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📉 Convergence Analysis in Hyperparameter Optimization

Watch an optimizer descend a 3D non-convex loss landscape and see how learning rate, momentum, optimizer type, and gradient noise change its convergence rate and whether it satisfies a stopping criterion.

AI & Machine Learning3DAdvanced60 FPS

An optimizer marker descends a synthetic non-convex 3D loss landscape, tracing its trajectory toward a minimum while a live log-scale chart tracks whether — and how fast — it converges under a chosen stopping criterion.

🔬 What It Demonstrates

Learning rate, momentum, optimizer choice, and gradient noise all change the shape and speed of the convergence curve — and whether the search gets stuck in a shallow local minimum or reaches the global basin.

🎮 How to Use

Adjust learning rate, momentum, gradient noise, the stopping threshold ε, and the optimizer type, then watch the trail, the gradient-norm readout, and the loss chart respond. Click "New start point" to test robustness from a different location.

💡 Did You Know?

In practice, hyperparameter search rarely has access to true gradients at all — methods like Bayesian optimization and grid/random search instead estimate a response surface from a handful of expensive trials.

⚙ Under the hood

machine learningoptimizationhyperparametersloss landscapeconvergencealgorithmsThree.js

3D · Three.js / WebGL renderer · 60 FPS target · runs fully client-side, no install