/*! 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 6 Consider the following \(3 \time... [FREE SOLUTION] | 91影视

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Consider the following \(3 \times 3\) matrix \(A\) and basis \(S\) of \(\mathbf{R}^{3}\) : \\[A=\left[\begin{array}{rrr}1 & -2 & 1 \\\3 & -1 & 0 \\\1 & 4 & -2 \end{array}\right] \quad \text { and } \quad S=\left\\{u_{1}, u_{2}, u_{3}\right\\}=\left\\{\left[\begin{array}{l}1 \\\1 \\\1 \end{array}\right], \quad\left[\begin{array}{l}0 \\\1 \\ 1\end{array}\right], \quad\left[\begin{array}{l}1 \\\2 \\\3\end{array}\right]\right\\}\\]

Short Answer

Expert verified
The transformed basis vectors using the matrix A are: \[S'=\left\\{Au_{1}, Au_{2}, Au_{3}\right\\}=\left\\{\left[\begin{array}{l}0 \\\3 \\\-1 \end{array}\right], \left[\begin{array}{r}1 \\\-1 \\\3 \end{array}\right], \left[\begin{array}{c}2 \\\-1 \\\4 \end{array}\right]\right\\}\]

Step by step solution

01

Define the given matrix A and basis vectors u鈧, u鈧, and u鈧

Matrix A is given as: \[A=\left[\begin{array}{rrr}1 & -2 & 1 \\\3 & -1 & 0 \\\1 & 4 & -2 \end{array}\right]\] Basis S is given as: \[S = \left\\{u_{1}, u_{2}, u_{3}\right\\}=\left\\{\left[\begin{array}{l}1 \\\1 \\\1 \end{array}\right],\left[\begin{array}{l}0\\\1\\\ 1\end{array}\right],\left[\begin{array}{l}1 \\\2 \\\3\end{array}\right]\right\\}\]
02

Carry out matrix-vector multiplication of A with u鈧, u鈧, and u鈧

To find the transformation of the basis vectors using the matrix A, we need to carry out the multiplication of A with each of the basis vectors. Let's find the transformed vector for u鈧: \[Au_{1}=\left[\begin{array}{rrr}1 & -2 & 1 \\\3 & -1 & 0 \\\1 & 4 & -2 \end{array}\right]\left[\begin{array}{l}1 \\\1 \\\1 \end{array}\right]\] Let's find the transformed vector for u鈧: \[Au_{2}=\left[\begin{array}{rrr}1 & -2 & 1 \\\3 & -1 & 0 \\\1 & 4 & -2 \end{array}\right]\left[\begin{array}{l}0\\\1\\\ 1\end{array}\right]\] Let's find the transformed vector for u鈧: \[Au_{3}=\left[\begin{array}{rrr}1 & -2 & 1 \\\3 & -1 & 0 \\\1 & 4 & -2 \end{array}\right]\left[\begin{array}{l}1 \\\2 \\\3\end{array}\right]\]
03

Calculate the transformed vectors using matrix-vector multiplication results

For u鈧: \[Au_{1}=\left[\begin{array}{c}0 \\\3 \\\-1 \end{array}\right]\] For u鈧: \[Au_{2}=\left[\begin{array}{c}1 \\\-1 \\\3 \end{array}\right]\] For u鈧: \[Au_{3}=\left[\begin{array}{c}2 \\\-1 \\\4 \end{array}\right]\] Thus, the transformed basis vectors are: \[S'=\left\\{Au_{1}, Au_{2}, Au_{3}\right\\}=\left\\{\left[\begin{array}{l}0 \\\3 \\\-1 \end{array}\right], \left[\begin{array}{r}1 \\\-1 \\\3 \end{array}\right], \left[\begin{array}{c}2 \\\-1 \\\4 \end{array}\right]\right\\}\]

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Key Concepts

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

Matrix-Vector Multiplication
Matrix-vector multiplication is a process where a matrix transforms a vector into another vector. This involves a linear combination of the columns of the matrix, weighted by the corresponding entries of the vector. It is essential in linear algebra for solving systems of linear equations and understanding transformations.

To multiply a matrix by a vector, you perform the dot product of each row of the matrix with the vector. For example, consider a matrix \(A\) and vector \(v\). You multiply each element of a row in \(A\) by the corresponding element in \(v\) and then sum the results. The expression \(Av\) leads to a new vector.

Consider \(A\) as a \(3 \times 3\) matrix and the vector \(u_1 = \begin{bmatrix} 1 \ 1 \ 1 \end{bmatrix}\):
\[A = \begin{bmatrix} 1 & -2 & 1 \ 3 & -1 & 0 \ 1 & 4 & -2 \end{bmatrix} \]
Perform the multiplication to find \(Au_1\):
\[Au_1 = \begin{bmatrix} 0 \ 3 \ -1 \end{bmatrix} \]
This process transforms the original vector through the structure dictated by \(A\). Each of these operations has deep ties to both practical computations and theoretical insights.
Basis in Linear Algebra
In linear algebra, a basis is a set of vectors that are linearly independent and span the vector space. This forms the foundation for building other vectors in the space, essentially providing a dimension-specific coordinate system.

