Stability of eigenfunctions
From Polymath1Wiki
In [CZ1994] some bounds for the Neumann heat kernels Pt(x,y) on a general domain are given; in particular, one can bound this kernel by a multiple of the Euclidean heat kernel for t small enough. Using this bound, [BP2008] showed uniform stability of the second eigenfunction (in the uniform topology) and eigenvalue with respect to uniform perturbations of the domain.
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Formal theory
Suppose one has a one-parameter family
of self-adjoint operators (on some Hilbert space, e.g. L2(Ω), though for this formal computation the domain will not be important), and one-parameter families
,
to the eigenfunction equation
- L(t)u(t) = λ(t)u(t). (1)
We normalise u(t) to have norm 1:
. (2)
Formally, if we differentiate the norm equation (2) at time zero, we get
(3)
while if we differentiate (1) at time zero we obtain
. (4)
Taking the inner product of (4) with u(0) and using (2), (3) we conclude the Hadamard first variation formula for eigenvalues:
. (5)
If we instead take the orthogonal projection
onto the orthogonal complement of u(0), we obtain the Hadamard first variation formula for eigenfunctions:
. (6)
Formally, if λ is a simple eigenvalue, then L(0) − λ is invertible on the orthogonal complement of u(0), and so (6) and (3) allow one to solve for
.
L^2 and H^1 theory
Suppose that one has a domain Ω with second Neumann eigenvalue λ2, and third Neumann eigenvalue (not counting multiplicity) λ3 > λ2. Thus, one has
(7)
for all mean zero u, with equality when u lies in the second eigenspace V2, and one can improve this to
(8)
when u is orthogonal to V2.
Now consider a perturbation BΩ of Ω, where B is an invertible linear transformation. Then a second eigenfunction of BΩ, after change of variables, becomes a function u on Ω that minimizes the modified Rayleigh quotient
where M: = (B − 1)(B − 1)T. We may normalize this eigenfunction as u = u2 + v, where u_2 is a unit eigenfunction in V_2 and v is orthogonal to V_2, so that
. Then the modified Rayleigh quotient of u is less than or equal to that of u_2, and hence
Expanding out u as u_2+v and rearranging, we end up at
Note that
is orthogonal to
by integration by parts, and so we may replace
on the RHS by the orthogonal projection
. Letting σ1(M) = σ2(B) − 2 be the least singular value of M, we now apply Cauchy-Schwarz and conclude that
.
By (8) we may bound
, and so we conclude that
. (9)
This gives an H^1 bound on the error v between the perturbed eigenfunction u and the original eigenfunction u_2. To understand this bound, note that we may upper bound
and also
so that
and so one has
provided that the denominator is positive. In terms of the condition number κ: = σ2(B) / σ1(B) of B, this becomes
(15)
which is a non-trivial bound when κ < (λ3 / λ2)1 / 2, and is of the order of
when κ is close to 1. Using (8), we conclude in particular that
(16)
For these calculations performed on a third reference triangle, http://www.math.sfu.ca/~nigam/polymath-figures/Perturbation.pdf
An alternate approach to the L^2 and H^1 theory
Let us keep the reference triangle Ω fixed, and view the matrix M=M(t) as being smoothly time dependent, so that the eigenvalues λk = λk(t) and L^2-normalised eigenfunctions uk = uk(t) are also time dependent. Assume for the sake of argument that eigenvalues stay simple and all functions depend smoothly on t. We have the eigenfunction equation
and the Neumann boundary condition
and the L^2 normalisation
.
Differentiating, we conclude that
(10)
and
(11)
and
. (12)
Taking the inner product of (10) with u_k and using (12) yields
.
Integrating by parts using the Neumann condition and (11) yields
.
By the eigenfunction equation and (12), the first inner product on the RHS vanishes. By Stokes theorem one has
and thus we have the variation formula
. (14)
Next, if we take the inner product of (10) with u_l for some l distinct from k, one has
.
Integrating by parts as before, we have
.
By the eigenfunction equation, the first inner product on the RHS is
. By Stokes theorem we have
and thus
and thus by eigenfunction expansion (and (12))
where the convergence is in an unconditional L^2 sense. (Note that
is an orthonormal system and so from Bessel's inequality we know that
, which is enough decay to justify the L^2 convergence of the RHS.) In fact we may differentiate and conclude that
where the convergence is again in the unconditional L^2 sense (i.e. the previous convergence was in the unconditional H^1 sense). From the Bessel inequality we see in particular that
;
in particular, we have
and thus
which can be viewed as infinitesimal variants of the estimates in the previous section.
