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Let\(T:{{\rm P}_2} \to {{\rm P}_3}\) be a linear transformation that maps a polynomial \({\bf{p}}\left( t \right)\) into the polynomial \(\left( {t + 5} \right){\bf{p}}\left( t \right)\).

  1. Find the image of\({\bf{p}}\left( t \right) = 2 - t + {t^2}\).
  2. Show that \(T\) is a linear transformation.
  3. Find the matrix for \(T\) relative to the bases \(\left\{ {1,t,{t^2}} \right\}\) and \(\left\{ {1,t,{t^2},{t^3}} \right\}\).

Short Answer

Expert verified
  1. The image of \({\bf{p}}\left( t \right) = 2 - t + {t^2}\) is \(10 - 3t + 4{t^2} + {t^3}\).
  1. It is verified that \(T\) is a linear transformation as both the properties of the transformation are satisfied.
  1. The matrix for \(T\) relative to \(\left\{ {1,t,{t^2}} \right\}\) and \(\left\{ {1,t,{t^2},{t^3}} \right\}\)is \(\left( {\begin{aligned}5&{}&0&{}&0\\1&{}&5&{}&0\\0&{}&1&{}&5\\0&{}&0&{}&1\end{aligned}} \right)\).

Step by step solution

01

Find the image of \({\bf{p}}\left( t \right) = 2 - t + {t^2}\) 

(a)

It is given that the image of \({\bf{p}}\left( t \right)\) is given by \(T\left( {{\bf{p}}\left( t \right)} \right) = \left( {t + 5} \right){\bf{p}}\left( t \right)\).

Obtain the image \({\bf{p}}\left( t \right) = 2 - t + {t^2}\) by using \(T\left( {{\bf{p}}\left( t \right)} \right) = \left( {t + 5} \right){\bf{p}}\left( t \right)\).

\(\begin{aligned}T\left( {{\bf{p}}\left( t \right)} \right) = T\left( {2 - t + {t^2}} \right)\\ = \left( {t + 5} \right)\left( {2 - t + {t^2}} \right)\\ = 2t - {t^2} + {t^3} + 10 - 5t + 5{t^2}\\ = 10 - 3t + 4{t^2} + {t^3}\end{aligned}\)

So, the image of \({\bf{p}}\left( t \right) = 2 - t + {t^2}\) is \(10 - 3t + 4{t^2} + {t^3}\).

02

Linear transformation

Let \(T:{\mathbb{R}^n} \to {\mathbb{R}^n}\) be a transformation; this transformation is said to be a linear transformation if it satisfies the following two properties:

  1. \(T\left( {u + v} \right) = T\left( u \right) + T\left( v \right)\)
  2. \(T\left( {cu} \right) = cT\left( u \right)\)

Here,\(c\) is any scalar and \(u,v\) are vectors.

03

Check if \(T\) is a linear transformation

(b)

Let there be two polynomials, \({\bf{p}}\left( t \right)\) and \({\bf{q}}\left( t \right)\) in \({{\rm P}_2}\), then, their images will be \(T\left( {{\bf{p}}\left( t \right)} \right) = \left( {t + 5} \right){\bf{p}}\left( t \right)\) and \(T\left( {q\left( t \right)} \right) = \left( {t + 5} \right)q\left( t \right)\) , respectively.

Check for the first property.

\(\begin{aligned}T\left( {{\bf{p}}\left( t \right) + {\bf{q}}\left( t \right)} \right) &= \left( {t + 5} \right)\left( {{\bf{p}}\left( t \right) + {\bf{q}}\left( t \right)} \right)\\ &= \left( {t + 5} \right){\bf{p}}\left( t \right) + \left( {t + 5} \right){\bf{q}}\left( t \right)\\ &= T\left( {{\bf{p}}\left( t \right)} \right) + T\left( {{\bf{q}}\left( t \right)} \right)\end{aligned}\)

The first property is satisfied; check for the second property. Let \(c\) be any scaler.

\(\begin{aligned}T\left( {c \cdot {\bf{p}}\left( t \right)} \right) &= \left( {t + 5} \right)\left( {c \cdot {\bf{p}}\left( t \right)} \right)\\ &= c \cdot \left( {t + 5} \right){\bf{p}}\left( t \right)\\ &= c \cdot T\left( {{\bf{p}}\left( t \right)} \right)\end{aligned}\)

As both the properties of a linear transformation are satisfied, so \(T\) is a linear transformation.

