Sparse-ir

Latest version: v1.1.1

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1.0.5

[Diff since v1.0.4](https://github.com/SpM-lab/SparseIR.jl/compare/v1.0.4...v1.0.5)

1.0.4

[Diff since v1.0.3](https://github.com/SpM-lab/SparseIR.jl/compare/v1.0.3...v1.0.4)

1.0.3

[Diff since v1.0.2](https://github.com/SpM-lab/SparseIR.jl/compare/v1.0.2...v1.0.3)

1.0.2

[Diff since v1.0.1](https://github.com/SpM-lab/SparseIR.jl/compare/v1.0.1...v1.0.2)

1.0.1

[Diff since v1.0.0](https://github.com/SpM-lab/SparseIR.jl/compare/v1.0.0...v1.0.1)

1.0

Today we are proud to release the first stable version of `sparse-ir`: a python library for optimal compression of many-body propagators on the imaginary (Euclidean) time axis as well as fast and stable diagrammatic computations.

Reasons to use IR basis functions and sparse sampling:

- The IR basis is a **provably optimal** basis for many-body propagators on the imaginary axis
- The IR basis comes with a **sparse, near-optimal set** of imaginary times and frequencies on which diagrammatic equations can be solved.
- The IR basis has an **intimate connection with the real-frequency** axis: it is a powerful preprocessor and preconditioner for analytic continuation.

Reasons to upgrade from the old `irbasis` library:

- sparse-ir computes bases for *arbitrary* cutoffs and kernels, usually within seconds
- sparse-ir provides battle-tested classes for sparse sampling, with fast and accurate fitting methods
- sparse-ir significantly improves upon the choice of sampling points, also allowing the use of symmetries
- sparse-ir packages objects for representing self-energies

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