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In Exercises 3-8, find the \({\bf{3}} \times {\bf{3}}\) matrices that produce the described composite 2D transformations, using homogenous coordinates.

Rotate points through \({\bf{60}}^\circ \) about the point \(\left( {{\bf{6}},{\bf{8}}} \right)\).

Short Answer

Expert verified

\(\left[ {\begin{array}{*{20}{c}}{\frac{1}{2}}&{ - \frac{{\sqrt 3 }}{2}}&{3 + 4\sqrt 3 }\\{\frac{{\sqrt 3 }}{2}}&{\frac{1}{2}}&{4 - 3\sqrt 3 }\\0&0&1\end{array}} \right]\)

Step by step solution

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01

Find the matrix for translation

The point \(\left( {6,8} \right)\) must be shifted back to the origin.

The translation matrix is

\(\left[ {\begin{array}{*{20}{c}}1&0&{ - 6}\\0&1&{ - 8}\\0&0&1\end{array}} \right]\).

02

Find the matrix for rotation

The matrix for rotation by \(60^\circ \) is

\(\left[ {\begin{array}{*{20}{c}}{\cos 60^\circ }&{ - \sin 60^\circ }&0\\{\sin 60^\circ }&{\cos 60^\circ }&0\\0&0&1\end{array}} \right] = \left[ {\begin{array}{*{20}{c}}{\frac{1}{2}}&{ - \frac{{\sqrt 3 }}{2}}&0\\{\frac{{\sqrt 3 }}{2}}&{\frac{1}{2}}&0\\0&0&1\end{array}} \right]\).

03

Find the matrix for translation

The point must again be shifted back to \(\left( {6,8} \right)\). The translation matrix is:

\(\left[ {\begin{array}{*{20}{c}}1&0&6\\0&1&8\\0&0&1\end{array}} \right]\)

04

Find the combined matrix of transformation

The combined matrix for transformation can be expressed as

\(\left[ {\begin{array}{*{20}{c}}1&0&6\\0&1&8\\0&0&1\end{array}} \right]\left[ {\begin{array}{*{20}{c}}{\frac{1}{2}}&{ - \frac{{\sqrt 3 }}{2}}&0\\{\frac{{\sqrt 3 }}{2}}&{\frac{1}{2}}&0\\0&0&1\end{array}} \right]\left[ {\begin{array}{*{20}{c}}1&0&{ - 6}\\0&1&{ - 8}\\0&0&1\end{array}} \right] = \left[ {\begin{array}{*{20}{c}}{\frac{1}{2}}&{ - \frac{{\sqrt 3 }}{2}}&{3 + 4\sqrt 3 }\\{\frac{{\sqrt 3 }}{2}}&{\frac{1}{2}}&{4 - 3\sqrt 3 }\\0&0&1\end{array}} \right]\).

So, the transformed matrix is \(\left[ {\begin{array}{*{20}{c}}{\frac{1}{2}}&{ - \frac{{\sqrt 3 }}{2}}&{3 + 4\sqrt 3 }\\{\frac{{\sqrt 3 }}{2}}&{\frac{1}{2}}&{4 - 3\sqrt 3 }\\0&0&1\end{array}} \right]\).

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Most popular questions from this chapter

Suppose \(AD = {I_m}\) (the \(m \times m\) identity matrix). Show that for any b in \({\mathbb{R}^m}\), the equation \(A{\mathop{\rm x}\nolimits} = {\mathop{\rm b}\nolimits} \) has a solution. (Hint: Think about the equation \(AD{\mathop{\rm b}\nolimits} = {\mathop{\rm b}\nolimits} \).) Explain why Acannot have more rows than columns.

Let \(T:{\mathbb{R}^n} \to {\mathbb{R}^n}\) be an invertible linear transformation. Explain why T is both one-to-one and onto \({\mathbb{R}^n}\). Use equations (1) and (2). Then give a second explanation using one or more theorems.

