EasterEig

Consider a parametric eigenvalue problem depending on one scalar $\nu$ or given vector $\boldsymbol\nu =(\nu_1,\nu_2,\ldots,\nu_N) \in \mathbb{C}^N$ of paramaters. This arises for instance in

• waveguides, where the wavenumber (eigenvalue) depends on the frequency (parameter)
• waveguides with absorbing materials on the wall, where modal attenuation (eigenvalue imaginary part) depends on the liner properties like impedance, admittance, density (parameter)
• structural dynamics with a randomly varying parameter, where the resonances frequencies (eigenvalue) depend on for instance of material parameters like Young modulus or density
• ...

The aim of this package is to reconstruct the eigenvalue loci and to locate exceptional points (EPs). The EPs in non-Hermitian systems correspond to particular values of the parameters leading to defective eigenvalue. At EPs, both eigenvalues and eigenvectors are merging.

The theoretical parts of this work are described in [1] for the location of exceptional points and in [2] for eigenvalues reconstruction. The extension to several parameters is presented in [3].

The method requires the computation of successive derivatives of some selected eigenvalues with respect to the parameter so that, after recombination, regular functions can be constructed. This algebraic manipulation overcomes the convergence limits of conventional methods due to the singularity branch point. This enables

• Fast approximation of eigenvalues, converging over a large region of parametric space
• High order EP localization
• Computation of the associated Puiseux series up to an arbitrary order
• Numerical representation of the problem discrimiant and of the partial characteristic polynomial

To use this package :

1. An access to the operator derivative with respect to $\boldsymbol\nu$ is required
2. The parametric eigenvalue problem must have the form $$\mathbf{L} (\lambda(\boldsymbol\nu), \boldsymbol\nu) \mathbf{x} (\boldsymbol\nu) =\mathbf{0},$$ where, for a given vector $\boldsymbol\nu$ which contains $N$ independent complex-valued parameters, $\lambda(\boldsymbol\nu)$ is an eigenvalue and $\mathbf{x}(\boldsymbol\nu)\neq \mathbf{0}$ is the associated right eigenvector. Here the matrix $\mathbf{L}$ admits the decomposition $$\mathbf{L} (\lambda, \boldsymbol\nu) =\sum_{i \geq 0} f_i(\lambda) \mathbf{K}_i(\boldsymbol\nu)$$ where $f_i$ is a polynomial function and matrices $\mathbf{K}_i$ are supposed to be an analytic function of the parameters vector $\boldsymbol\nu$.

The matrices of discrete operators can be either of numpy type for full, scipy type for sparse or petsc mpiaij type for sparse parallel matrices.

If eastereig is useful for your research, please cite the following references. If you have some questions, suggestions or find some bugs, report them as issues here.

References

[1] B. Nennig and E. Perrey-Debain. A high order continuation method to locate exceptional points and to compute Puiseux series with applications to acoustic waveguides. Journal of Computational Physics, 109425, (2020). [doi]; [open access]

[2] M. Ghienne and B. Nennig. Beyond the limitations of perturbation methods for real random eigenvalue problems using Exceptional Points and analytic continuation. Journal of Sound and vibration, (2020). [doi]; [open access]

[3] B. Nennig, Martin Ghienne, E. Perrey-Debain. Fast recovery of parametric eigenvalues depending on several parameters and location of high order exceptional points. Journal of Computational Physics, 551, pp.114692, (2026). [doi];[open access]

Install

eastereig is based on numpy (full) and scipy (sparse) for most internal computation and can handle large parallel sparse matrices thanks to optional import of petsc4py (>=3.20) (and mumps), slepc4py and mpi4py. As non-Hermitian problems involve complex-valued eigenvalues, computations are realized with complex arithmetic and the complex petsc version is expected. The sympy package is used for formal manipulation of multivariate polynomials. Riemann surface can also be plotted using the Loci class either with matplotlib or with pyvista and pyvistaqt (optional).

