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Let \(V\) and \(W\) be finite-dimensional inner product spaces, and let \(T: V \rightarrow W\) be linear. Prove the following results. (a) If \(\mathrm{T}\) is one-to-one, then \(\mathrm{T}^{*} \mathrm{~T}\) is invertible and \(\mathrm{T}^{\dagger}=\left(\mathrm{T}^{*} \mathrm{~T}\right)^{-1} \mathrm{~T}^{*}\). (b) If \(\mathrm{T}\) is onto, then \(\mathrm{TT}^{*}\) is invertible and \(\mathrm{T}^{\dagger}=\mathrm{T}^{*}\left(\mathrm{TT}^{*}\right)^{-1}\).

Short Answer

Expert verified
In summary, we proved the following properties of a linear transformation \(T: V \rightarrow W\): (a) If \(T\) is one-to-one, then \(T^*~T\) is invertible, and \(T^{\dagger} = (T^*~T)^{-1} T^*\). (b) If \(T\) is onto, then \(TT^*\) is invertible, and \(T^{\dagger} = T^*(TT^*)^{-1}\).

Step by step solution

01

Lemma: Adjoint Property

Recall that, by definition, the adjoint of a linear transformation \(T: V \rightarrow W\) is the transformation \(T^*: W \rightarrow V\) satisfying the following property for all \(\textbf{v} \in V\) and \(\textbf{w} \in W\): \[\langle T(\textbf{v}), \textbf{w} \rangle_W = \langle \textbf{v}, T^*(\textbf{w}) \rangle_V\] #a# Step 1: Prove \(T^*~T\) is invertible
02

Prove \(\mathrm{Ker}\left(\mathrm{T}^{*} \mathrm{~T}\right)=\{0\}\) (Kernel)

If \(T\) is one-to-one, we have that \(\mathrm{Ker}(T)=\{0\}\). Using the adjoint property, let \(\textbf{v} \in \mathrm{Ker}(T^*~T)\), we get \[\langle T^* \mathrm{~T}(\textbf{v}), \textbf{v} \rangle_V = \langle T(\textbf{v}), T(\textbf{v}) \rangle_W = 0\] Therefore, \(T(\textbf{v})=0\). Since \(T\) is one-to-one, \(\textbf{v} = 0\). Thus, \(\mathrm{Ker}(T^* \mathrm{~T}) = \{0\}\).
03

Prove \(\mathrm{dim}\left(\mathrm{Im}\left(\mathrm{T}^{*}\mathrm{~T}\right)\right)=\mathrm{dim}(V)\) (Image)

Since \(\mathrm{Im}(T^* \mathrm{~T})⊆V\). Using the Rank-Nullity Theorem, we have: \[ \mathrm{dim}(V) = \mathrm{dim}\left(\mathrm{Ker}\left(\mathrm{T}^{*} \mathrm{~T}\right)\right) + \mathrm{dim}\left(\mathrm{Im}\left(\mathrm{T}^{*}\mathrm{~T}\right)\right)\] As we have shown that \(\mathrm{Ker}\left(\mathrm{T}^{*} \mathrm{~T}\right)=\{0\}\), we get \(\mathrm{dim}\left(\mathrm{Im}\left(\mathrm{T}^{*}\mathrm{~T}\right)\right)=\mathrm{dim}(V)\). Since \(\mathrm{Ker}\left(\mathrm{T}^{*} \mathrm{~T}\right)=\{0\}\) and \(\mathrm{dim}\left(\mathrm{Im}\left(\mathrm{T}^{*}\mathrm{~T}\right)\right)=\mathrm{dim}(V)\), it follows that \(T^*~T\) is invertible. Step 2: Prove \(T^{\dagger}=(T^* \mathrm{~T})^{-1}~T^*\) By definition of the adjoint, \[\langle T^\dagger T(\textbf{v}), \textbf{v} \rangle_V = \langle (\mathrm{I}_W(\textbf{v})), \textbf{v} \rangle_V = \langle \textbf{v}, \textbf{v} \rangle_V\] Since \(T^*~T\) is invertible, \[\langle (T^* \mathrm{~T})^{-1}~T^*~T(\textbf{v}), \textbf{v} \rangle_V = \langle \textbf{v}, \textbf{v} \rangle_V\] Now, since both the expressions equal \(\langle \textbf{v}, \textbf{v} \rangle_V\), we can conclude that \(T^\dagger = (T^* \mathrm{~T})^{-1}~T^*\). #b# Step 3: Prove \(TT^*\) is invertible
04

Prove \(\mathrm{Ker}\left(\mathrm{TT}^{*}\right)=\{0\}\) (Kernel)

