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🌱 Getting Started

  • Philosophy
  • Quick Start

🧰 Basic Functionality

  • Walkthrough of Linear Algebra Functionality
  • Implementing new Linear Operators and Dispatch Rules
  • Defining New Dispatch Rules
  • Accessing Lower Level Algorithms (CG, Lanczos, Arnoldi, etc)
  • GPU Support and Changing Operator Device

💡 Example Applications

  • Gaussian Processes from scratch
  • 2nd order optimization of neural nets using Gauss Newton
  • Computing the eigenspectrum of the Hessian of a Neural Network
  • Boundary Value PDEs
  • Diagonalizing a Hamiltonian (PDE eigenvalue problems)
  • Spectral Clustering

🧙‍♂️ Advanced Features

  • jit, vmap, grad, and pytrees

📚 API Reference

  • Linear Algebra
  • Linear Operators
  • Linear Operator Base Class
  • Decompositions
  • Functions
  • Annotations for Linear Operators

🚧 Tricky Bits 🚧

  • Sharp Bits

👩‍💻 Developer Documentation

  • Contributing to CoLA
CoLA
  • Overview: module code

All modules for which code is available

  • cola.annotations
  • cola.fns
  • cola.linalg.algorithm_base
  • cola.linalg.decompositions.decompositions
  • cola.linalg.eig.eigs
  • cola.linalg.eig.power_iteration
  • cola.linalg.inverse.cg
  • cola.linalg.inverse.gmres
  • cola.linalg.inverse.inv
  • cola.linalg.logdet.logdet
  • cola.linalg.trace.diag_trace
  • cola.linalg.trace.diagonal_estimation
  • cola.linalg.unary.unary
  • cola.ops.operator_base
  • cola.ops.operators

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