/*! This file is auto-generated */ .wp-block-button__link{color:#fff;background-color:#32373c;border-radius:9999px;box-shadow:none;text-decoration:none;padding:calc(.667em + 2px) calc(1.333em + 2px);font-size:1.125em}.wp-block-file__button{background:#32373c;color:#fff;text-decoration:none} Problem 34 Given \(\mathbf{u} \neq \mathbf{... [FREE SOLUTION] | 91Ó°ÊÓ

91Ó°ÊÓ

Given \(\mathbf{u} \neq \mathbf{0}\) in \(\mathbb{R}^{n},\) let \(L=\operatorname{Span}\\{\mathbf{u}\\} .\) For \(\mathbf{y}\) in \(\mathbb{R}^{n},\) the reflection of \(\mathbf{y}\) in \(L\) is the point refl \(_{L} \mathbf{y}\) defined by \(\operatorname{ref}_{L} \mathbf{y}=2 \cdot \operatorname{proj}_{L} \mathbf{y}-\mathbf{y}\) See the figure, which shows that refl \(_{L} \mathbf{y}\) is the sum of \(\hat{\mathbf{y}}=\operatorname{proj}_{L} \mathbf{y}\) and \(\hat{\mathbf{y}}-\mathbf{y} .\) Show that the mapping \(\mathbf{y} \mapsto \operatorname{ref}_{L} \mathbf{y}\) is a linear transformation.

Short Answer

Expert verified
Reflection in a subspace is a linear transformation.

Step by step solution

01

Define the Reflection and Projection

The reflection of a vector \( \mathbf{y} \) in the line \( L \) is given by the formula: \( \operatorname{ref}_{L} \mathbf{y} = 2 \cdot \operatorname{proj}_{L} \mathbf{y} - \mathbf{y} \). The projection of \( \mathbf{y} \) onto \( \mathbf{u} \), a basis for \( L \), is \( \operatorname{proj}_{L} \mathbf{y} = \frac{\mathbf{y} \cdot \mathbf{u}}{\mathbf{u} \cdot \mathbf{u}} \mathbf{u} \). We need to use these definitions to show that reflection is a linear transformation.
02

Formulate Linear Transformation Test

To prove that \( \mathbf{y} \mapsto \operatorname{ref}_{L} \mathbf{y} \) is linear, we need to check if this mapping satisfies two properties of linear transformations: (i) \( \operatorname{ref}_{L} (\mathbf{a} + \mathbf{b}) = \operatorname{ref}_{L} \mathbf{a} + \operatorname{ref}_{L} \mathbf{b} \) for any vectors \( \mathbf{a} \) and \( \mathbf{b} \), and (ii) \( \operatorname{ref}_{L} (c\mathbf{a}) = c \operatorname{ref}_{L} \mathbf{a} \) for any scalar \( c \).
03

Verify Additivity of Reflection

Let's verify \( \operatorname{ref}_{L} (\mathbf{a} + \mathbf{b}) = \operatorname{ref}_{L} \mathbf{a} + \operatorname{ref}_{L} \mathbf{b} \).Compute \( \operatorname{ref}_{L} (\mathbf{a} + \mathbf{b}) = 2 \cdot \operatorname{proj}_{L} (\mathbf{a} + \mathbf{b}) - (\mathbf{a} + \mathbf{b}) \).Expanding, we have:\[ 2 \left( \frac{(\mathbf{a} + \mathbf{b}) \cdot \mathbf{u}}{\mathbf{u} \cdot \mathbf{u}} \mathbf{u} \right) - \mathbf{a} - \mathbf{b} \]Which simplifies to:\[ 2 \left( \frac{\mathbf{a} \cdot \mathbf{u} + \mathbf{b} \cdot \mathbf{u}}{\mathbf{u} \cdot \mathbf{u}} \mathbf{u} \right) - \mathbf{a} - \mathbf{b} \]Separately calculating \( \operatorname{ref}_{L} \mathbf{a} + \operatorname{ref}_{L} \mathbf{b} \), we find:\[ \left( 2 \cdot \operatorname{proj}_{L} \mathbf{a} - \mathbf{a} \right) + \left( 2 \cdot \operatorname{proj}_{L} \mathbf{b} - \mathbf{b} \right) \]And this simplifies to:\[ 2 \left( \frac{\mathbf{a} \cdot \mathbf{u}}{\mathbf{u} \cdot \mathbf{u}} \mathbf{u} \right) + 2 \left( \frac{\mathbf{b} \cdot \mathbf{u}}{\mathbf{u} \cdot \mathbf{u}} \mathbf{u} \right) - \mathbf{a} - \mathbf{b} \]Clearly, both expressions are equal, confirming additivity.
04

