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In Exercises 23 and 24, mark each statement True or False. Justify each answer.

24.

a. Not every linear transformation from\({\mathbb{R}^n}\)to\({\mathbb{R}^m}\)is a matrix transformation.

b. The columns of the standard matrix for a linear transformation from\({\mathbb{R}^n}\)to\({\mathbb{R}^m}\)are the images of the columns of the\(n \times n\)identity matrix.

c. The standard matrix of a linear transformation from\({\mathbb{R}^2}\)to\({\mathbb{R}^2}\)that reflects points through the horizontal axis, the vertical axis, or the origin has the form\(\left[ {\begin{array}{*{20}{c}}a&0\\0&d\end{array}} \right]\), where\(a\)and\(d\)are\( \pm 1\).

d. A mapping\(T:{\mathbb{R}^n} \to {\mathbb{R}^m}\)is one-to-one if each vector in\({\mathbb{R}^n}\)maps onto a unique vector in\({\mathbb{R}^m}\).

e. If\(A\)is a\(3 \times 2\)matrix, then the transformation\(x \mapsto Ax\)cannot map\({\mathbb{R}^2}\)onto\({\mathbb{R}^3}\).

Short Answer

Expert verified

a. The given statement is false.

b. The given statement is true.

c. The given statement is true.

d. The given statement is false.

e. The given statement is true.

Step by step solution

01

Determine whether the given statement is true or false

(a)

Every linear transformation from\({\mathbb{R}^n}\)to\({\mathbb{R}^m}\)can be viewed as a matrix transformation.

Thus, the given statement (a) is false.

02

Determine whether the given statement is true or false

(b)

Theorem 10 states that let\(T:{\mathbb{R}^n} \to {\mathbb{R}^m}\)be a linear transformation, then there exists a unique matrix\(A\)such that\(T\left( x \right) = Ax\)for all\(x\)in\({\mathbb{R}^n}\). \(A\)is the\(m \times n\)matrix whose\[j{\mathop{\rm th}\nolimits} \]column of the identity matrix in\({\mathbb{R}^n}\):\(A = \left[ {\begin{array}{*{20}{c}}{T\left( {{e_1}} \right)}&{...}&{T\left( {{e_n}} \right)}\end{array}} \right]\).

Thus, the given statement (b) is true.

03

Determine whether the given statement is true or false

(c)

The standard matrix of a linear transformation from\({\mathbb{R}^2}\)to\({\mathbb{R}^2}\)that reflects points through the horizontal axis, the vertical axis or the origin has the form\(\left[ {\begin{array}{*{20}{c}}1&0\\0&1\end{array}} \right]\).

Thus, the given statement (c) is true.

04

Determine whether the given statement is true or false

(d)

A transformation\(T:{\mathbb{R}^n} \to {\mathbb{R}^m}\)is said to be one-to-one\({\mathbb{R}^m}\)if each\[{\mathop{\rm b}\nolimits} \]in\({\mathbb{R}^m}\)is the image of at most one\(x\)in\({\mathbb{R}^n}\).

Thus, the given statement (d) is false.

05

Determine whether the given statement is true or false

(e)

Consider that\(A\)is a\(3 \times 2\)matrix; the columns of\(A\)span\({\mathbb{R}^3}\)if and only if\(A\)has 3 pivot columns. Since\(A\)has only two columns, so the columns of\(A\)do not span\({\mathbb{R}^3}\), and the linear transformation does not map\({\mathbb{R}^2}\)onto\({\mathbb{R}^3}\).

Thus, the given statement (e) is true.

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Most popular questions from this chapter

[M] Let \(A = \left[ {\begin{array}{*{20}{c}}7&{ - 9}&{ - 4}&5&3&{ - 3}&{ - 7}\\{ - 4}&6&7&{ - 2}&{ - 6}&{ - 5}&5\\5&{ - 7}&{ - 6}&5&{ - 6}&2&8\\{ - 3}&5&8&{ - 1}&{ - 7}&{ - 4}&8\\6&{ - 8}&{ - 5}&4&4&9&3\end{array}} \right]\).

