Matrices: a normal M, a projection Q, Hermitian transpose of product

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nomadreid
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Homework Statement
Given: orthogonal basis, normal matrix M, Projection P onto k-eigenspace, Q orthogonal complement of P, N*= Hermitian transpose of N. Prove: (QMQ)*=Q(M*)Q
Relevant Equations
M*M=MM*, PP=P, Q=(Id-P), Pv = w implies Mw=kw
Establish QMQ=QM and QM*Q = QM*, reducing the problem to
(QM)*=QM*
((Id-P)M)*=(Id-P)M*
(M-PM)*=M*-PM*
Applying to random vector v (ie. |v>),
(M-PM)*v = M*v-PM*v
Not sure where to go from here, although it is probably something that is supposed to be obvious.
Any help would be appreciated.
 
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This thread can be closed, as I understand how to do it now. Simply split the Hermitian transpose into its two parts as transposing then taking complex conjugate; use (AB)T= BTAT on ((QM)Q) twice, then use (this time using * as simply complex conjugate) (AB)*= A*B* twice. Sorry for the inconvenience; I should have seen this the first time.
 

FAQ: Matrices: a normal M, a projection Q, Hermitian transpose of product

What is a normal matrix?

A normal matrix is a matrix that commutes with its Hermitian transpose, meaning that \( M M^* = M^* M \), where \( M^* \) denotes the Hermitian (conjugate) transpose of \( M \). Normal matrices include important classes such as unitary, Hermitian, and skew-Hermitian matrices.

What is a projection matrix?

A projection matrix \( Q \) is a square matrix that satisfies the condition \( Q^2 = Q \). This means that applying the projection matrix twice is the same as applying it once. Projection matrices are used to project vectors onto a subspace and are idempotent by definition.

How do you compute the Hermitian transpose of a product of matrices?

The Hermitian transpose (also known as the conjugate transpose) of a product of matrices \( AB \) is given by \( (AB)^* = B^* A^* \). This property follows from the definition of the Hermitian transpose and the properties of matrix multiplication.

What are some properties of normal matrices?

Normal matrices have several important properties: they are diagonalizable by a unitary matrix, their eigenvalues are invariant under unitary transformations, and they have orthogonal eigenvectors. These properties make normal matrices particularly useful in various applications in linear algebra and quantum mechanics.

What is the significance of projection matrices in linear algebra?

Projection matrices are significant in linear algebra because they are used to represent linear transformations that project vectors onto a subspace. They are essential in various applications such as solving linear systems, performing dimensionality reduction, and in computer graphics for rendering scenes. Projection matrices also play a crucial role in least squares problems and in the study of orthogonal projections.

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