Spectrum of a linear operator on a Banach space

In summary, the spectrum and resolvent of a linear operator on a Banach space can be understood in a general sense as a linear operator T: D(T) \to X, where X is a Banach space and D(T) is a subspace of X. The Wikipedia article states that for a value \lambda to be in the resolvent set, (T-\lambda)^{-1} must exist and be defined on a dense subset of X, which is equivalent to saying that Ran(T-\lambda) must be dense in X and (T-\lambda): D(T) \to Ran(T-\lambda) must be a bijection. A more general definition can be found in Banach algebras, but it
  • #1
AxiomOfChoice
533
1
I'm trying to understand the spectrum and resolvent of a linear operator on a Banach space in as much generality as I possibly can.

It seems that the furthest the concept can be "pulled back" is to a linear operator [itex]T: D(T) \to X[/itex], where [itex]X[/itex] is a Banach space and [itex]D(T)\subseteq X[/itex]. But here are a few questions:

(1) Doesn't [itex]D(T)[/itex] necessarily have to be a subspace of [itex]X[/itex] for this concept to make any sense? For instance, if it's not a subspace, there can be elements [itex]x,y[/itex] for which [itex]Tx[/itex] and [itex]Ty[/itex] make sense, but for which [itex]T(x+y)[/itex] makes no sense, since [itex]x+y[/itex] might not be an element of [itex]D(T)[/itex].

(2) The Wikipedia article here says that, in order for [itex]\lambda \in \rho(T)[/itex], we need both (1) [itex](T-\lambda)^{-1}[/itex] exists and (2) [itex](T-\lambda)^{-1}[/itex] is defined on a dense subset of [itex]X[/itex]. Is this equivalent to saying that [itex]\text{Ran} (T-\lambda)[/itex] must be a dense subset of [itex]X[/itex] and that [itex](T-\lambda): D(T) \to \text{Ran} (T - \lambda)[/itex] must be a bijection?
 
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  • #2
AxiomOfChoice said:
I'm trying to understand the spectrum and resolvent of a linear operator on a Banach space in as much generality as I possibly can.

It seems that the furthest the concept can be "pulled back" is to a linear operator [itex]T: D(T) \to X[/itex], where [itex]X[/itex] is a Banach space and [itex]D(T)\subseteq X[/itex]. But here are a few questions:

(1) Doesn't [itex]D(T)[/itex] necessarily have to be a subspace of [itex]X[/itex] for this concept to make any sense? For instance, if it's not a subspace, there can be elements [itex]x,y[/itex] for which [itex]Tx[/itex] and [itex]Ty[/itex] make sense, but for which [itex]T(x+y)[/itex] makes no sense, since [itex]x+y[/itex] might not be an element of [itex]D(T)[/itex].

Yes. And we usually require ##D(T)## to be dense in ##T## as well. It doesn't really matter, if it's not dense, then we can always restrict ##X## to ##\overline{D(T)}## and work with that.

(2) The Wikipedia article here says that, in order for [itex]\lambda \in \rho(T)[/itex], we need both (1) [itex](T-\lambda)^{-1}[/itex] exists and (2) [itex](T-\lambda)^{-1}[/itex] is defined on a dense subset of [itex]X[/itex]. Is this equivalent to saying that [itex]\text{Ran} (T-\lambda)[/itex] must be a dense subset of [itex]X[/itex] and that [itex](T-\lambda): D(T) \to \text{Ran} (T - \lambda)[/itex] must be a bijection?

Yes, this is correct. We also want ##(T- \lambda I)^{-1}## to be bounded though.

If you want to see a very general definition of spectrum, then you should study Banach algebras. But this is a spectrum that coincides with the spectrum of bounded operators.
 

1. What is a linear operator on a Banach space?

A linear operator on a Banach space is a function that maps elements from one Banach space to another while preserving the linear structure of the space. It is denoted by L(X, Y), where X and Y are Banach spaces.

2. What is the spectrum of a linear operator?

The spectrum of a linear operator is the set of all complex numbers for which the operator does not have an inverse. It is denoted by σ(L) and can be broken down into three parts: the point spectrum, continuous spectrum, and residual spectrum.

3. How is the spectrum of a linear operator related to its eigenvalues?

The eigenvalues of a linear operator are a subset of the point spectrum of the operator. However, not all elements in the point spectrum are necessarily eigenvalues. The remaining elements in the point spectrum and the continuous and residual spectra together make up the full spectrum of the operator.

4. What is the significance of the spectrum of a linear operator?

The spectrum of a linear operator provides important information about the behavior and properties of the operator. For example, the location of the spectrum in the complex plane can indicate whether the operator is invertible or not. It also helps in the analysis of the operator's stability, convergence, and other important properties.

5. How is the spectrum of a linear operator calculated?

The spectrum of a linear operator can be calculated using various techniques, such as the spectral mapping theorem, the Gelfand transform, or the resolvent formula. These methods involve finding the inverse of the operator or solving certain equations to determine the spectrum. In some cases, the spectrum can be explicitly calculated for specific types of operators, such as compact or self-adjoint operators.

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