NonArchimedeanMachineLearning.jl

Non-Archimedean Machine Learning in Julia.

Welcome to the documentation for NonArchimedeanMachineLearning.jl. This library provides data structures and algorithms for machine learning over p-adic numbers and polydisc spaces.

Overview

The src codebase is structured into the following categories:

• Basic Structures: Core mathematical structures including p-adic valuation, polydiscs, and tangent vectors.
• Optimization: Machine learning models, loss functions, and optimization algorithms (Gradient/Greedy Descent and Tree Search methods).
• Visualization: Loss landscape sampling and search tree plotting.

Use the sidebar to explore the API reference for each category.