04

The matrix for a linear transformation

A matrix associated with a linear transformation \(T\) for \(V\) and \(W\) is given by \({\left( {T\left( {\bf{x}} \right)} \right)_C} = \left( {\begin{aligned}{{{\left( {T\left( {{{\bf{b}}_1}} \right)} \right)}_C}}&{{{\left( {T\left( {{{\bf{b}}_2}} \right)} \right)}_C}}& \cdots &{{{\left( {T\left( {{{\bf{b}}_n}} \right)} \right)}_C}}\end{aligned}} \right)\), where \(V\) and \(W\) are \(n\) and \(m\)-dimensional subspaces respectively, and \(B\), and\(C\) are the bases for \(V\), and \(W\).

05

: Find the matrix for a linear transformation 

Let \(B = \left\{ {1,t,{t^2}} \right\}\), and\(C = \left\{ {1,t,{t^2},{t^3}} \right\}\).

Find \(T\left( {{{\bf{b}}_1}} \right)\), \(T\left( {{{\bf{b}}_2}} \right)\) and \(T\left( {{{\bf{b}}_3}} \right)\) for \(B = \left\{ {1,t,{t^2}} \right\}\) by using \(T\left( {{\bf{p}}\left( t \right)} \right) = \left( {t + 5} \right){\bf{p}}\left( t \right)\).

\(\begin{aligned}T\left( {{{\bf{b}}_1}} \right) &= T\left( 1 \right)\\ &= \left( {t + 5} \right)\left( 1 \right)\\ &= t + 5\end{aligned}\)

\(\begin{aligned}T\left( {{{\bf{b}}_2}} \right) &= T\left( t \right)\\ &= \left( {t + 5} \right)\left( t \right)\\ &= {t^2} + 5t\end{aligned}\)

\(\begin{aligned}T\left( {{{\bf{b}}_3}} \right) &= T\left( {{t^2}} \right)\\ &= \left( {t + 5} \right)\left( {{t^2}} \right)\\ &= {t^3} + 5{t^2}\end{aligned}\)

Write\({\left( {T\left( {{{\bf{b}}_1}} \right)} \right)_C}\), \({\left( {T\left( {{{\bf{b}}_2}} \right)} \right)_C}\) and \({\left( {T\left( {{{\bf{b}}_3}} \right)} \right)_C}\).

\({\left( {T\left( {{{\bf{b}}_1}} \right)} \right)_C} = \left( {\begin{aligned}5\\1\\0\\0\end{aligned}} \right)\), \({\left( {T\left( {{{\bf{b}}_2}} \right)} \right)_C} = \left( {\begin{aligned}0\\5\\1\\0\end{aligned}} \right)\), \({\left( {T\left( {{{\bf{b}}_3}} \right)} \right)_C} = \left( {\begin{aligned}0\\0\\5\\1\end{aligned}} \right)\)

Form a matrix \(T\) for the obtained vectors by using the formula \({\left( {T\left( {\bf{x}} \right)} \right)_C} = \left( {\begin{aligned}{{{\left( {T\left( {{{\bf{b}}_1}} \right)} \right)}_C}}&{{{\left( {T\left( {{{\bf{b}}_2}} \right)} \right)}_C}}& \cdots &{{{\left( {T\left( {{{\bf{b}}_n}} \right)} \right)}_C}}\end{aligned}} \right)\), where \(n = 3\).

\(\begin{aligned}{c}{\left( {T\left( {\bf{x}} \right)} \right)_C} = \left( {\begin{aligned}{{{\left( {T\left( {{{\bf{b}}_1}} \right)} \right)}_C}}&{{{\left( {T\left( {{{\bf{b}}_2}} \right)} \right)}_C}}&{{{\left( {T\left( {{{\bf{b}}_3}} \right)} \right)}_C}}\end{aligned}} \right)\\ = \left( {\begin{aligned}5&{}&0&{}&0\\1&{}&5&{}&0\\0&{}&1&{}&5\\0&{}&0&{}&1\end{aligned}} \right)\end{aligned}\)

So, the required matrix is \(\left( {\begin{aligned}5&{}&0&{}&0\\1&{}&5&{}&0\\0&{}&1&{}&5\\0&{}&0&{}&1\end{aligned}} \right)\).

