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Let \({\bf{u}} = \left( {\begin{aligned}5\\{ - 6}\\7\end{aligned}} \right)\), and let \(W\) be the set of all \({\bf{x}}\) in \({\mathbb{R}^3}\) such that \({\bf{u}} \cdot {\bf{x}} = 0\). What theorem in Chapter 4 can be used to show that \(W\) is a subspace of \({\mathbb{R}^3}\)? Describe \(W\) in geometric language.

Short Answer

Expert verified

The theorem that can be used in chapter 4 is theorem 2. And geometrically, \(W\) is a plane through the origin.

Step by step solution

01

Definition of Orthogonal sets

The two vectors \({\bf{u}}{\rm{ and }}{\bf{v}}\) are Orthogonal if:

\(\begin{aligned}{l}{\left\| {{\bf{u}} + {\bf{v}}} \right\|^2} = {\left\| {\bf{u}} \right\|^2} + {\left\| {\bf{v}} \right\|^2}\\{\rm{and}}\\{\bf{u}} \cdot {\bf{v}} = 0\end{aligned}\).

02

Check whether \(W\) is a subspace of \({\mathbb{R}^3}\) or not

The given vector is, \({\bf{u}} = \left( {\begin{aligned}{*{20}{c}}5\\{ - 6}\\7\end{aligned}} \right)\) and \(W = \left\{ {x \in {\mathbb{R}^3}|{\bf{u}} \cdot {\bf{x}} = 0} \right\}\).

Since is a null space of the \(1 \times 3\) matrix \({{\bf{u}}^T}\).

Therefore, Theorem 2 can be used to verify that \(W\) is a subspace of \({\mathbb{R}^3}\), which is possible only, if \({\bf{u}} \cdot {\bf{x}} = 0\) or \({{\bf{u}}^T} \cdot {\bf{x}} = 0\), this shows that \(W\) is a null-pace of \({{\bf{u}}^T}\). Hence \(W\) is a subspace of \({\mathbb{R}^3}\).

03

Define geometrically

As \(W\) has all the vectors which are perpendicular to \({\bf{u}}\). So find \({\bf{u}} \cdot {\bf{x}} = 0\) by letting \({\bf{x}} = \left( {\begin{aligned}{*{20}{c}}{{x_1}}\\{{x_2}}\\{{x_3}}\end{aligned}} \right)\).

\(\begin{aligned}{c}\left( {\begin{aligned}{*{20}{c}}5\\{ - 6}\\7\end{aligned}} \right) \cdot \left( {\begin{aligned}{*{20}{c}}{{x_1}}\\{{x_2}}\\{{x_3}}\end{aligned}} \right) = 0\\5{x_1} - 6{x_2} + 7{x_3} = 0\end{aligned}\)

So geometrically, the subspace \(W\) is a plane passing through the origin.

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Most popular questions from this chapter

In Exercises 11 and 12, find the closest point to\[{\bf{y}}\]in the subspace\[W\]spanned by\[{{\bf{v}}_1}\], and\[{{\bf{v}}_2}\].

11.\[y = \left[ {\begin{aligned}3\\1\\5\\1\end{aligned}} \right]\],\[{{\bf{v}}_1} = \left[ {\begin{aligned}3\\1\\{ - 1}\\1\end{aligned}} \right]\],\[{{\bf{v}}_2} = \left[ {\begin{aligned}1\\{ - 1}\\1\\{ - 1}\end{aligned}} \right]\]

In Exercises 9-12 find (a) the orthogonal projection of b onto \({\bf{Col}}A\) and (b) a least-squares solution of \(A{\bf{x}} = {\bf{b}}\).

12. \(A = \left[ {\begin{array}{{}{}}{\bf{1}}&{\bf{1}}&{\bf{0}}\\{\bf{1}}&{\bf{0}}&{ - {\bf{1}}}\\{\bf{0}}&{\bf{1}}&{\bf{1}}\\{ - {\bf{1}}}&{\bf{1}}&{ - {\bf{1}}}\end{array}} \right]\), \({\bf{b}} = \left( {\begin{array}{{}{}}{\bf{2}}\\{\bf{5}}\\{\bf{6}}\\{\bf{6}}\end{array}} \right)\)

24. Question: In Exercises 23 and 24, all vectors are in \({\mathbb{R}^n}\). Mark each statement True or False. Justify each answer.

  1. Not every orthogonal set in \({\mathbb{R}^n}\) is linearly independent.
  2. If a set \(S = \left\{ {{{\mathop{\rm u}\nolimits} _1}, \ldots ,{{\mathop{\rm u}\nolimits} _p}} \right\}\) has the property that \({{\mathop{\rm u}\nolimits} _i} \cdot {{\mathop{\rm u}\nolimits} _j} = 0\) whenever \(i \ne j\), then \(S\) is an orthonormal set.
  3. If the columns of a \(m \times n\) matrix A are orthonormal, then the linear mapping \({\mathop{\rm x}\nolimits} \mapsto A{\mathop{\rm x}\nolimits} \) preserves lengths.
  4. The orthogonal projection of y onto v is the same as the orthogonal projection of y onto \(c{\mathop{\rm v}\nolimits} \) whenever \(c \ne 0\).
  5. An orthogonal matrix is invertible.

Question: In Exercises 17-22, determine which sets of vectors are orthonormal. If a set is only orthogonal, normalize the vectors to produce an orthonormal set.

20. \(\left( {\begin{array}{*{20}{c}}{ - \frac{2}{3}}\\{\frac{1}{3}}\\{\frac{2}{3}}\end{array}} \right),\left( {\begin{array}{*{20}{c}}{\frac{1}{3}}\\{\frac{2}{3}}\\0\end{array}} \right)\)

In Exercises 7–10, let\[W\]be the subspace spanned by the\[{\bf{u}}\]’s, and write y as the sum of a vector in\[W\]and a vector orthogonal to\[W\].

7.\[y = \left[ {\begin{aligned}1\\3\\5\end{aligned}} \right]\],\[{{\bf{u}}_1} = \left[ {\begin{aligned}1\\3\\{ - 2}\end{aligned}} \right]\],\[{{\bf{u}}_2} = \left[ {\begin{aligned}5\\1\\4\end{aligned}} \right]\]

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