The fg list | The parameters to optimize | Basic options | Defaults | Logging progress to console | Storing progress | Optimization Methods | Steepest Descent ("SD") | Broyden-Fletcher-Goldfarb-Shanno ("BFGS") | Limited-Memory BFGS ("L-BFGS") | Conjugate Gradient ("CG") | Nesterov Accelerated Gradient ("NAG") | Classical Momentum ("MOM") | Simplified Nesterov Momentum | Line Searches | Wolfe Line Search | Initial Step Size Guess | Initial Step Size Guess on First Iteration | Other Line Searches | Maximum function or gradient evaluations per line search | Adaptive Learning Rate Methods | Adaptive Restart

Metric Multi-Dimensional Scaling | The Metric MDS Cost Function | The Metric MDS Gradient | mize function and gradient | Improving the Efficiency of the Function and Gradient Routines

Brute Force | Random Projection Forests | Graph Search | n_threads | epsilon | init | Neighbor Graph Input | Neighbor Indices Only | Forest initialization | Preparing the Search Graph | Diversification Probability | Degree Pruning | References

Specialized Binary Metrics | References

Basic UMAP | Parameters | min_dist | n_neighbors | init | dens_scale | Embedding New Data | Supported Distances | Multi-threading support | Python Comparison | Limitations and Other Issues | Nearest Neighbor Calculation | Spectral Initialization | Supporting Libraries

Comparing Low- and High-Dimensional Nearest Neighbors | Detecting Hubness | k-occurrence in the 2D case | k-occurrence in the 1000D case | k-occurrence as a diagnostic of NND failure | Detecting Poorly Predicted Neighbors | Detecting Problems Early | Improving accuracy | Weight candidates by degree | Use More Neighbors | Use More Candidates | Decrease the convergence tolerance | Merging Multiple Independent Results | Using a Search Graph | Conclusions | References

Building a Space-Partitioning Tree | Building a Random Partition Tree | From Trees to Forests | Build a Forest | Finding Nearest Neighbors | A Small Optimization for the k-Nearest Neighbors | Margin | Filtering a Forest | References

Find the k-nearest neighbors | The Neighbor Graph Format | Build an Index | Querying Data | Parallelism | Available Metrics | Supported Data Types | Parameters | References

Local Join | Other Heuristics | PyNNDescent Modifications | Example | Troubleshooting | Querying New Data | References

Creating an Optimizer | Initialize the Optimizer | A potential simplification | Start optimizing | Return value of mize_step | Step information | Convergence

Iteration tolerance | Function tolerance | Gradient tolerance | Step tolerance | Function and gradient count tolerance | A minor complication with convergence checking