petsc4pySLEPc

petsc4pySLEPc is a Python wrapper for SLEPc, which in turn is a scalable eigenvalue problem library built on top of PETSc. SLEPc provides a set of data structures and routines for the solution of large-scale eigenvalue problems, including standard, generalized, and polynomial eigenvalue problems. petsc4pySLEPc allows Python users to leverage these capabilities directly within their Python workflows.

The library bridges the gap between the high-performance C/Fortran code of SLEPc and the ease of use and rapid prototyping offered by Python. It enables users to define eigenvalue problems, select appropriate SLEPc solvers, and extract the computed eigenvalues and eigenvectors. This integration is particularly useful for scientific computing, quantum mechanics, structural analysis, and other fields where eigenvalue problems are common.

Key features include support for various SLEPc solvers such as Krylov subspace methods (like Arnoldi and Lanczos) and direct solvers. It also allows for parallel computation, leveraging PETSc's distributed memory parallelism. Users can manage problem sizes, specify convergence criteria, and control solver parameters through the familiar Python syntax. The use of petsc4py ensures that data can be easily exchanged between PETSc, SLEPc, and other Python libraries like NumPy.