Let \(a\) and \(b\) be nonzero numbers. Show that the mapping \(T\) defined by \(T\left\{ {{y_k}} \right\} = \left\{ {{w_k}} \right\}\), where \({w_k} = {y_{k + 2}} + a{y_{k + 1}} + b{y_k}\) is a linear transformation from S into S.

Short Answer

Expert verified

It is shown that \(T\left\{ {{y_k}} \right\} = \left\{ {{w_k}} \right\}\)is a linear transformation from S into S.

Step by step solution

01

Define the linearity property

If a vector is linear, then it must follow the distributive property, that is, \(T\left( {\left\{ {{y_k}} \right\} + \left\{ {{z_k}} \right\}} \right) = T\left\{ {{y_k}} \right\} + T\left\{ {{z_k}} \right\}\) and associative property of vectors, that is, \(T\left( {r\left\{ {{y_k}} \right\}} \right) = rT\left\{ {{y_k}} \right\}\). This is true for vectors \(\left\{ {{y_k}} \right\}\) and \(\left\{ {{z_k}} \right\}\) in S and a scalar quantity \(r\).

02

Check the two properties for \(T\) to be linear

As \({w_k} = {y_{k + 2}} + a{y_{k + 1}} + b{y_k}\),

\(\begin{aligned} T\left\{ {{y_k}} \right\} &= \left\{ {{w_k}} \right\}\\ &= {y_{k + 2}} + a{y_{k + 1}} + b{y_k}.\end{aligned}\)

To check the distributive property, replace \(\left\{ {{y_k}} \right\}\)by \(\left\{ {{y_k}} \right\} + \left\{ {{z_k}} \right\}\)and simplify as shown below:

\(\begin{aligned} T\left( {\left\{ {{y_k}} \right\} + \left\{ {{z_k}} \right\}} \right) &= T\left( {{y_k} + {z_k}} \right)\\ &= {y_{k + 2}} + {z_{k + 2}} + a\left( {{y_{k + 1}} + {z_{k + 2}}} \right) + b\left( {{y_k} + {z_k}} \right)\\ &= \left( {{y_{k + 2}} + a{y_{k + 1}} + b{y_k}} \right) + \left( {{z_{k + 2}} + a{z_{k + 1}} + b{z_k}} \right)\\ &= T\left\{ {{y_k}} \right\} + T\left\{ {{z_k}} \right\}\end{aligned}\)

To check the associative property, replace \(\left\{ {{y_k}} \right\}\)by \(\left\{ {r{y_k}} \right\}\)and simplify as shown below:

\(\begin{aligned} T\left( {r\left\{ {{y_k}} \right\}} \right) &= T\left( {r{y_k}} \right)\\ &= r{y_{k + 2}} + a\left( {r{y_{k + 1}}} \right) + b\left( {r{y_k}} \right)\\ &= r\left( {{y_{k + 2}} + a{y_{k + 1}} + b{y_k}} \right)\\ &= rT\left\{ {{y_k}} \right\}\end{aligned}\)

03

Draw a conclusion

As vector \(T\) satisfies both properties, the given mapping is a linear transformation from S into S.

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Most popular questions from this chapter

In Exercises 27-30, use coordinate vectors to test the linear independence of the sets of polynomials. Explain your work.

\({\bf{1}} - {\bf{2}}{t^{\bf{2}}} - {t^{\bf{3}}}\), \(t + {\bf{2}}{t^{\bf{3}}}\), \({\bf{1}} + t - {\bf{2}}{t^{\bf{2}}}\)

Question 18: Suppose A is a \(4 \times 4\) matrix and B is a \(4 \times 2\) matrix, and let \({{\mathop{\rm u}\nolimits} _0},...,{{\mathop{\rm u}\nolimits} _3}\) represent a sequence of input vectors in \({\mathbb{R}^2}\).

  1. Set \({{\mathop{\rm x}\nolimits} _0} = 0\), compute \({{\mathop{\rm x}\nolimits} _1},...,{{\mathop{\rm x}\nolimits} _4}\) from equation (1), and write a formula for \({{\mathop{\rm x}\nolimits} _4}\) involving the controllability matrix \(M\) appearing in equation (2). (Note: The matrix \(M\) is constructed as a partitioned matrix. Its overall size here is \(4 \times 8\).)
  2. Suppose \(\left( {A,B} \right)\) is controllable and v is any vector in \({\mathbb{R}^4}\). Explain why there exists a control sequence \({{\mathop{\rm u}\nolimits} _0},...,{{\mathop{\rm u}\nolimits} _3}\) in \({\mathbb{R}^2}\) such that \({{\mathop{\rm x}\nolimits} _4} = {\mathop{\rm v}\nolimits} \).

(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.)

Question: Determine if the matrix pairs in Exercises 19-22 are controllable.

20. \(A = \left( {\begin{array}{*{20}{c}}{.8}&{ - .3}&0\\{.2}&{.5}&1\\0&0&{ - .5}\end{array}} \right),B = \left( {\begin{array}{*{20}{c}}1\\1\\0\end{array}} \right)\).

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

13. Show that if \(P\) is an invertible \(m \times m\) matrix, then rank\(PA\)=rank\(A\).(Hint: Apply Exercise12 to \(PA\) and \({P^{ - 1}}\left( {PA} \right)\).)

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