Every vector in a vector space can be uniquely represented as a linear combination of the basis vectors. For instance, consider the basis \(S\) provided in the exercise:
\[S = \left\{ u_1, u_2, u_3 \right\} = \left\{ \begin{bmatrix} 1 \ 1 \ 1 \end{bmatrix}, \begin{bmatrix} 0 \ 1 \ 1 \end{bmatrix}, \begin{bmatrix} 1 \ 2 \ 3 \end{bmatrix} \right\}\]
These vectors are linearly independent, meaning no vector is a linear combination of the others. This ensures the set spans the entire space \(\mathbf{R}^3\).

Understanding a basis and its properties is crucial for various applications, including:
  • Transformations within vector spaces
  • Changing coordinates
  • Diagonalization of matrices
Each basis provides a unique way to express and manipulate vectors, often simplifying complex operations and calculations.
Linear Transformations
Linear transformations are functions between vector spaces that preserve the operations of vector addition and scalar multiplication. They are significant in translating geometrical transformations such as rotations, reflections, and scalings into algebraic forms.

Matrix representation of linear transformations makes them easy to work with. Any linear transformation from \(\mathbf{R}^n\) to \(\mathbf{R}^m\) can be expressed as a matrix multiplication \(T(x) = Ax\), where \(A\) is an \(m \times n\) matrix and \(x\) is a vector.

For the given matrix \(A\) in the exercise, the transformation when applied to basis vectors alters their orientation or magnitude as follows:
\[Au_1 = \begin{bmatrix} 0 \ 3 \ -1 \end{bmatrix}, \, Au_2 = \begin{bmatrix} 1 \ -1 \ 3 \end{bmatrix}, \, Au_3 = \begin{bmatrix} 2 \ -1 \ 4 \end{bmatrix} \]
This shows how \(A\) acts to transform each vector. By applying \(A\) to all vectors in a basis, one can observe the complete effect of a transformation on the vector space. Linear transformations maintain the structure of vector spaces, making them a powerful tool in both theoretical and applied mathematics.

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Most popular questions from this chapter

For each linear transformation \(L\) on \(\mathbf{R}^{2}\), find the matrix \(A\) representing \(L\) (relative to the usual basis of \(\mathbf{R}^{2}\) ): (a) \(L\) is the rotation in \(\mathbf{R}^{2}\) counterclockwise by \(45^{\circ}\). (b) \(L\) is the reflection in \(\mathbf{R}^{2}\) about the line \(y=x\). (c) \(L\) is defined by \(L(1,0)=(3,5)\) and \(L(0,1)=(7,-2)\). (d) \(L\) is defined by \(L(1,1)=(3,7)\) and \(L(1,2)=(5,-4)\).

Suppose \(V=U+W\), and suppose \(U\) and \(V\) are each invariant under a linear operator \(F: V \rightarrow V\). Also, suppose dim \(U=r\) and \(\operatorname{dim} W=S\). Show that \(F\) has a block diagonal matrix representation \(M=\left[\begin{array}{ll}A & 0 \\ 0 & B\end{array}\right]\) where \(A\) is an \(r \times r\) submatrix.

For each of the following linear transformations (operators) \(L\) on \(\mathbf{R}^{2},\) find the matrix \(A\) that represents \(L\) (relative to the usual basis of \(\mathbf{R}^{2}\) ): (a) \(L\) is defined by \(L(1,0)=(2,4)\) and \(L(0,1)=(5,8)\) (b) \(L\) is the rotation in \(\mathbf{R}^{2}\) counterclockwise by \(90^{\circ}\) (c) \(L\) is the reflection in \(\mathbf{R}^{2}\) about the line \(y=-x\)

