Optimizers

Cheat sheet to help you choose the right optimizer based on different problem characteristics like dataset size, noise, and convergence speed:

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🛠 Optimizer Cheat Sheet

Optimizer	Best for	Pros ✅	Cons ❌
SGD (Stochastic Gradient Descent)	Large datasets, online learning	Efficient for large data, generalizes well	Requires careful tuning of learning rate, slower convergence
Momentum SGD	High-dimensional problems	Faster convergence than vanilla SGD	Still needs learning rate tuning
Nesterov Accelerated Gradient (NAG)	Smooth, convex problems	Improves momentum by looking ahead	Slightly more complex implementation
AdaGrad	Sparse data (e.g., NLP, recommendation systems)	No need to tune learning rate per feature	Learning rate decreases too much over time
RMSProp	Non-stationary problems (e.g., deep learning, RNNs)	Adapts learning rate, prevents AdaGrad's decay issue	Requires tuning decay parameter
Adam (Adaptive Moment Estimation)	Most deep learning tasks	Combines momentum + RMSProp, adaptive learning rate	Can lead to poor generalization (overfits sometimes)
AdamW	Deep learning with weight decay	Better generalization than Adam	Requires tuning weight decay
Adadelta	Limited memory situations	No need to set learning rate	Less commonly used
Nadam (Adam + NAG)	Faster convergence	Improves Adam with look-ahead momentum	More computation than Adam
L-BFGS (Limited-memory BFGS)	Small datasets, convex problems	Second-order optimization without full Hessian	Not scalable to deep learning

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🔍 How to Choose?

🔢 Based on Dataset Size

• Small datasets → L-BFGS, Adam

• Medium datasets → Adam, RMSProp

• Large datasets → SGD, Momentum SGD, AdamW

⚡️ Based on Convergence Speed

• Fast convergence required? → Adam, Nadam

• Slower but stable? → SGD with Momentum, RMSProp

🌊 Based on Data Type

• Sparse data (e.g., NLP, embeddings) → AdaGrad, RMSProp

• Dense data (e.g., vision, tabular) → Adam, SGD with Momentum

🔀 Based on Noise in Gradients

• Noisy gradients (e.g., reinforcement learning, RNNs, online learning) → RMSProp, Adam

• Stable gradients (e.g., batch training, tabular data) → SGD with Momentum

🎯 General Recommendation

• If unsure, start with Adam. If it overfits or behaves poorly, switch to SGD with Momentum or AdamW.

When Adam is a Good Choice

• Works well for most deep learning tasks (CNNs, RNNs, transformers, etc.).

• Converges faster than plain SGD, especially in noisy environments.

• Doesn’t require much hyperparameter tuning (default settings often work).

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❌ When Adam Might Not Be the Best Choice

Problem Scenario	Why Adam Might Not Work	Better Alternative
Overfitting happens	Adam adapts too aggressively, leading to poor generalization	AdamW (Adam with weight decay)
You need best generalization (e.g., Image Classification)	Adam can converge too quickly to a suboptimal solution	SGD with Momentum
Training very large datasets (e.g., billion+ samples)	Adam uses more memory due to per-parameter adaptive learning rates	SGD with Momentum
Training on Reinforcement Learning (RL) tasks	Adam struggles with high variance gradients	RMSProp
Training deep networks (e.g., Transformers, LSTMs)	Adam works but may lead to instability	AdamW (better stability)

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🚀 Practical Rule of Thumb

• Start with Adam if you’re unsure.

• If you see overfitting → Try AdamW or SGD with Momentum.

• If you need better generalization → Use SGD with Momentum.

• If you work with reinforcement learning → Use RMSProp.