Verify Homogeneity of Reflection

Next, we need to verify \( \operatorname{ref}_{L} (c\mathbf{a}) = c \operatorname{ref}_{L} \mathbf{a} \).Compute \( \operatorname{ref}_{L} (c\mathbf{a}) = 2 \cdot \operatorname{proj}_{L} (c\mathbf{a}) - c\mathbf{a} \).This becomes:\[ 2 \left( \frac{(c\mathbf{a}) \cdot \mathbf{u}}{\mathbf{u} \cdot \mathbf{u}} \mathbf{u} \right) - c\mathbf{a} \]Which simplifies to:\[ 2c \left( \frac{\mathbf{a} \cdot \mathbf{u}}{\mathbf{u} \cdot \mathbf{u}} \mathbf{u} \right) - c\mathbf{a} \]Now compute \( c \operatorname{ref}_{L} \mathbf{a} = c(2 \cdot \operatorname{proj}_{L} \mathbf{a} - \mathbf{a}) \):\[ c \left( 2 \left( \frac{\mathbf{a} \cdot \mathbf{u}}{\mathbf{u} \cdot \mathbf{u}} \mathbf{u} \right) - \mathbf{a} \right) \]Simplifying gives:\[ 2c \left( \frac{\mathbf{a} \cdot \mathbf{u}}{\mathbf{u} \cdot \mathbf{u}} \mathbf{u} \right) - c\mathbf{a} \]Both expressions are equal, showing homogeneity.
05

Conclusion

Since the mapping \( \mathbf{y} \mapsto \operatorname{ref}_{L} \mathbf{y} \) satisfies both additivity and homogeneity properties, it is therefore a linear transformation.

Unlock Step-by-Step Solutions & Ace Your Exams!

  • Full Textbook Solutions

    Get detailed explanations and key concepts

  • Unlimited Al creation

    Al flashcards, explanations, exams and more...

  • Ads-free access

    To over 500 millions flashcards

  • Money-back guarantee

    We refund you if you fail your exam.

Over 30 million students worldwide already upgrade their learning with 91Ó°ÊÓ!

Key Concepts

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

Reflection
Reflection is the process of flipping a vector across a particular subspace, which in this case is a line. It helps us visualize how vectors can change orientation while maintaining certain characteristics. The formula for the reflection of a vector \( \mathbf{y} \) along a line \( L \) spanned by \( \mathbf{u} \) is given by: \( \operatorname{ref}_{L} \mathbf{y} = 2 \cdot \operatorname{proj}_{L} \mathbf{y} - \mathbf{y} \).
This operation creates a mirrored image of \( \mathbf{y} \) about the line \( L \). In simpler terms, the reflection of \( \mathbf{y} \) means bouncing it back through the line, effectively creating an opposite version on the other side of the line. This is especially useful in various computer graphics and geometrical transformations.
Projection
Projection involves mapping a vector onto another vector or subspace to find its component in that direction.
The projection of a vector \( \mathbf{y} \) onto a line spanned by \( \mathbf{u} \) is defined as \( \operatorname{proj}_{L} \mathbf{y} = \frac{\mathbf{y} \cdot \mathbf{u}}{\mathbf{u} \cdot \mathbf{u}} \mathbf{u} \).
This helps to extract the part of \( \mathbf{y} \) that lies in the direction of \( \mathbf{u} \).
  • Understanding projection aids in decomposing vectors into parts parallel and perpendicular to subspaces.
  • It is frequently used in fields like physics and computer graphics for realistic rendering and simulations.
Vector Spaces
Vector spaces are fundamental structures in linear algebra consisting of vectors, which can be added together or multiplied by scalars.
  • They provide a framework to study linear dependencies and transformations.
  • For a set to be a vector space, it must satisfy certain properties like closure under addition and scalar multiplication, presence of a zero vector, among others.
In this exercise, \( L = \operatorname{Span}\{\mathbf{u}\} \) represents a simple vector space spanned by a single non-zero vector \( \mathbf{u} \).
Understanding vector spaces helps grasp how linear transformations like reflections and projections operate within them.
Additivity
Additivity is one of the key properties of linear transformations.
It relates to whether a transformation distributes over vector addition. To check for additivity in this case, we confirm: \( \operatorname{ref}_{L} (\mathbf{a} + \mathbf{b}) = \operatorname{ref}_{L} \mathbf{a} + \operatorname{ref}_{L} \mathbf{b} \).
  • This means that reflecting the sum of two vectors should yield the same result as individually reflecting each vector and then adding the results.
Ensuring additivity is crucial because it allows the transformation to manage systems collectively without disrupting the individual components.
It simplifies computations and ensures the operations retain consistency.
Homogeneity
Homogeneity pertains to the proportional scaling property of linear transformations.
A transformation is homogeneous if \( \operatorname{ref}_{L} (c\mathbf{a}) = c \operatorname{ref}_{L} \mathbf{a} \) holds for any scalar \( c \).
This ensures that scaling a vector before applying the transformation is equivalent to scaling the result of the transformation afterward.
  • Homogeneity ensures transformations handle scalar multiples appropriately.
  • This feature of linear transformations allows us to model and predict the behavior of systems under scaling efficiently.
Together, additivity and homogeneity define the linearity essential for reflections, ensuring transformations are consistent and predictable.

