ACFlow
A full-fledged analytic continuation toolkit in Julia.
Thank you for using ACFlow. This documentation will help you to be familiar with and explore the ACFlow toolkit. It is just compatible with ACFlow v2.1.1-devel.241001.
Analytic continuation is an art of optimization. Please check your simulated results carefully.
The ACFlow toolkit is in heavy development. Please use it at your own risk. If you encounter any bugs or troubles, or require new features, please consult me directly: huangli at caep.cn
Introduction
Manual
- Main Features
- Implementations
- Installation
- Running Modes
- Input Files
- Output Files
- Parameters
- Tricks and tips
Examples
- Matsubara Self-Energy Function
- Matsubara Green's Function
- Imaginary Time Green's Function
- Current-Current Correlation Function
Theory
- Basic Principles
- Maximum Entropy Method
- Barycentric Rational Function Approximation
- Nevanlinna Analytical Continuation
- Stochastic Analytic Continuation 1
- Stochastic Analytic Continuation 2
- Stochastic Optimization Method
- Stochastic Pole Expansion
- References