updated incrementally
• Related to: gradient-descent, optimization-algorithms

Cons

• Specific tradeoffs depend on your use case

The Verdict

These tools serve different purposes. Projected Gradient Descent is a concept while Stochastic Gradient Descent is a methodology. We picked Projected Gradient Descent based on overall popularity, but your choice depends on what you're building.

The Bottom Line

Projected Gradient Descent wins

Based on overall popularity. Projected Gradient Descent is more widely used, but Stochastic Gradient Descent excels in its own space.