First tutorial on SLEPc EPS

Signed-off-by: Umberto Zerbinati <[email protected]>

docs/src/SLEPcEPS/poisson.py.rst

@@ -0,0 +1,94 @@
+Solving the Laplace eigenvalue problem using SLEPc EPS
+=======================================================
+
+In this tutorial, we explore using `SLEPc EPS` to solve the Laplace eigenvalue problem in different formulations.
+We begin considering the Poisson eigenvalue problem in primal form, i.e.
+
+.. math::
+
+   \text{find } (u,\lambda) \in H^1_0(\Omega)\times\mathbb{R} \text{ s.t. } a(u,v) := \int_{\Omega} \nabla u\cdot \nabla v \; d\vec{x} = \lambda (u,v)_{L^2(\Omega)},\quad v\in H^1_0(\Omega).
+
+Such a discretisation can easily be constructed using NGSolve as follows: ::
+
+   from ngsolve import *
+   import netgen.gui
+   import netgen.meshing as ngm
+   import numpy as np
+   from mpi4py.MPI import COMM_WORLD
+
+   if COMM_WORLD.rank == 0:
+      mesh = Mesh(unit_square.GenerateMesh(maxh=0.1).Distribute(COMM_WORLD))
+   else:
+      mesh = Mesh(ngm.Mesh.Receive(COMM_WORLD))
+
+   order = 3
+   fes = H1(mesh, order=order, dirichlet="left|right|top|bottom")
+   print("Number of degrees of freedom: ", fes.ndof)
+   u,v = fes.TnT()
+   a = BilinearForm(grad(u)*grad(v)*dx, symmetric=True)
+   a.Assemble()
+   m = BilinearForm(-1*u*v*dx, symmetric=True)
+   m.Assemble()
+
+We then proceed to solve the eigenvalue problem using `SLEPc EPS` using ngsPETSc's `EigenSolver` class. 
+In particular, we will use SLEPc implementation of a locally optimal block preconditioned conjugate gradient.
+Notice that we have assembled the mass matrix so that it is symmetric negative definite, this is because ngsPETSc `Eigensolver` requires you to write all eigenvalue problems as a polynomial eigenvalue problem, i.e.
+
+.. math::
+   A\vec{U} - \lambda M\vec{U} = 0
+
+where :math:`A` and :math:`M` are the stiffness and mass matrices, respectively. ::
+
+   from ngsPETSc import EigenSolver
+   solver = EigenSolver((m, a), fes, 10, solverParameters={"eps_type":"lobpcg", "st_type":"precond"})
+   solver.solve()
+   for i in range(10):
+      print("Normalised (by pi^2) Eigenvalue ", i, " = ", solver.eigenValue(i)/(np.pi*np.pi))
+   modes, _ = solver.eigenFunctions(list(range(10)))
+   Draw(modes)
+
+Notice that we can access a single eigenvalue using `solver.eigenValue(i)` and the corresponding eigenfunction using `solver.eigenFunction(i)`.
+If we use `solver.eigenFunctions(indices)` we can access the first 10 eigenfunctions as a multi-dimensional array and we can obtain the corresponding eigenvalues using `solver.eigenValues(indices)`.
+We now consider a mixed formulation of the Laplace eigenvalue problem, i.e.
+
+.. math::
+
+   \text{find } (\vec{\sigma}, u, \lambda) \in H(\text{div},\Omega)\times L^2(\Omega)\times \mathbb{R} \text{ s.t. } \\
+   \begin{cases}
+      \int_{\Omega} \vec{\sigma}\cdot\vec{\tau} \; d\vec{x} - \int_{\Omega} u \nabla \cdot \vec{\tau} \; d\vec{x} = 0 & \forall \vec{\tau}\in H(\text{div},\Omega)\\
+      \int_{\Omega} \nabla\cdot\vec{\sigma}v \; d\vec{x} = \lambda \int_{\Omega} uv \; d\vec{x} & \forall v\in L^2(\Omega)
+   \end{cases}
+
+We can discretise this problem using NGSolve as follows: ::
+
+   V = HDiv(mesh, order=order)
+   Q = L2(mesh, order=order-1)
+   W = FESpace([V,Q])
+   print("Number of degrees of freedom: ", W.ndof)
+   sigma, u = W.TrialFunction()
+   tau, v = W.TestFunction()
+   a = BilinearForm(sigma*tau*dx + div(tau)*u*dx + div(sigma)*v*dx)
+   a.Assemble()
+
+   m = BilinearForm(1*u*v*dx)
+   m.Assemble()
+
+We can then solve the eigenvalue problem using `SLEPc EPS` using ngsPETSc's `EigenSolver` class.
+Since the mass matrix now has a large kernel, hence is no longer symmetric positive definite, we can not use LOBPCG as a solver.
+Instead, we will use a Krylov-Schur solver with a shift-and-invert spectral transformation to target the smallest eigenvalues.
+Notice that because we are using a shift-and-invert spectral transformation we only need to invert the stiffness matrix which has a trivial kernel because we are using an inf-sup discretisation.
+If we tried to use an simple shift transformation to target the largest eigenvalues we would have run into the error caused by trying to invert a singular matrix.::
+   
+   solver = EigenSolver((m, a), W, 10,
+                        solverParameters={"eps_type":"krylovschur", 
+                                          "st_type":"sinvert",
+                                          "eps_target": 0,
+                                          "st_pc_type": "lu",
+                                          "st_pc_factor_mat_solver_type": "mumps"})
+   solver.solve()
+   for i in range(10):
+      print("Normalised (by pi^2) Eigenvalue ", i, " = ", solver.eigenValue(i)/(np.pi*np.pi))
+   mode, _ = solver.eigenFunction(0)
+   modeSigma, modeU = mode.components
+   Draw(modeSigma, mesh, "sigma")
+   Draw(modeU, mesh, "u")

docs/src/index.rst

@@ -18,6 +18,11 @@ Welcome to ngsPETSc's documentation!
 
    PETScPC/poisson.py
 
+.. toctree::
+   :maxdepth: 1
+   :caption: SLEPc EPS
+
+   SLEPcEPS/poisson.py