One App. One Place for Learning.

All the tools & learning materials you need for study success - in one app.

Get started for free

Most popular questions from this chapter

In Exercises 17 and \(18,\) all vectors and subspaces are in \(\mathbb{R}^{n} .\) Mark each statement True or False. Justify each answer. a. If \(\left\\{\mathbf{v}_{1}, \mathbf{v}_{2}, \mathbf{v}_{3}\right\\}\) is an orthogonal basis for \(W,\) then mul- tiplying \(\mathbf{v}_{3}\) by a scalar \(c\) gives a new orthogonal basis \(\left\\{\mathbf{v}_{1}, \mathbf{v}_{2}, c \mathbf{v}_{3}\right\\} .\) b. The Gram-Schmidt process produces from a linearly in- dependent set \(\left\\{\mathbf{x}_{1}, \ldots, \mathbf{x}_{p}\right\\}\) an orthogonal set \(\left\\{\mathbf{v}_{1}, \ldots, \mathbf{v}_{p}\right\\}\) with the property that for each \(k,\) the vectors \(\mathbf{v}_{1}, \ldots, \mathbf{v}_{k}\) span the same subspace as that spanned by \(\mathbf{x}_{1}, \ldots, \mathbf{x}_{k}\) c. If \(A=Q R,\) where \(Q\) has orthonormal columns, then \(R=Q^{T} A\)

Find a formula for the least-squares solution of \(A \mathbf{x}=\mathbf{b}\) when the columns of \(A\) are orthonormal.

Given \(a \geq 0\) and \(b \geq 0,\) let \(\mathbf{u}=\left[\begin{array}{c}{\sqrt{a}} \\ {\sqrt{b}}\end{array}\right]\) and \(\mathbf{v}=\left[\begin{array}{c}{\sqrt{b}} \\\ {\sqrt{a}}\end{array}\right]\) Use the Cauchy-Schwarz inequality to compare the geometric mean \(\sqrt{a b}\) with the arithmetic mean \((a+b) / 2\)

[M] For a matrix program, the Gram-Schmidt process works better with orthonormal vectors. Starting with \(\mathbf{x}_{1}, \ldots, \mathbf{x}_{p}\) as in Theorem \(11,\) let \(A=\left[\mathbf{x}_{1} \quad \cdots \quad x_{p}\right] .\) Suppose \(Q\) is an \(n \times k\) matrix whose columns form an orthonormal basis for the subspace \(W_{k}\) spanned by the first \(k\) columns of \(A .\) Then for \(\mathbf{x}\) in \(\mathbb{R}^{n}, Q Q^{T} \mathbf{x}\) is the orthogonal projection of \(\mathbf{x}\) onto \(W_{k}\) (Theorem 10 in Section 6.3\() .\) If \(\mathbf{x}_{k+1}\) is the next column of \(A\) then equation \((2)\) in the proof of Theorem 11 becomes $$ \mathbf{v}_{k+1}=\mathbf{x}_{k+1}-Q\left(Q^{T} \mathbf{x}_{k+1}\right) $$

Use the inner product axioms and other results of this section to verify the statements in Exercises \(15-18\) . \(\|\mathbf{u}+\mathbf{v}\|^{2}+\|\mathbf{u}-\mathbf{v}\|^{2}=2\|\mathbf{u}\|^{2}+2\|\mathbf{v}\|^{2}\)

See all solutions

Recommended explanations on Math Textbooks

View all explanations

What do you think about this solution?

We value your feedback to improve our textbook solutions.

Study anywhere. Anytime. Across all devices.