Question 24: Let W be a subspace of \({\mathbb{R}^n}\) with an orthogonal basis \(\left\{ {{{\mathop{\rm w}\nolimits} _1}, \ldots ,{{\mathop{\rm w}\nolimits} _p}} \right\}\), and let \(\left\{ {{{\mathop{\rm v}\nolimits} _1}, \ldots ,{{\bf{v}}_q}} \right\}\) be an orthogonal basis for \({W^ \bot }\).

  1. Explain why \(\left\{ {{{\mathop{\rm w}\nolimits} _1}, \ldots ,{{\mathop{\rm w}\nolimits} _p},{{\mathop{\rm v}\nolimits} _1}, \ldots ,{{\bf{v}}_q}} \right\}\) is an orthogonal set.
  2. Explain why the set in part (a) spans \({\mathbb{R}^n}\).
  3. Show that \(\dim W + \dim {W^ \bot } = n\).

Short Answer

Expert verified
  1. \(\left\{ {{{\mathop{\rm w}\nolimits} _1}, \ldots ,{{\mathop{\rm w}\nolimits} _p},{{\mathop{\rm v}\nolimits} _1}, \ldots ,{{\bf{v}}_q}} \right\}\) creates an orthogonal set because the vectors are pairwise orthogonal.
  2. By using the Orthogonal Decomposition Theorem, the set \(\left\{ {{{\mathop{\rm w}\nolimits} _1}, \ldots ,{{\mathop{\rm w}\nolimits} _p},{{\mathop{\rm v}\nolimits} _1}, \ldots ,{{\bf{v}}_q}} \right\}\) spans \({\mathbb{R}^n}\).
  3. It is proved that \(\dim W + \dim {W^ \bot } = n\).

Step by step solution

01

Explain why \(\left\{ {{{\mathop{\rm w}\nolimits} _1}, \ldots ,{{\mathop{\rm w}\nolimits} _p},{{\mathop{\rm v}\nolimits} _1}, \ldots ,{{\bf{v}}_q}} \right\}\) is an orthogonal set

According to the hypothesis, the vectors \({{\mathop{\rm w}\nolimits} _1}, \ldots ,{{\mathop{\rm w}\nolimits} _p}\) are pairwise orthogonal, and the vectors \({{\mathop{\rm v}\nolimits} _1}, \ldots ,{{\mathop{\rm v}\nolimits} _q}\) are pairwise orthogonal. So, \({{\bf{v}}_j}\) is in \({W^ \bot }\) for all j, \({{\bf{w}}_j} \cdot {{\bf{v}}_j} = 0\) for all i because \({{\bf{w}}_i}\) is in \(W\) for any i.

Therefore, \(\left\{ {{{\mathop{\rm w}\nolimits} _1}, \ldots ,{{\mathop{\rm w}\nolimits} _p},{{\mathop{\rm v}\nolimits} _1}, \ldots ,{{\bf{v}}_q}} \right\}\) creates an orthogonal set.

02

Explain why the set in part (a) spans \({\mathbb{R}^n}\)

Write \({\bf{y}} = \widehat {\bf{y}} + {\bf{z}}\) for all y in \({\mathbb{R}^n}\), according to the Orthogonal Decomposition Theorem, with \(\widehat {\bf{y}}\) in W and \({\bf{z}}\) in \({W^ \bot }\). Then, there is a scalar \({c_1}, \ldots {c_p}\) and \({d_1}, \ldots {d_q}\) such that;

\(\begin{array}{c}{\bf{y}} = \widehat {\bf{y}} + {\bf{z}}\\ = {c_1}{{\bf{w}}_1} + \ldots + {c_p}{{\bf{w}}_p} + {d_1}{{\bf{v}}_1} + \ldots + {d_q}{{\bf{v}}_q}\end{array}\)

Therefore, the set \(\left\{ {{{\mathop{\rm w}\nolimits} _1}, \ldots ,{{\mathop{\rm w}\nolimits} _p},{{\mathop{\rm v}\nolimits} _1}, \ldots ,{{\bf{v}}_q}} \right\}\) spans \({\mathbb{R}^n}\).

