sparse-ir v1.0: optimal basis meets stable code

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

sparse-ir v0.3 - Now with documentation!

This release adds more documentation.

sparse-ir v0.2 - new name, same IR sparseness!

We have decided to rename the irbasis3 to sparse-ir for consistency with the Julia package.