Let \(\textbf{w} \in \mathrm{Ker}(TT^*)\), then using the adjoint property, we have: \[\langle TT^*(\textbf{w}),\textbf{w}\rangle_W = \langle T^*(\textbf{w}),T^*(\textbf{w})\rangle_V = 0.\] Therefore, \(T^*(\textbf{w})=0\). Since \(\mathrm{Ker}(T^*) = \{0\}\), we must have \(\textbf{w}=0\). Thus, \(\mathrm{Ker}(TT^*)=\{0\}\).
05

Prove \(\mathrm{dim}\left(\mathrm{Im}\left(\mathrm{TT}^{*}\right)\right)=\mathrm{dim}(W)\) (Image)

Using the Rank-Nullity Theorem again, we have: \[ \mathrm{dim}(W) = \mathrm{dim}\left(\mathrm{Ker}\left(\mathrm{TT}^{*}\right)\right) + \mathrm{dim}\left(\mathrm{Im}\left(\mathrm{TT}^{*}\right)\right)\] Since we have shown that \(\mathrm{Ker}(TT^*) = \{0\}\), we get \(\mathrm{dim}\left(\mathrm{Im}\left(\mathrm{TT}^{*}\right)\right)=\mathrm{dim}(W)\). Since \(\mathrm{Ker}(TT^*)=\{0\}\) and \(\mathrm{dim}\left(\mathrm{Im}\left(\mathrm{TT}^{*}\right)\right)=\mathrm{dim}(W)\), it follows that \(TT^*\) is invertible. Step 4: Prove \(T^{\dagger}=T^*(TT^*)^{-1}\) By definition of the adjoint, \[\langle TT^\dagger(\textbf{w}),\textbf{w}\rangle_W = \langle (\mathrm{I}_V(\textbf{w})), \textbf{w} \rangle_W = \langle \textbf{w}, \textbf{w} \rangle_W\] Since \(TT^*\) is invertible, \[\langle T(T^*(TT^*)^{-1})(\textbf{w}),\textbf{w}\rangle_W = \langle \textbf{w}, \textbf{w} \rangle_W\] Now, since both the expressions equal \(\langle \textbf{w}, \textbf{w} \rangle_W\), we can conclude that \(T^\dagger = T^*(TT^*)^{-1}\).

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Key Concepts

These are the key concepts you need to understand to accurately answer the question.

Adjoint of a Linear Transformation
In linear algebra, the adjoint of a linear transformation is a concept that bridges functions and their dual spaces. The adjoint provides a powerful way to connect a linear transformation and its corresponding conjugate transpose.
When you have a linear map or a transformation, denoted by \( T: V \rightarrow W \), its adjoint is expressed as \( T^*: W \rightarrow V \). This adjoint is defined by a special relationship involving inner products: for all vectors \( \textbf{v} \in V \) and \( \textbf{w} \in W \), this relationship is \( \langle T(\textbf{v}), \textbf{w} \rangle_W = \langle \textbf{v}, T^*(\textbf{w}) \rangle_V \).
  • \( \langle \cdot, \cdot \rangle_W \) and \( \langle \cdot, \cdot \rangle_V \) represent the inner products in spaces \( W \) and \( V \), respectively.
  • This property makes the adjoint very useful for analyzing linear transformations, especially in the context of Hilbert spaces where inner products define geometry.
Invertibility
Invertibility in linear algebra refers to the ability of a matrix or linear transformation to be reversed. An invertible linear transformation is one where every output or image can be traced back to exactly one input.
For a linear transformation \( T: V \rightarrow W \) to be invertible, it must be both one-to-one (injective) and onto (surjective). When a transformation is one-to-one, no two different inputs point to the same output. Meanwhile, a transformation is onto when every element in the codomain \( W \) has some corresponding element in the domain \( V \).
  • If \( T \) is one-to-one, \( T^*T \) is invertible, as shown in the provided solution part (a).
  • If \( T \) is onto, \( TT^* \) is invertible, corresponding to part (b) of the exercise.
  • The inverse of a linear transformation \( T \) is denoted \( T^{-1} \), and it satisfies \( T(T^{-1}(\textbf{w})) = \textbf{w} \) for all \( \textbf{w} \in W \).
Rank-Nullity Theorem
The Rank-Nullity Theorem is a central theorem in linear algebra that establishes a relationship between dimension properties of linear transformations. This theorem states that for a linear transformation \( T: V \rightarrow W \), the dimension of the domain \( V \) is the sum of the rank of \( T \) and the nullity of \( T \).
The rank of \( T \), \( \text{rank}(T) \), is the dimension of its image (the set of outputs), and the nullity, \( \text{nullity}(T) \), is the dimension of its kernel (the set of vectors mapped to zero).
\[ \text{dim}(V) = \text{rank}(T) + \text{nullity}(T) \]
  • The theorem confirms that the greater the rank, the more the original space \( V \)'s dimensions are used in mapping outputs in \( W \).
  • In the context of an invertible transformation, the kernel must be zero-dimensional, meaning \( T \) can be inverted without ambiguity.
Kernel and Image
In linear transformations, understanding the kernel and image is crucial for analyzing the transformation’s effects. The kernel of a linear transformation \( T: V \rightarrow W \), denoted by \( \text{Ker}(T) \), is the set of vectors in \( V \) that are mapped to the zero vector in \( W \).
On the other hand, the image of \( T \), represented as \( \text{Im}(T) \), consists of all vectors in \( W \) that are outputs of applying \( T \) to vectors in \( V \). These concepts help determine if a transformation is injective or surjective:
  • If \( \text{Ker}(T) = \{0\} \), the transformation is injective; each input corresponds uniquely to an output in the image.
  • If \( \text{Im}(T) = W \), the transformation is surjective, covering the entire codomain.
  • Understanding these sets allows you to apply the rank-nullity theorem effectively.

