Dissipative operator

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In mathematics, a dissipative operator is a linear operator A defined on a linear subspace D(A) of Banach space X, taking values in X such that for all λ > 0 and all xD(A)

(λIA)xλx.

A couple of equivalent definitions are given below. A dissipative operator is called maximally dissipative if it is dissipative and for all λ > 0 the operator λIA is surjective, meaning that the range when applied to the domain D is the whole of the space X.

An operator that obeys a similar condition but with a plus sign instead of a minus sign (that is, the negation of a dissipative operator) is called an accretive operator.[1]

The main importance of dissipative operators is their appearance in the Lumer–Phillips theorem which characterizes maximally dissipative operators as the generators of contraction semigroups.

Properties

A dissipative operator has the following properties:[2]

  • From the inequality given above, we see that for any x in the domain of A, if ‖x‖ ≠ 0 then (λIA)x0, so the kernel of λIA is just the zero vector and λIA is therefore injective and has an inverse for all λ > 0. (If we have the strict inequality (λIA)x>λx for all non-null x in the domain, then, by the triangle inequality, λx+Ax(λIA)x>λx, which implies that A itself has an inverse.) We may then state that
(λIA)1z1λz
for all z in the range of λIA. This is the same inequality as that given at the beginning of this article, with z=(λIA)x. (We could equally well write these as (IκA)1zz or (IκA)xx which must hold for any positive κ.)
  • λIA is surjective for some λ > 0 if and only if it is surjective for all λ > 0. (This is the aforementioned maximally dissipative case.) In that case one has (0, ∞) ⊂ ρ(A) (the resolvent set of A).
  • A is a closed operator if and only if the range of λI - A is closed for some (equivalently: for all) λ > 0.

Equivalent characterizations

Define the duality set of xX, a subset of the dual space X' of X, by

J(x):={xX:xX2=xX2=x,x}.

By the Hahn–Banach theorem this set is nonempty.[3] In the Hilbert space case (using the canonical duality between a Hilbert space and its dual) it consists of the single element x.[4] More generally, if X is a Banach space with a strictly convex dual, then J(x) consists of a single element.[5] Using this notation, A is dissipative if and only if[6] for all xD(A) there exists a x' ∈ J(x) such that

ReAx,x0.

In the case of Hilbert spaces, this becomes ReAx,x0 for all x in D(A). Since this is non-positive, we have

xAx2=x2+Ax22ReAx,xx2+Ax2+2ReAx,x=x+Ax2
xAxx+Ax

Since I−A has an inverse, this implies that (I+A)(IA)1 is a contraction, and more generally, (λI+A)(λIA)1 is a contraction for any positive λ. The utility of this formulation is that if this operator is a contraction for some positive λ then A is dissipative. It is not necessary to show that it is a contraction for all positive λ (though this is true), in contrast to (λI−A)−1 which must be proved to be a contraction for all positive values of λ.

Examples

  • For a simple finite-dimensional example, consider n-dimensional Euclidean space Rn with its usual dot product. If A denotes the negative of the identity operator, defined on all of Rn, then
xAx=x(x)=x20,
so A is a dissipative operator.
  • So long as the domain of an operator A (a matrix) is the whole Euclidean space, then it is dissipative if and only if A+A* (the sum of A and its adjoint) does not have any positive eigenvalue, and (consequently) all such operators are maximally dissipative. This criterion follows from the fact that the real part of x*Ax, which must be nonpositive for any x, is x*A+A*2x. The eigenvalues of this quadratic form must therefore be nonpositive. (The fact that the real part of x*Ax, must be nonpositive implies that the real parts of the eigenvalues of A must be nonpositive, but this is not sufficient. For example, if A=(1301) then its eigenvalues are negative, but the eigenvalues of A+A* are −5 and 1, so A is not dissipative.) An equivalent condition is that for some (and hence any) positive λ,λA has an inverse and the operator (λ+A)(λA)1 is a contraction (that is, it either diminishes or leaves unchanged the norm of its operand). If the time derivative of a point x in the space is given by Ax, then the time evolution is governed by a contraction semigroup that constantly decreases the norm (or at least doesn't allow it to increase). (Note however that if the domain of A is a proper subspace, then A cannot be maximally dissipative because the range will not have a high enough dimensionality.)
  • Consider H = L2([0, 1]; R) with its usual inner product, and let Au = u′ (in this case a weak derivative) with domain D(A) equal to those functions u in the Sobolev space H1([0,1];𝐑) with u(1) = 0. D(A) is dense in L2([0, 1]; R). Moreover, for every u in D(A), using integration by parts,
u,Au=01u(x)u(x)dx=12u(0)20.
Hence, A is a dissipative operator. Furthermore, since there is a solution (almost everywhere) in D to uλu=f for any f in H, the operator A is maximally dissipative. Note that in a case of infinite dimensionality like this, the range can be the whole Banach space even though the domain is only a proper subspace thereof.
  • Consider H = H02(Ω; R) (see Sobolev space) for an open and connected domain Ω ⊆ Rn and let A = Δ, the Laplace operator, defined on the dense subspace of compactly supported smooth functions on Ω. Then, using integration by parts,
u,Δu=Ωu(x)Δu(x)dx=Ω|u(x)|2dx=uL2(Ω;𝐑)20,
so the Laplacian is a dissipative operator.

Notes

  1. "Dissipative operator". Encyclopedia of Mathematics. http://www.encyclopediaofmath.org/index.php/Dissipative_operator. 
  2. Engel and Nagel Proposition II.3.14
  3. The theorem implies that for a given x there exists a continuous linear functional φ with the property that φ(x)=‖x‖, with the norm of φ equal to 1. We identify ‖x‖φ with x'.
  4. Engel and Nagel Exercise II.3.25i
  5. Engel and Nagel Example II.3.26
  6. Engel and Nagel Proposition II.3.23

References

  • Engel, Klaus-Jochen; Nagel, Rainer (2000). One-parameter semigroups for linear evolution equations. Springer. 
  • Renardy, Michael; Rogers, Robert C. (2004). An introduction to partial differential equations. Texts in Applied Mathematics 13 (Second ed.). New York: Springer-Verlag. pp. 356. ISBN 0-387-00444-0.  (Definition 12.25)