A Sobolev inequality
- Lemma Suppose that
obeys the inhomogeneous eigenfunction equation − Δu = λu + F. Suppose also that
is a smooth function that equals 1 at 0 and vanishes on
. Then
-
-
Proof We introduce the averaged functions
and observe that
obeys the inhomogeneous Bessel equation
with initial condition
.
We can solve this equation by separation of variables, writing
.
Using the standard Wronskian identity
one soon arrives at the equations
and
.
Meanwhile, by integration by parts
and thus
From Cauchy-Schwarz one has
and
and similarly
The claim then follows from further applications of the Cauchy-Schwarz inequality. []
In principle, the above inequality, when combined with the previous L^2 and H^1 estimates, gives L^infty control on
in the interior of the triangle. One should also be able to get control near the interior of an edge by reflection. One needs to modify the argument a bit though to handle what is going on at vertices.
Here is an alternate approach that avoids the use of Bessel functions of the first and second kinds. Suppose one has a solution to the inhomogeneous eigenfunction equation − Δu = λu + F on a ball B(0,R). Let η be a smooth (or at least C^2) cutoff on this ball which equals 1 at the origin. Then uη is compactly supported with Laplacian
and hence by the fundamental solution to the Laplacian
and hence by Cauchy-Schwarz
Because Δ(ηu) has mean zero, one can also replace log | x | here by log | x | − C for any constant C, for instance one can use log( | x | / R).
Sobolev inequalities in logarithmic coordinates
The computations appear to become cleaner if we work in logarithmic coordinates
- (s,θ): = (logr,θ).
Thus, for instance, a sector
in polar coordinates, becomes a half-infinite strip
in logarithmic coordinates. A triangle Ω which contains this sector as one of its corners, then becomes a slightly enlarged version logΩ of this half-infinite strip, with a concave boundary connecting the two half-infinite edges.
Consider an eigenfunction − Δu = λu in the original domain Ω. In polar coordinates, the eigenfunction equation is
the Rayleigh quotient is
and the Neumann boundary condition on the two radial edges are
.
If we convert to logarithmic coordinates
by the change of variables
- v(s,θ): = u(es,θ)
then one can compute that the eigenfunction equation now becomes
(A.1)
the Rayleigh quotient becomes
and the Neumann boundary conditions on the two half-infinite edges are
. (A.2)
We can reflect logΩ infinitely often across the half-infinite edges, extending v to a half-infinite domain that contains the half-space
. Thanks to the Neumann condition (A.2), v will remain smooth and obey the eigenfunction equation (A.1) in the entirety of this half-space.
Let's suppose that we have normalised our original eigenfunction u to have an L^2 norm of 1, then after transforming into logarithmic coordinates we have
(A.3)
and hence by the Rayleigh quotient we have H^1 control:
. (A.4)
Now we can start moving towards L^infty control of v. We first look at the mean
.
From the eigenfunction equation and the Neumann boundary condition, the mean obeys the equation
. (A.5)
On the other hand, the L^2 normalisation of v and Cauchy-Schwarz gives
(A.6)
and the H^1 bounds similarly gives
. (A.7)
Among other things, (A.7) tells us that
must go to zero along some subsequence as
. (Indeed, a Bessel expansion of the original eigenfunction shows that it goes to zero uniformly, but we won't need this fact here.) Integrating (A.5) and using (A.6) and Cauchy-Schwarz then gives us the bound
(A.8)
and hence on a second integration
. (A.9)
From the triangle inequality, we conclude that
(A.10)
and hence by (A.6)
(A.11)
(which already bounds the original eigenfunction at the vertex) and hence by (A.9)
. (A.12)
This gives L^infty bounds on the mean
. Now we move on to bounding v itself. We have the Sobolev inequality
(A.13)
so it will suffice to get pointwise bounds on the angular energy
(A.14)
since we then have
(A.15)
from the triangle inequality.
Note that (A.4) gives an integrated bound
. (A.16)
On the other hand, differentiating (A.1) in θ gives
and hence by integration by parts
for any test function η. Bounding
we conclude that
.