  1. Construct matrices \(C\) and \(N\) whose columns are bases for \({\mathop{\rm Col}\nolimits} A\) and \({\mathop{\rm Nul}\nolimits} A\), respectively, and construct a matrix \(R\) whose rows form a basis for Row\(A\).
  2. Construct a matrix \(M\) whose columns form a basis for \({\mathop{\rm Nul}\nolimits} {A^T}\), form the matrices \(S = \left[ {\begin{array}{*{20}{c}}{{R^T}}&N\end{array}} \right]\) and \(T = \left[ {\begin{array}{*{20}{c}}C&M\end{array}} \right]\), and explain why \(S\) and \(T\) should be square. Verify that both \(S\) and \(T\) are invertible.

Use coordinate vector to test whether the following sets of poynomial span \({{\bf{P}}_{\bf{2}}}\). Justify your conclusions.

a. \({\bf{1}} - {\bf{3}}t + {\bf{5}}{t^{\bf{2}}}\), \( - {\bf{3}} + {\bf{5}}t - {\bf{7}}{t^{\bf{2}}}\), \( - {\bf{4}} + {\bf{5}}t - {\bf{6}}{t^{\bf{2}}}\), \({\bf{1}} - {t^{\bf{2}}}\)

b. \({\bf{5}}t + {t^{\bf{2}}}\), \({\bf{1}} - {\bf{8}}t - {\bf{2}}{t^{\bf{2}}}\), \( - {\bf{3}} + {\bf{4}}t + {\bf{2}}{t^{\bf{2}}}\), \({\bf{2}} - {\bf{3}}t\)

Question: Exercises 12-17 develop properties of rank that are sometimes needed in applications. Assume the matrix \(A\) is \(m \times n\).

16. If \(A\) is an \(m \times n\) matrix of rank\(r\), then a rank factorization of \(A\) is an equation of the form \(A = CR\), where \(C\) is an \(m \times r\) matrix of rank\(r\) and \(R\) is an \(r \times n\) matrix of rank \(r\). Such a factorization always exists (Exercise 38 in Section 4.6). Given any two \(m \times n\) matrices \(A\) and \(B\), use rank factorizations of \(A\) and \(B\) to prove that rank\(\left( {A + B} \right) \le {\mathop{\rm rank}\nolimits} A + {\mathop{\rm rank}\nolimits} B\).

(Hint: Write \(A + B\) as the product of two partitioned matrices.)

Exercises 23-26 concern a vector space V, a basis \(B = \left\{ {{{\bf{b}}_{\bf{1}}},....,{{\bf{b}}_n}\,} \right\}\) and the coordinate mapping \({\bf{x}} \mapsto {\left( {\bf{x}} \right)_B}\).

Show that a subset \(\left\{ {{{\bf{u}}_1},...,{{\bf{u}}_p}} \right\}\) in V is linearly independent if and only if the set of coordinate vectors \(\left\{ {{{\left( {{{\bf{u}}_{\bf{1}}}} \right)}_B},.....,{{\left( {{{\bf{u}}_p}} \right)}_B}} \right\}\) is linearly independent in \({\mathbb{R}^n}\)(Hint: Since the coordinate mapping is one-to-one, the following equations have the same solutions, \({c_{\bf{1}}}\),….,\({c_p}\))

\({c_{\bf{1}}}{{\bf{u}}_{\bf{1}}} + ..... + {c_p}{{\bf{u}}_p} = {\bf{0}}\) The zero vector V

\({\left( {{c_{\bf{1}}}{{\bf{u}}_{\bf{1}}} + ..... + {c_p}{{\bf{u}}_p}} \right)_B} = {\left( {\bf{0}} \right)_B}\) The zero vector in \({\mathbb{R}^n}\)a

In Exercise 17, Ais an \(m \times n\] matrix. Mark each statement True or False. Justify each answer.

17. a. The row space of A is the same as the column space of \({A^T}\].

b. If B is any echelon form of A, and if B has three nonzero rows, then the first three rows of A form a basis for Row A.

c. The dimensions of the row space and the column space of A are the same, even if Ais not square.

d. The sum of the dimensions of the row space and the null space of A equals the number of rows in A.

e. On a computer, row operations can change the apparent rank of a matrix.

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