Linear Operator and Self Adjoint

In summary: Just by definition of a_ij we have: <Lvj,vi>=a_{ij}=<Lv_{i},v_{j}>, which is exactly the statement of ii).In summary, L is self-adjoint means that L is equal to its own adjoint L*. To prove this, we need to show that <Lu,v>=<u,Lv> for all u,v in V. To prove this, we use the fact that any vector v can be written as a linear combination of basis vectors v_i, and recall that <Lvi,uj>=<vi,Luj>. This leads to the conclusion that <Lu,v>=<u,Lv>. Finally, to prove ii), we simply use the definition of
  • #1
charikaar
5
0
I would be grateful for some help/tips/with this question.

Let (V,<,>) be a complex inner product space with an orthonormal basis {v1,v2,...vn}. Let L:V------>V be a linear operator. Explain what is meant by saying that L is self-adjoint. Let A=a_ij be the matrix representing L with respect to the basis {v1,...v_n}. prove the following.

i) L is self-adjoint if and only if <Lvi,vj>=<vi,Lvj>.
ii) a_ij=<Lvj,vi>.

L is self adjoint means L = L*, but we know L* is the one and unique operator for which <Lv, u> = <v, L*u> for all u,v. How do i prove i) and ii).

Thanks
 
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  • #2
Let's begin with the statement i). You gave a definition of a self-adjoint operator. This definition implies (since L=L*) that L is self-adjoint iff <Lu,v>=<u,Lv> for all u,v in V. Thus, it's obvious that if L is self-adjoint then <Lvi,vj>=<vi,Lvj>. Now we'll prove the converse assertion. That is, we should show that <Lu,v>=<u,Lv>. Note first that any vector v is written as v=b_{i}v_{i}, where b_{i},i=1,...,n, are complex numbers. In addition we remember that <Lvi,uj>=<vi,Luj>. We have:
<Lu,v>=<L(b_{i}v_{i},c_{j}u_{j})>=b_{i}c*_{j}<Lvi,uj>=b_{i}c*_{j}<vi,Luj>=<b_{i}v_{i},c_{j}Lu_{j}>=<v,Lu>

To prove ii) you should recall the meaning of numbers a_ij. Being more precise, if v_i is a basis vector then Lv_{i}=a_{ij}v_{j}.
 

What is a linear operator?

A linear operator is a mathematical function that maps a vector space onto itself. It preserves the vector space's linear structure, meaning that the operator maintains the properties of addition and scalar multiplication.

What is the difference between a linear operator and a matrix?

A linear operator is a generalization of a matrix. While a matrix is a table of numbers, a linear operator is a function that can be applied to any vector in a vector space. A matrix can be thought of as a representation of a linear operator with respect to a specific basis.

What does it mean for a linear operator to be self-adjoint?

A linear operator is self-adjoint if it is equal to its own adjoint, or if its adjoint is the same as its transpose. This means that the operator has the same effect on a vector as its adjoint, or conjugate transpose, has on the same vector.

Why is self-adjointness important in linear algebra?

Self-adjoint operators have many important properties, including real eigenvalues and orthogonal eigenvectors. This makes them particularly useful in applications such as quantum mechanics and signal processing. Additionally, self-adjoint operators are easier to work with mathematically, making them a convenient tool in linear algebra.

How are linear operators related to other mathematical concepts?

Linear operators are closely related to matrices, as well as other concepts in linear algebra such as vector spaces, bases, and inner products. They also have connections to other branches of mathematics, including functional analysis and operator theory.

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