In particular, if η is a function just of s and is supported in
, we conclude that
. (A.17)
Combining this with (A.16) for various choices of cutoff η gives integral control on
, and this combined with (A.16) gives pointwise control on E(s) and hence on v(s). This gives L^infty control on the eigenfunction u near the vertex. (The computations are somewhat ugly, though, particularly if one wants to optimise in eta.)
H^2 theory
Formally, if on a triangle Ω, a function v obeys the inhomogeneous Laplace equation
- − Δv = F (B.1)
with homogeneous Neumann boundary data
, then after two integrations by parts we have the H^2 bound
(Bochner-Weitzenbock identity.) The integrations by parts can be justified if v is smooth on Omega, with first derivative uniformly bounded and second derivative blowing up by at most
at each vertex for some
, by the usual trick of inserting a cutoff function to smoothly cut out the integral at the vertices, performing the integration by parts, and then sending the cutoff parameter to zero. I think this sort of regularity for acute-angled triangles can be achieved in practice without difficulty.
Now suppose that one is solving the inhomogeneous Laplace equation (B.1) but with inhomogeneous Neumann data
. (B.2)
Then the integration by parts becomes much less favorable, as there are now a number of nasty boundary terms to take care of. But suppose that one can find a reasonably smooth function w (of the same regularity needed for v, i.e. first derivative uniformly bounded and second derivative blowing up slower than 1/r) which solves (B.2) but not (B.1), i.e.
on the boundary. Then the difference v-w solves (B.1) with forcing term F + Δw and thus
From the pointwise estimate
and the triangle inequality we conclude that
(B.3)
We can apply this to the first variation
of the second eigenfunction at M=I. From the L^2 and H^1 stability theory one has
with boundary condition
(B.4)
and thus we have the H^2 bound
(B.5)
where w is any function with boundary value (B.4). From the L^2 stability theory one has
and so one has bounds on everything on the RHS of (B.5) except for the last term.
To control this term, we divide the triangle Ω = ABC into three overlapping regions ΩA,ΩB,ΩC, each one being a region around one of the three vertices A,B,C that avoids the other two vertices, as well as the edge joining those vertices. In each of these regions, we find a function w that solves the boundary condition (B.5) in that region, which obeys good H^2 bounds; combining these functions together by a partition of unity will then give a global w in H2(Ω) that obeys (B.5) on the entire boundary.
Let's look at the region ΩA near the vertex A, which we set to be the origin. In polar coordinates (r,θ), we know that
on the edges AB, AC, and so the boundary condition (B.4) becomes
on AB and
on AC for some explicit scalar constants cAB,cAC depending only on geometry of the triangle ABC and on
. On ΩA, we can build a function w_A that exhibits this data by the formula
- wA(x): = u(Φ(x))
where
is a smooth function that is the identity on the edges AB, BC inside ΩA, and whose Hessian maps the outward normal n to a vector with radial component cAB on AB and cAC on AC. It is not difficult to see that one can choose such a function on ΩA so that
and
(one can choose a function of the form
for a suitable function f). The function w_A defined as above will solve the Neumann conditions, and from the chain rule one has
and so
A similar estimate (with some additional lower order terms) obtains when one glues together the three partial solutions w_A, w_B, w_C to create a solution w to the boundary problem with
To finish the task of estimating
in H^2, one therefore needs bounds on
(and similarly for the other two vertices of the triangle). I think such bounds can be obtained as follows. Let's work in ΩA with some suitable smooth cutoff and ignore terms coming from the derivatives of the cutoff (which don't involve the singularity 1/|x| and so are easy to estimate). If we subtract off
then u now has mean zero in angular directions. From integration by parts we have
But if A has angle α, then the Poincare inequality in the angular direction gives
and so as α < π, we can bound
as required.
An alternate approach to the H^2 theory
Yet another approach to the H^2 theory, which seems to give better results, comes from using Schwarz-Christoffel transformations, with the upper half-plane H being the reference domain.
More precisely, given three angles α + β + γ = π for a triangle, we can find a Schwarz-Christoffel transformation
from the half-plane H = {z:Im(z) > 0} to a triangle Ωα,β with angles α,β,γ by solving the differential equation
with initial condition Φα,β(0) = 0, keeping Φα,β conformal. In particular, Φα,β(z) behaves like
near the origin,
near 1, and
at infinity, for some complex numbers A,B,C with A=0. The image Ωα,β is a triangle ABC with angles α,β,γ.