Let \(X\) be \(m \times n\) data matrix such that \({X^T}X\) is invertible., and let \(M = {I_m} - X{\left( {{X^T}X} \right)^{ - {\bf{1}}}}{X^T}\). Add a column \({x_{\bf{0}}}\) to the data and form

\(W = \left[ {\begin{array}{*{20}{c}}X&{{x_{\bf{0}}}}\end{array}} \right]\)

Compute \({W^T}W\). The \(\left( {{\bf{1}},{\bf{1}}} \right)\) entry is \({X^T}X\). Show that the Schur complement (Exercise 15) of \({X^T}X\) can be written in the form \({\bf{x}}_{\bf{0}}^TM{{\bf{x}}_{\bf{0}}}\). It can be shown that the quantity \({\left( {{\bf{x}}_{\bf{0}}^TM{{\bf{x}}_{\bf{0}}}} \right)^{ - {\bf{1}}}}\) is the \(\left( {{\bf{2}},{\bf{2}}} \right)\)-entry in \({\left( {{W^T}W} \right)^{ - {\bf{1}}}}\). This entry has a useful statistical interpretation, under appropriate hypotheses.

In the study of engineering control of physical systems, a standard set of differential equations is transformed by Laplace transforms into the following system of linear equations:

\(\left[ {\begin{array}{*{20}{c}}{A - s{I_n}}&B\\C&{{I_m}}\end{array}} \right]\left[ {\begin{array}{*{20}{c}}{\bf{x}}\\{\bf{u}}\end{array}} \right] = \left[ {\begin{array}{*{20}{c}}{\bf{0}}\\{\bf{y}}\end{array}} \right]\)

Where \(A\) is \(n \times n\), \(B\) is \(n \times m\), \(C\) is \(m \times n\), and \(s\) is a variable. The vector \({\bf{u}}\) in \({\mathbb{R}^m}\) is the “input” to the system, \({\bf{y}}\) in \({\mathbb{R}^m}\) is the “output” and \({\bf{x}}\) in \({\mathbb{R}^n}\) is the “state” vector. (Actually, the vectors \({\bf{x}}\), \({\bf{u}}\) and \({\bf{v}}\) are functions of \(s\), but we suppress this fact because it does not affect the algebraic calculations in Exercises 19 and 20.)

If A, B, and X are \(n \times n\) invertible matrices, does the equation \({C^{ - 1}}\left( {A + X} \right){B^{ - 1}} = {I_n}\) have a solution, X? If so, find it.

In Exercises 27 and 28, view vectors in \({\mathbb{R}^n}\) as \(n \times 1\) matrices. For \({\mathop{\rm u}\nolimits} \) and \({\mathop{\rm v}\nolimits} \) in \({\mathbb{R}^n}\), the matrix product \({{\mathop{\rm u}\nolimits} ^T}v\) is a \(1 \times 1\) matrix, called the scalar product, or inner product, of u and v. It is usually written as a single real number without brackets. The matrix product \({{\mathop{\rm uv}\nolimits} ^T}\) is an \(n \times n\) matrix, called the outer product of u and v. The products \({{\mathop{\rm u}\nolimits} ^T}{\mathop{\rm v}\nolimits} \) and \({{\mathop{\rm uv}\nolimits} ^T}\) will appear later in the text.

27. Let \({\mathop{\rm u}\nolimits} = \left( {\begin{aligned}{*{20}{c}}{ - 2}\\3\\{ - 4}\end{aligned}} \right)\) and \({\mathop{\rm v}\nolimits} = \left( {\begin{aligned}{*{20}{c}}a\\b\\c\end{aligned}} \right)\). Compute \({{\mathop{\rm u}\nolimits} ^T}{\mathop{\rm v}\nolimits} \), \({{\mathop{\rm v}\nolimits} ^T}{\mathop{\rm u}\nolimits} \),\({{\mathop{\rm uv}\nolimits} ^T}\), and \({\mathop{\rm v}\nolimits} {{\mathop{\rm u}\nolimits} ^T}\).

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