03

Show that \(\dim W + \dim {W^ \bot } = n\)

According to part (a), the set \(\left\{ {{{\mathop{\rm w}\nolimits} _1}, \ldots ,{{\mathop{\rm w}\nolimits} _p},{{\mathop{\rm v}\nolimits} _1}, \ldots ,{{\bf{v}}_q}} \right\}\) is linearly independent and spans \({\mathbb{R}^n}\) according to part (b), and therefore, forms a basis for \({\mathbb{R}^n}\).

Thus, \(\dim W + \dim {W^ \bot } = p + q = \dim {\mathbb{R}^n}\).

Thus, it is proved that \(\dim W + \dim {W^ \bot } = n\).

Unlock Step-by-Step Solutions & Ace Your Exams!

  • Full Textbook Solutions

    Get detailed explanations and key concepts

  • Unlimited Al creation

    Al flashcards, explanations, exams and more...

  • Ads-free access

    To over 500 millions flashcards

  • Money-back guarantee

    We refund you if you fail your exam.

Over 30 million students worldwide already upgrade their learning with Vaia!

One App. One Place for Learning.

All the tools & learning materials you need for study success - in one app.

Get started for free

Most popular questions from this chapter

Suppose the x-coordinates of the data \(\left( {{x_1},{y_1}} \right), \ldots ,\left( {{x_n},{y_n}} \right)\) are in mean deviation form, so that \(\sum {{x_i}} = 0\). Show that if \(X\) is the design matrix for the least-squares line in this case, then \({X^T}X\) is a diagonal matrix.

In Exercises 5 and 6, describe all least squares solutions of the equation \(A{\bf{x}} = {\bf{b}}\).

6.\(A = \left( {\begin{aligned}{{}{}}{\bf{1}}&{\bf{1}}&{\bf{0}}\\{\bf{1}}&{\bf{1}}&{\bf{0}}\\{\bf{1}}&{\bf{1}}&{\bf{0}}\\{\bf{1}}&{\bf{0}}&{\bf{1}}\\{\bf{1}}&{\bf{0}}&{\bf{1}}\\{\bf{1}}&{\bf{0}}&{\bf{1}}\end{aligned}} \right)\),\({\bf{b}} = \left( {\begin{aligned}{{}{}}{\bf{7}}\\{\bf{2}}\\{\bf{3}}\\{\bf{6}}\\{\bf{5}}\\{\bf{4}}\end{aligned}} \right)\)

Compute the quantities in Exercises 1-8 using the vectors

\({\mathop{\rm u}\nolimits} = \left( {\begin{aligned}{*{20}{c}}{ - 1}\\2\end{aligned}} \right),{\rm{ }}{\mathop{\rm v}\nolimits} = \left( {\begin{aligned}{*{20}{c}}4\\6\end{aligned}} \right),{\rm{ }}{\mathop{\rm w}\nolimits} = \left( {\begin{aligned}{*{20}{c}}3\\{ - 1}\\{ - 5}\end{aligned}} \right),{\rm{ }}{\mathop{\rm x}\nolimits} = \left( {\begin{aligned}{*{20}{c}}6\\{ - 2}\\3\end{aligned}} \right)\)

6. \(\left( {\frac{{{\mathop{\rm x}\nolimits} \cdot {\mathop{\rm w}\nolimits} }}{{{\mathop{\rm x}\nolimits} \cdot {\mathop{\rm x}\nolimits} }}} \right){\mathop{\rm x}\nolimits} \)

(M) Use the method in this section to produce a \(QR\) factorization of the matrix in Exercise 24.

Find the distance between \({\mathop{\rm x}\nolimits} = \left( {\begin{aligned}{*{20}{c}}{10}\\{ - 3}\end{aligned}} \right)\) and \({\mathop{\rm y}\nolimits} = \left( {\begin{aligned}{*{20}{c}}{ - 1}\\{ - 5}\end{aligned}} \right)\).

See all solutions

Recommended explanations on Math Textbooks

View all explanations

What do you think about this solution?

We value your feedback to improve our textbook solutions.

Study anywhere. Anytime. Across all devices.

Sign-up for free