[Linear Algebra] Finding T*; complex conjugate linear transformation

In summary, the problem involves finding the adjoint of a linear operator in the vector space of real linear polynomials. The inner product for this vector space is given, and the adjoint is defined as the linear transformation that satisfies the inner product property. The solution involves finding an orthonormal basis for the vector space and using the hermitian conjugate to construct the adjoint matrix. The matrix for the adjoint is then used to construct the linear mapping for the adjoint.
  • #1
mick25
13
0
[Linear Algebra] Finding T* adjoint of a linear operator

Homework Statement



Consider [itex]P_1{}(R)[/itex], the vector space of real linear polynomials, with inner product

[itex] < p(x), q(x) > = \int_0^1 \! p(x)q(x) \, \mathrm{d} x [/itex]

Let [itex]T: P_1{}(R) \rightarrow P_1{}(R)[/itex] be defined by [itex]T(p(x)) = p'(x) + p(x)[/itex]. Find [itex] T^*(p(x)) [/itex] for an arbitrary
[itex]p(x) = a + bx^2 \in P_1{}(R)[/itex].


Homework Equations



[itex] < T(p(x)), q(x) > = < p(x), T^*(q(x)) > [/itex]

The Attempt at a Solution



Using the standard basis of [itex]P_1{}(R), \alpha = <1, x>[/itex]

[itex]<T(1),1> = 1[/itex]
[itex]<T(1), x> = 1/2[/itex]
[itex]<T(x), 1> = 3/2[/itex]
[itex]<T(x), x> = 5/6[/itex]

[itex]
[T]_\alpha^{\alpha} =
\left[ {\begin{array}{cc}
1 & 3/2 \\
1/2 & 5/6 \\
\end{array} } \right]
[/itex]
[itex]
[T^*]_\alpha^{\alpha} =
\left[ {\begin{array}{cc}
1 & 1/2 \\
3/2 & 5/6 \\
\end{array} } \right]
[/itex]

Not sure how to find [itex]T^*[/itex] from here...
 
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  • #2
What you did only works for an orthonormal basis. So perhaps you should first find an orthonormal basis of your space??
 
  • #3
I just got the new matrices with the orthonormal basis but I'm still at the same sticking point

The matrices aren't equal so T isn't self-adjoint; that's all I can conclude right now
 
  • #4
If you work in an orthonormal basis, then the adjoint is given by the hermition conjugate. So find the hermitian conjugate and express the matrix as a linear transformation. And there you have your adjoint!
 
  • #5
[itex]\alpha = <1, \sqrt{3}(2x-1)>[/itex] is an orthonormal basis for [itex]P_1{}(R)[/itex]

By the definition of the inner product space, have

[itex] < T(p(x)), q(x) > = \int_0^1 \! p'(x)q(x) \, \mathrm{d} x + \int_0^1 \! p(x)q(x) \, \mathrm{d} x [/itex]

So I computed

[itex]< T(1), 1 > = 1 [/itex]

[itex]< T(1), \sqrt{3}(2x-1) > = 0[/itex]

[itex]< T(\sqrt{3}(2x-1)), 1 > = 4\sqrt3[/itex]

[itex]< T(\sqrt{3}(2x-1)), \sqrt{3}(2x-1) > = 1[/itex]

[itex]
[T]_\alpha^{\alpha} =
\left[ {\begin{array}{cc}
1 & 4\sqrt{3} \\
0 & 1 \\
\end{array} } \right]
[/itex]

[itex]
[T^*]_\alpha^{\alpha} =
\left[ {\begin{array}{cc}
1 & 0 \\
4\sqrt{3} & 1 \\
\end{array} } \right]
[/itex]

Still not sure how to find [itex]T^*[/itex] from this...

Verifying the matrix...

[itex] < T(p(x)), q(x) > = \int_0^1 \! p'(x)q(x) \, \mathrm{d} x + \int_0^1 \! p(x)q(x) \, \mathrm{d} x = < p(x), T^*(q(x))> [/itex]

[itex] < T(p(x)), q(x) > = \int_0^1 \! p'(x)q(x) \, \mathrm{d} x + \int_0^1 \! p(x)q(x) \, \mathrm{d} x [/itex]

[itex] < p(x), T(q(x)) > = \int_0^1 \! p(x)q'(x) \, \mathrm{d} x + \int_0^1 \! p(x)q(x) \, \mathrm{d} x = p(1)q(1) - p(0)q(0) - \int_0^1 \! p'(x)q(x) \, \mathrm{d} x + \int_0^1 \! p(x)q(x) \, \mathrm{d} x[/itex]

So [itex] T [/itex] is not self-adjoint.
 
  • #6
Doesn't that matrix for T* actually give you T*?? How do you construct a linear mapping from a matrix?
 

1. What is a complex conjugate linear transformation?

A complex conjugate linear transformation is a type of linear transformation that involves complex numbers. It is a function that maps one complex vector space to another, while preserving the complex conjugation operation. This means that the transformation of a complex number and its conjugate will also be complex conjugates.

2. How is a complex conjugate linear transformation represented?

A complex conjugate linear transformation is typically represented by the symbol T* or T-bar. This notation is used to distinguish it from a regular linear transformation, which is represented by the symbol T.

3. What is the difference between a complex conjugate linear transformation and a regular linear transformation?

The main difference between a complex conjugate linear transformation and a regular linear transformation is that the former involves complex numbers, while the latter only involves real numbers. Additionally, a complex conjugate linear transformation preserves the complex conjugation operation, while a regular linear transformation does not.

4. What is the importance of complex conjugate linear transformations in linear algebra?

Complex conjugate linear transformations are important in linear algebra as they allow for the study of complex vector spaces and their transformations. They also play a crucial role in quantum mechanics and other fields that involve complex numbers.

5. How do you find the complex conjugate of a linear transformation T?

To find the complex conjugate of a linear transformation T, you can use the following formula: T*(z) = conj(T(z)), where conj(z) is the complex conjugate of z. In other words, you take the complex conjugate of the output of the linear transformation T.

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