Before installing eastereig, you'll need python (tested for v >= 3.8), pip, and to manually install the optional dependencies you want:

• The python packages pyvista and pyvistaqt (optional Riemann surfaces plotting)
• The python package petsc4py and slepc4py (optional sparse parallel matrices surpport)
• A fortran compiler (only for building from the sources, tested with gfortran on linux and macos, with m2w64-toolchain on windows with conda or with rtools distribution of mingw64)
• The python package scikit-umfpack to improve scipy.sparse LU factorization performance.

Other dependencies will be installed automatically.

From the wheel

The easiest way to install eastereig is to use the wheel available on pypi. Wheels, with compiled extension, are available for the most usual 64 bits architectures and os (Linux, Windows, Macos). You can install eastereig from pip:

pip install eastereig [--user]

If wheels are not available, the pure python version will be installed. You can also use a virtual environnement for better isolation.

From the source

If you need to modify the code or the last development version, you need to build eastereig from the source. The sources are available on pypi or on the github repos.

If the variable use_fpoly=false, the fortran extension is skipped. If use_fpoly=true, the fortran extension is enabled. The evaluations of the PCP will be faster but a fortran compiler is required.

Once you manualy get the sources, in the eastereig source folder (same folder as meson.build file), run

pip install -v . -Csetup-args=-Duse_fpoly=true

or,

pip install -v --no-build-isolation --editable . -Csetup-args=-Duse_fpoly=true

to install it in editable mode. If -Csetup-args=-Duse_fpoly=true is omitted, the default behavior, defined is meson.options applied.

If needed, please see the steps given in the continuous integration scripts ci-ubuntu.

Running tests

Tests are handled with doctest and with unittest. To execute the full test suite, run :

python -m eastereig

Basic workflow and class hierarchy

eastereig provides several top level classes:

1. OP class, defines operators of your problem
2. Eig class, handles eigenvalues, their derivatives and reconstruction
3. CharPol class, combines several eigenvalues and their derivatives to reconstruction a part of the characteristic polynomial
4. EP class, combines Eig object to locate EP and compute Puiseux series
5. Loci class, stores numerical value of eigenvalues loci and allows easy Riemann surface plotting

Getting started

Several working examples are available in ./examples/ folder

1. Acoustic waveguide with an impedance boundary condition (with the different supported linear libraries)
2. 3-dof toy model of a structure with one random parameter (with numpy)
3. 3-dof toy with two parameters, based on CharPol class and leading to EP3
4. ...

Remarks : To run an example with petsc (parallel), you need to run python with mpirun (or mpiexec). For instance, to run a program with 2 proc mpirun -n 2 python myprog.py

To get started, the first step is to define your problem. Basically it means to link the discrete operators (matrices) and their derivatives to the eastereig OP class. The problem has to be recast in the following form:

\left[\underbrace{1}_{f_0(\lambda)=1} \mathbf{K}_0(\nu) + \underbrace{\lambda(\nu)}_{f_1(\lambda)=\lambda} \mathbf{K}_1(\nu) + \underbrace{\lambda(\nu)^2}_{f_2(\lambda)=\lambda^2} \mathbf{K}_2(\nu) \right] \mathbf{x} =  \mathbf{0}.

Matrices are then stacked in the variable K

K = [K0, K1, K2].

The functions that return the derivatives with respect to $$\nu$$ of each matrices have to be put in dK. The prototype of this function is fixed (the parameter n corresponds to the derivative order) to ensure automatic computation of the operator derivatives.

dK = [dK0, dK1, dK2].

Finally, the functions that returns derivatives with respect to $\lambda$ are stored in $f_i(\lambda)$

flda = [None, ee.lda_func.Lda, ee.lda_func.Lda2].

Basic linear and quadratic dependency are defined in the module lda_func. Others dependencies can be easily implemented; provided that the appropriate eigenvalue solver is also implemented). The None keyword is used when there is no dependency to the eigenvalue, e. g. $\mathbf{