Consider the following bases of \(\mathbf{R}^{2}\) : $$ S=\left\\{u_{1}, u_{2}\right\\}=\\{(1,-2),(3,-4)\\} \quad \text { and } \quad S^{\prime}=\left\\{v_{1}, v_{2}\right\\}=\\{(1,3),(3,8)\\} $$ (a) Find the coordinates of \(v=(a, b)\) relative to the basis \(S\). (b) Find the change-of-basis matrix \(P\) from \(S\) to \(S^{\prime}\). (c) Find the coordinates of \(v=(a, b)\) relative to the basis \(S^{\prime}\). (d) Find the change-of-basis matrix \(Q\) from \(S^{\prime}\) back to \(S\). (e) Verify \(Q=P^{-1}\). (f) Show that, for any vector \(v=(a, b)\) in \(\mathbf{R}^{2}, P^{-1}[v]_{S}=[v]_{S^{\prime}}\). (See Theorem 6.6.) (a) Let \(v=x u_{1}+y u_{2}\) for unknowns \(x\) and \(y\); that is, $$ \left[\begin{array}{l} a \\ b \end{array}\right]=x\left[\begin{array}{r} 1 \\ -2 \end{array}\right]+y\left[\begin{array}{r} 3 \\ -4 \end{array}\right] \quad \text { or } \begin{aligned} x+3 y=a \\ -2 x-4 y=b \end{aligned} \quad \text { or } \quad \begin{aligned} x+3 y &=a \\ 2 y &=2 a+b \end{aligned} $$ Solve for \(x\) and \(y\) in terms of \(a\) and \(b\) to get \(x=-2 a-\frac{3}{2} b\) and \(y=a+\frac{1}{2} b\). Thus, $$ (a, b)=\left(-2 a-\frac{3}{2}\right) u_{1}+\left(a+\frac{1}{2} b\right) u_{2} \quad \text { or } \quad[(a, b)]_{S}=\left[-2 a-\frac{3}{2} b, a+\frac{1}{2} b\right]^{T} $$ (b) Use part (a) to write each of the basis vectors \(v_{1}\) and \(v_{2}\) of \(S^{\prime}\) as a linear combination of the basis vectors \(u_{1}\) and \(u_{2}\) of \(S\); that is, $$ \begin{aligned} &v_{1}=(1,3)=\left(-2-\frac{9}{2}\right) u_{1}+\left(1+\frac{3}{2}\right) u_{2}=-\frac{13}{2} u_{1}+\frac{5}{2} u_{2} \\ &v_{2}=(3,8)=(-6-12) u_{1}+(3+4) u_{2}=-18 u_{1}+7 u_{2} \end{aligned} $$ Then \(P\) is the matrix whose columns are the coordinates of \(v_{1}\) and \(v_{2}\) relative to the basis \(S ;\) that is, $$ P=\left[\begin{array}{rr} -\frac{13}{2} & -18 \\ \frac{5}{2} & 7 \end{array}\right] $$ (c) Let \(v=x v_{1}+y v_{2}\) for unknown scalars \(x\) and \(y\) : $$ \left[\begin{array}{l} a \\ b \end{array}\right]=x\left[\begin{array}{l} 1 \\ 3 \end{array}\right]+y\left[\begin{array}{l} 3 \\ 8 \end{array}\right] \quad \text { or } \quad \begin{array}{r} x+3 y=a \\ 3 x+8 y=b \end{array} \quad \text { or } \quad \begin{array}{r} x+3 y=a \\ -y=b-3 a \end{array} $$ Solve for \(x\) and \(y\) to get \(x=-8 a+3 b\) and \(y=3 a-b\). Thus, $$ (a, b)=(-8 a+3 b) v_{1}+(3 a-b) v_{2} \quad \text { or } \quad[(a, b)]_{S^{\prime}}=[-8 a+3 b, \quad 3 a-b]^{T} $$ (d) Use part ( \(c)\) to express each of the basis vectors \(u_{1}\) and \(u_{2}\) of \(S\) as a linear combination of the basis vectors \(v_{1}\) and \(v_{2}\) of \(S^{\prime}\) $$ \begin{aligned} &u_{1}=(1,-2)=(-8-6) v_{1}+(3+2) v_{2}=-14 v_{1}+5 v_{2} \\ &u_{2}=(3,-4)=(-24-12) v_{1}+(9+4) v_{2}=-36 v_{1}+13 v_{2} \end{aligned} $$ Write the coordinates of \(u_{1}\) and \(u_{2}\) relative to \(S^{\prime}\) as columns to obtain \(Q=\left[\begin{array}{rr}-14 & -36 \\ 5 & 13\end{array}\right]\). (e) \(Q P=\left[\begin{array}{rr}-14 & -36 \\ 5 & 13\end{array}\right]\left[\begin{array}{rr}-\frac{13}{2} & -18 \\ \frac{5}{2} & 7\end{array}\right]=\left[\begin{array}{ll}1 & 0 \\ 0 & 1\end{array}\right]=I\) (f) Use parts \((\mathrm{a}),(\mathrm{c})\), and (d) to obtain $$ P^{-1}[v]_{S}=Q[v]_{S}=\left[\begin{array}{rr} -14 & -36 \\ 5 & 13 \end{array}\right]\left[\begin{array}{r} -2 a-\frac{3}{2} b \\ a+\frac{1}{2} b \end{array}\right]=\left[\begin{array}{c} -8 a+3 b \\ 3 a-b \end{array}\right]=[v]_{S} $$

Let \(A=\left[\begin{array}{rr}1 & 1 \\ 2 & -3\end{array}\right]\) and \(P=\left[\begin{array}{ll}1 & -2 \\ 3 & -5\end{array}\right]\) (a) Find \(B=P^{-1} A P\). (b) Verify that \(\operatorname{tr}(B)=\operatorname{tr}(A)\). (c) Verify that \(\operatorname{det}(B)=\operatorname{det}(A)\).

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