(calculus required) Define \(T:C\left( {0,1} \right) \to C\left( {0,1} \right)\) as follows: For f in \(C\left( {0,1} \right)\), let \(T\left( t \right)\) be the antiderivative \({\mathop{\rm F}\nolimits} \) of \({\mathop{\rm f}\nolimits} \) such that \({\mathop{\rm F}\nolimits} \left( 0 \right) = 0\). Show that \(T\) is a linear transformation, and describe the kernel of \(T\). (See the notation in Exercise 20 of Section 4.1.)

Short Answer

Expert verified

It is proved that \(T\) is a linear transformation. The kernel of \(T\) is \(\left\{ 0 \right\}\).

Step by step solution

01

State the condition for linear transformation

The conditions for linear transformationTare as follows:

1.\(T\left( {{\mathop{\rm u}\nolimits} + {\mathop{\rm v}\nolimits} } \right) = T\left( {\mathop{\rm u}\nolimits} \right) + T\left( {\mathop{\rm v}\nolimits} \right)\) for all \({\mathop{\rm u}\nolimits} ,{\mathop{\rm v}\nolimits} \,\,{\mathop{\rm in}\nolimits} \,\,V\), and

2 \(T\left( {c{\mathop{\rm u}\nolimits} } \right) = cT\left( {\mathop{\rm u}\nolimits} \right)\) for all \({\mathop{\rm u}\nolimits} \,\,\,{\mathop{\rm in}\nolimits} \,\,V\) and all scalar \(c\).

02

Show that \(T\) is a linear transformation

represents the antiderivative \({\mathop{\rm F}\nolimits} \) of \({\mathop{\rm f}\nolimits} \) with \({\mathop{\rm F}\nolimits} \left( 0 \right) = 0\), and \(T\left( {\mathop{\rm g}\nolimits} \right)\) represents the antiderivative \({\mathop{\rm G}\nolimits} \) of \({\mathop{\rm g}\nolimits} \) with \({\mathop{\rm G}\nolimits} \left( 0 \right) = 0\).

According to the rules of antidifferentiation, \({\mathop{\rm F}\nolimits} + G\) is the antiderivative of \({\mathop{\rm f}\nolimits} + {\mathop{\rm g}\nolimits} \). Then,

\(\begin{array}{c}\left( {{\mathop{\rm F}\nolimits} + G} \right)\left( 0 \right) = \left( {\mathop{\rm F}\nolimits} \right)\left( 0 \right) + \left( G \right)\left( 0 \right)\\ = 0 + 0\\ = 0\end{array}\)

Therefore, \(T\left( {{\mathop{\rm f}\nolimits} + g} \right) = T\left( {\mathop{\rm f}\nolimits} \right) + T\left( g \right)\).

Similarly, \(c{\mathop{\rm F}\nolimits} \) is an antiderivative of \({\mathop{\rm cf}\nolimits} \).

\(\begin{array}{c}\left( {c{\mathop{\rm F}\nolimits} } \right)\left( 0 \right) = c{\mathop{\rm F}\nolimits} \left( 0 \right)\\ = c0\\ = 0\end{array}\)

Therefore, \(T\left( {c{\mathop{\rm f}\nolimits} } \right) = cT\left( {\mathop{\rm f}\nolimits} \right)\), and \(T\) is a linear transformation.

Thus, it is proved that \(T\) is a linear transformation.

03

Describe the kernel of \(T\)

To determine the kernel of \(T\), find all functions \({\mathop{\rm f}\nolimits} \) in \(C\left( {0,1} \right)\) with the antiderivative equal to the zero function. The zero function (0) is the only function that has this property.

Therefore, the kernel of \(T\) is \(\left\{ 0 \right\}\).

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Most popular questions from this chapter

(M) Determine whether w is in the column space of \(A\), the null space of \(A\), or both, where

\({\mathop{\rm w}\nolimits} = \left( {\begin{array}{*{20}{c}}1\\1\\{ - 1}\\{ - 3}\end{array}} \right),A = \left( {\begin{array}{*{20}{c}}7&6&{ - 4}&1\\{ - 5}&{ - 1}&0&{ - 2}\\9&{ - 11}&7&{ - 3}\\{19}&{ - 9}&7&1\end{array}} \right)\)

Consider the polynomials \({{\bf{p}}_{\bf{1}}}\left( t \right) = {\bf{1}} + t\), \({{\bf{p}}_{\bf{2}}}\left( t \right) = {\bf{1}} - t\), \({{\bf{p}}_{\bf{3}}}\left( t \right) = {\bf{4}}\), \({{\bf{p}}_{\bf{4}}}\left( t \right) = {\bf{1}} + {t^{\bf{2}}}\), and \({{\bf{p}}_{\bf{5}}}\left( t \right) = {\bf{1}} + {\bf{2}}t + {t^{\bf{2}}}\), and let H be the subspace of \({P_{\bf{5}}}\) spanned by the set \(S = \left\{ {{{\bf{p}}_{\bf{1}}},\,{{\bf{p}}_{\bf{2}}},\;{{\bf{p}}_{\bf{3}}},\,{{\bf{p}}_{\bf{4}}},\,{{\bf{p}}_{\bf{5}}}} \right\}\). Use the method described in the proof of the Spanning Set Theorem (Section 4.3) to produce a basis for H. (Explain how to select appropriate members of S.)

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

  1. Show that if \(B\) is \(n \times p\), then rank\(AB \le {\mathop{\rm rank}\nolimits} A\). (Hint: Explain why every vector in the column space of \(AB\) is in the column space of \(A\).
  2. Show that if \(B\) is \(n \times p\), then rank\(AB \le {\mathop{\rm rank}\nolimits} B\). (Hint: Use part (a) to study rank\({\left( {AB} \right)^T}\).)

Given \(T:V \to W\) as in Exercise 35, and given a subspace \(Z\) of \(W\), let \(U\) be the set of all \({\mathop{\rm x}\nolimits} \) in \(V\) such that \(T\left( {\mathop{\rm x}\nolimits} \right)\) is in \(Z\). Show that \(U\) is a subspace of \(V\).

If A is a \({\bf{6}} \times {\bf{4}}\) matrix, what is the smallest possible dimension of Null A?

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