NonArchimedeanMachineLearning.jl

Non-Archimedean optimization in Julia.

NonArchimedeanMachineLearning.jl provides data structures and algorithms for optimization 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.

The sidebar links to the API reference for each category.