Write w = Φα,β(z), then
The Lebesgue measure dm_w on the triangle is related to the Lebesgue measure dm_z on the half-plane by the change of variables formula
We write the expression
as e2ω, where
The Rayleigh quotient, pulled back to the reference half-plane H, is then
and so the second eigenfunction u_2, in the reference half-plane will obey the equation
- − Δu2 = λ2e2ωu2 (C.1)
with Neumann boundary condition
(C.2)
on the boundary of the half-plane and the mean zero condition
. (C.3)
We can normalise u_2 in L^2 norm,
(C.4)
which by the Rayleigh quotient immediately gives an H^1 bound
(C.5)
Now suppose that we vary α,β smoothly with respect to a time parameter; this means that ω will also vary by the formula
(C.6)
The eigenvalue
can also vary at a rate which can be controlled by the previous perturbation theory (e.g. equation (14)). As for the variation of the eigenfunction
, we can compute this by differentiating (C.1) to obtain
. (C.7)
Also, on differentating (C.2) we see that
conveniently satisfies a homogeneous Neumann condition:
(C.8)
Using the orthonormal basis ukeω of L2(H,dmz) as in the previous notes on L^2 and H^1 stability, we can obtain an explicit formula for
. Indeed, integrating (C.7) against another eigenfunction u_l and integrating by parts using (C.1), (C.8) gives
and thus
and hence by the eigenfunction expansion
which among other things gives the L^2 identity
and hence by the Bessel inequality
(C.9)
where X is the quantity
. (C.10)
The quantity cannot quite be bounded directly by (C.4) because of the logarithmic divergence in the weight
, but it should be controllable by also employing (C.5) and (C.3), perhaps after transferring back to the triangle ABC.
The quantity X also controls
. Indeed, on integrating (C.7) against u_2 and using (C.1), (C.2), (C.4) we obtain that
and hence by Cauchy-Schwarz and (C.4)
(C.11)
If we now multiply (C.7) by e − ω and then take L^2 norms using the triangle inequality and the previous estimates (C.9), (C.10), (C.11) we conclude that
which simplifies to
We can transfer this back to the triangle Ωα,β = ABC by the change of variables
giving
which after two integration by parts (Bochner-Weitzenbock inequality) gives an H^2 bound on v:
By Sobolev embedding, (noting also that we have L^2 and H^1 control on v) we can thus obtain L^infty control on v (or equivalently, u) in terms of the quantity X. So the main task is to understand how to bound X, which in the ABC coordinates is basically the L^2 norm of v weighted by a reasonably explicit logarithmic weight.
This is done more explicitly at this set of notes.
The Bessel function Jν(x) for
has the Weierstrass factorisation
where jν,i are the positive zeroes of Jν; see e.g. this link.
In particular, we have
It is known that jν,i are non-decreasing in ν (page 508 of WATSON, G.N., A Treatise on the Theory of Bessel Functions, 2nd ed., Cambridge University Press, 1944; one can also use here the Sturm comparison theorem). Thus if
and
, then
is non-increasing in ν, and thus
. (1)
Now, consider a solution to the eigenfunction equation − Δu = λu in the sector
for some acute 0 < α < π / 2 and some radius R > 0, obeying the Neumann conditions
for θ = 0,α. For technical reasons we will also need the upper bound
(2)
which seems to hold in practice.
By separation of variables, we can write u in polar coordinates as
for some coefficients ck. From (2) and the triangle inequality we thus have the bound
for any
. To bound the coefficients, we assume an L^2 normalisation
The left-hand side may be expanded as
and thus
where A is the quantity
(3)
Thus by the Cauchy-Schwarz inequality, we have
(4)
where B(r_0) is the quantity
We may upper bound B(r_0) as follows. We may reduce the range of integration from [0,R] to [r1,R] for some intermediate radius
.
By (1) and (2), we may replace
by
. Thus
One may then sum the geometric series and conclude that
(5)
for any
, where
One can use (5) to exclude extrema for small values of r_0. For instance, (5) shows that | u(r0,θ) | cannot exceed | u(0) | unless
The left hand side behaves like r_0^2, and the right-hand side like
, so for acute α this inequality becomes impossible for small enough r0 (and "small enough" can be made numerically explicit if one is willing to compute with Bessel functions). Things become bad when α is close to a right angle, but it seems in those cases that the extremum is obtained elsewhere.