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Most popular questions from this chapter

Let \(S=\\{(1,0, i),(1,2,1)\\}\) in C \(^{3}\). Compute \(S^{\perp}\).

Let \(\mathrm{T}\) and \(\mathrm{U}\) be positive definite operators on an inner product space V. Prove the following results. (a) \(\mathrm{T}+\mathrm{U}\) is positive definite. (b) If \(c>0\), then \(c \mathrm{~T}\) is positive definite. (c) \(\mathrm{T}^{-1}\) is positive definite. Visit goo.gl/cQch7i for a solution.

Give an example of a linear operator \(\mathrm{T}\) on an inner product space \(\mathrm{V}\) such that \(N(T) \neq N\left(T^{*}\right)\).

Let \(V\) be the vector space defined in Example 5 of Section 1.2, the space of all sequences \(\sigma\) in \(F\) (where \(F=R\) or \(F=C\) ) such that \(\sigma(n) \neq 0\) for only finitely many positive integers \(n\). For \(\sigma, \mu \in \mathrm{V}\), we define \(\langle\sigma, \mu\rangle=\sum_{n=1}^{\infty} \sigma(n) \overline{\mu(n)}\). Since all but a finite number of terms of the series are zero, the series converges. (a) Prove that \(\langle\cdot, \cdot\rangle\) is an inner product on \(\mathrm{V}\), and hence \(\mathrm{V}\) is an inner product space. (b) For each positive integer \(n\), let \(e_{n}\) be the sequence defined by \(e_{n}(k)=\delta_{n k}\), where \(\delta_{n k}\) is the Kronecker delta. Prove that \(\left\\{e_{1}, e_{2}, \ldots\right\\}\) is an orthonormal basis for \(\mathrm{V}\). (c) Let \(\sigma_{n}=e_{1}+e_{n}\) and \(\mathrm{W}=\operatorname{span}\left(\left\\{\sigma_{n}: n \geq 2\right\\}\right.\). (i) Prove that \(e_{1} \notin \mathrm{W}\), so \(\mathrm{W} \neq \mathrm{V}\). (ii) Prove that \(\mathrm{W}^{\perp}=\\{0\\}\), and conclude that \(\mathrm{W} \neq\left(\mathrm{W}^{\perp}\right)^{\perp}\). Thus the assumption in Exercise 13(c) that \(\mathrm{W}\) is finite-dimensional is essential.

31\. Let \(\mathrm{H}_{u}\) be a Householder operator on a finite-dimensional inner product space V. Prove the following results. (a) \(\mathrm{H}_{u}\) is linear. (b) \(\mathrm{H}_{u}(x)=x\) if and only if \(x\) is orthogonal to \(u\). (c) \(\mathrm{H}_{u}(u)=-u\). (d) \(\mathrm{H}_{u}^{*}=\mathrm{H}_{u}\) and \(\mathrm{H}_{u}^{2}=\mathrm{I}\), and hence \(\mathrm{H}_{u}\) is a unitary [orthogonal] operator on \(\mathrm{V}\). (Note: If \(\mathrm{V}\) is a real inner product space, then in the language of Section \(6.11, \mathrm{H}_{u}\) is a reflection.) \({ }^{1}\) At one time, because of its great stability, this method for solving large systems of linear equations with a computer was being advocated as a better method than Gaussian elimination even though it requires about three times as much work. (Later, however, J. H. Wilkinson showed that if Gaussian elimination is done "properly," then it is nearly as stable as the orthogonalization method.)

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