What Is a Core for an Unbounded Operator?
At a glance
- 1. Choose a subdomain
Start with a linear operator and a candidate subdomain contained in its domain.
- 2. Use the graph norm
Measure approximation in both the vector and its image under the operator.
- 3. Check graph density
The candidate is a core when its graph is dense in the operator's graph.
- 4. Compare closures
Equivalently, restricting the operator to the core does not change its closure.
A core is a smaller domain on which an unbounded operator is fully determined by limits. This idea is useful because an operator may be difficult to work with on its entire domain, while a simpler subdomain—such as smooth functions—can still contain all the information needed to recover it by closure.
The key is to measure density in the graph norm, which accounts for both a vector and the operator applied to it. Ordinary density in the underlying Hilbert space is not enough.
Definition of a core
Let be a linear operator on a Hilbert space. For , define the graph norm by
A linear subspace is a core for if it is dense in with respect to this norm. Equivalently, the graph
is dense in the graph of in the product space .
In concrete terms, for every there must be a sequence such that both
in . Approximating alone does not establish that is a core; the operator images must converge to the right limit too.
The closure criterion
If is closable, its closure is the operator whose graph is the closure of the graph of . The core condition can then be written as
Thus, starting with the restriction of to and closing that restriction gives the same operator as closing on its full domain. If is already closed, this becomes .
This characterization also explains the word “core”: the restricted operator retains enough graph information to reconstruct the whole closed operator.
Worked example: multiplication by the variable
Consider the operator on defined by
The space of smooth, compactly supported functions is a core for . Its graph norm is
To see why smooth compactly supported functions are dense in this norm, take any and first truncate it: let . Then
as , because the integrand is integrable by the definition of the domain. For fixed , approximate in ordinary by smooth functions supported in a slightly larger bounded interval. On that interval, is bounded, so this approximation also converges in the graph norm. Combining the truncation and smooth approximation gives a sequence from converging to in graph norm.
Therefore is a core. Notice that this conclusion is stronger than saying it is dense in : it also guarantees convergence after applying .
How to check a proposed core
For a candidate subspace , the most direct approach is to take an arbitrary and construct with both and . Alternatively, show that is dense in for the graph norm. For differential operators, this often means proving approximation in a function-space norm that controls both the function and the relevant derivatives.
When is closed, it is also enough to show that every graph point is a limit of graph points with . This is exactly the graph-density condition, expressed without introducing a separate norm.
Common mistakes
- Checking only density in . A subspace can be dense in the Hilbert space but fail to approximate operator images. That does not make it a core.
- Ignoring the domain. A candidate core must be a subspace of . It is not enough for its elements to be in the ambient space.
- Confusing a core with the whole domain. A core may be a proper subdomain; its defining property is that restriction to it leaves the closure unchanged.
- Mixing operator cores with form cores. A core for a quadratic form is defined using the form norm, which is generally different from the graph norm of an operator. State which notion is meant, especially in discussions of self-adjoint operators.
References
- Methods of Modern Mathematical Physics, Vol. I: Functional Analysis — Michael Reed and Barry Simon
- Perturbation Theory for Linear Operators — Tosio Kato
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