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Most popular questions from this chapter

Exercises 19โ€“23 concern the polynomial \(p\left( t \right) = {a_{\bf{0}}} + {a_{\bf{1}}}t + ... + {a_{n - {\bf{1}}}}{t^{n - {\bf{1}}}} + {t^n}\) and \(n \times n\) matrix \({C_p}\) called the companion matrix of \(p\): \({C_p} = \left( {\begin{aligned}{*{20}{c}}{\bf{0}}&{\bf{1}}&{\bf{0}}&{...}&{\bf{0}}\\{\bf{0}}&{\bf{0}}&{\bf{1}}&{}&{\bf{0}}\\:&{}&{}&{}&:\\{\bf{0}}&{\bf{0}}&{\bf{0}}&{}&{\bf{1}}\\{ - {a_{\bf{0}}}}&{ - {a_{\bf{1}}}}&{ - {a_{\bf{2}}}}&{...}&{ - {a_{n - {\bf{1}}}}}\end{aligned}} \right)\).

21. Use mathematical induction to prove that for \(n \ge {\bf{2}}\),\(\begin{aligned}{c}det\left( {{C_p} - \lambda I} \right) = {\left( { - {\bf{1}}} \right)^n}\left( {{a_{\bf{0}}} + {a_{\bf{1}}}\lambda + ... + {a_{n - {\bf{1}}}}{\lambda ^{n - {\bf{1}}}} + {\lambda ^n}} \right)\\ = {\left( { - {\bf{1}}} \right)^n}p\left( \lambda \right)\end{aligned}\)

(Hint: Expanding by cofactors down the first column, show that \(det\left( {{C_p} - \lambda I} \right)\) has the form \(\left( { - \lambda B} \right) + {\left( { - {\bf{1}}} \right)^n}{a_{\bf{0}}}\) where \(B\) is a certain polynomial (by the induction assumption).)

Use Exercise 12 to find the eigenvalues of the matrices in Exercises 13 and 14.

13. \(A = \left( {\begin{array}{*{20}{c}}3&{ - 2}&8\\0&5&{ - 2}\\0&{ - 4}&3\end{array}} \right)\)

Question: Is \(\left( {\begin{array}{*{20}{c}}1\\{ - 2}\\1\end{array}} \right)\) an eigenvector of\(\left){\begin{array}{*{20}{c}}3&6&7\\3&3&7\\5&6&5\end{array}} \right)\)? If so, find the eigenvalue.

A common misconception is that if \(A\) has a strictly dominant eigenvalue, then, for any sufficiently large value of \(k\), the vector \({A^k}{\bf{x}}\) is approximately equal to an eigenvector of \(A\). For the three matrices below, study what happens to \({A^k}{\bf{x}}\) when \({\bf{x = }}\left( {{\bf{.5,}}{\bf{.5}}} \right)\), and try to draw general conclusions (for a \({\bf{2 \times 2}}\) matrix).

a. \(A{\bf{ = }}\left( {\begin{aligned}{ {20}{c}}{{\bf{.8}}}&{\bf{0}}\\{\bf{0}}&{{\bf{.2}}}\end{aligned}} \right)\) b. \(A{\bf{ = }}\left( {\begin{aligned}{ {20}{c}}{\bf{1}}&{\bf{0}}\\{\bf{0}}&{{\bf{.8}}}\end{aligned}} \right)\) c. \(A{\bf{ = }}\left( {\begin{aligned}{ {20}{c}}{\bf{8}}&{\bf{0}}\\{\bf{0}}&{\bf{2}}\end{aligned}} \right)\)

Question: In Exercises \({\bf{3}}\) and \({\bf{4}}\), use the factorization \(A = PD{P^{ - {\bf{1}}}}\) to compute \({A^k}\) where \(k\) represents an arbitrary positive integer.

3. \(\left( {\begin{array}{*{20}{c}}a&0\\{3\left( {a - b} \right)}&b\end{array}} \right) = \left( {\begin{array}{*{20}{c}}1&0\\3&1\end{array}} \right)\left( {\begin{array}{*{20}{c}}a&0\\0&b\end{array}} \right)\left( {\begin{array}{*{20}{c}}1&0\\{ - 3}&1\end{array}} \right)\)

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