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Let \({\bf{u}} = \left( {{u_1},{u_2},{u_3}} \right)\). Explain why \({\bf{u}} \cdot {\bf{u}} \ge 0\). When is \({\bf{u}} \cdot {\bf{u}} = 0\)?

Short Answer

Expert verified

The expression \({\bf{u}} \cdot {\bf{u}}\) means the sum of squares of element which will always be greater than zero. Their product will be zero if and only if the elements themselves are zero.

Step by step solution

01

Find the product \({\bf{u}} \cdot {\bf{u}}\) 

The given vector can be written as,\({\bf{u}} = \left( {\begin{aligned}{*{20}{c}}{{u_1}}\\{{u_2}}\\{{u_3}}\end{aligned}} \right)\).

Find \({\bf{u}} \cdot {\bf{u}}\).

\(\begin{aligned}{c}{\bf{u}} \cdot {\bf{u}} &= {\left( {\begin{aligned}{*{20}{c}}{{u_1}}\\{{u_2}}\\{{u_3}}\end{aligned}} \right)^T} \cdot \left( {\begin{aligned}{*{20}{c}}{{u_1}}\\{{u_2}}\\{{u_3}}\end{aligned}} \right)\\ &= u_1^2 + u_2^2 + u_3^2\end{aligned}\)

02

 Verification of statement

Since, the product \({\bf{u}} \cdot {\bf{u}}\) is the sum of squares of element which will always be greater than zero.

Therefore,

\({\bf{u}} \cdot {\bf{u}} \ge 0\)

The equation \({\bf{u}} \cdot {\bf{u}} = 0\) will be valid only when all the elements of the vector will be equal to zero.

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Most popular questions from this chapter

Compute the least-squares error associated with the least square solution found in Exercise 4.

In Exercises 17 and 18, all vectors and subspaces are in \({\mathbb{R}^n}\). Mark each statement True or False. Justify each answer.

17. a.If \(\left\{ {{{\bf{v}}_1},{{\bf{v}}_2},{{\bf{v}}_3}} \right\}\) is an orthogonal basis for\(W\), then multiplying

\({v_3}\)by a scalar \(c\) gives a new orthogonal basis \(\left\{ {{{\bf{v}}_1},{{\bf{v}}_2},c{{\bf{v}}_3}} \right\}\).

b. The Gram–Schmidt process produces from a linearly independent

set \(\left\{ {{{\bf{x}}_1}, \ldots ,{{\bf{x}}_p}} \right\}\)an orthogonal set \(\left\{ {{{\bf{v}}_1}, \ldots ,{{\bf{v}}_p}} \right\}\) with the property that for each \(k\), the vectors \({{\bf{v}}_1}, \ldots ,{{\bf{v}}_k}\) span the same subspace as that spanned by \({{\bf{x}}_1}, \ldots ,{{\bf{x}}_k}\).

c. If \(A = QR\), where \(Q\) has orthonormal columns, then \(R = {Q^T}A\).

In Exercises 1-6, the given set is a basis for a subspace W. Use the Gram-Schmidt process to produce an orthogonal basis for W.

5. \(\left( {\begin{aligned}{{}{}}1\\{ - 4}\\0\\1\end{aligned}} \right),\left( {\begin{aligned}{{}{}}7\\{ - 7}\\{ - 4}\\1\end{aligned}} \right)\)

Find an orthogonal basis for the column space of each matrix in Exercises 9-12.

10. \(\left( {\begin{aligned}{{}{}}{ - 1} & 6 & 6 \\ 3 & { - 8}&3\\1&{ - 2}&6\\1&{ - 4}&{ - 3}\end{aligned}} \right)\)

A certain experiment produces the data \(\left( {1,1.8} \right),\left( {2,2.7} \right),\left( {3,3.4} \right),\left( {4,3.8} \right),\left( {5,3.9} \right)\). Describe the model that produces a least-squares fit of these points by a function of the form

\(y = {\beta _1}x + {\beta _2}{x^2}\)

Such a function might arise, for example, as the revenue from the sale of \(x\) units of a product, when the amount offered for sale affects the price to be set for the product.

a. Give the design matrix, the observation vector, and the unknown parameter vector.

b. Find the associated least-squares curve for the data.

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