Crate dendritic 

Dendritic

Dendritic is a lightweight and extensible optimization library built with flexibility in mind. Contains utilities for first order optimization with multi variate/vector valued functions using ndarray. This crate aims to contain extensible interfaces for common abstractions in optimization & machine learning problems.

🚀 Features

• 📐 Auto-Differentiation: Reverse-mode autodiff for computing gradients using ndarray.
• ⚙️ Optimizers: Built-in optimizers like SGD, Adam, etc.
• 📈 Regression Models: Traditional regression models (Linear, Logistic).
• 🔣 Preprocessing: Lightweight utilities for common preprocessing tasks (e.g., one-hot encoding).
• 🧱 Modular: Designed to be flexible and easy to extend for research or custom pipelines.

🔮 Future Enhancements

There are more features on the roadmap for this crate. Version v2 was a redesign focused on improving crate structure and aligning functionality with abstractions common in optimization theory.

Below are some ideas for future features that may be incorporated into the crate:

• Second Order Optimization
  Methods like Newton’s or Secant methods that leverage second-order derivatives (Hessian) for faster convergence.

• Population Methods
  Optimization techniques that maintain and evolve a population of candidate solutions. Includes genetic algorithms and evolutionary strategies.

• Zero Order Methods
  Approaches that do not require gradient information, useful in settings where derivatives are unavailable or expensive to compute.

Modules

autodiff

Autodifferentiation Abstractions

optimizer

Optimizer & model abstractions with defaults

preprocessing

Data pre-processing module