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Compute the products in Exercises 1–4 using (a) the definition, as

in Example 1, and (b) the row–vector rule for computing \(A{\bf{x}}\). If a product is undefined, explain why.

1. \(\left[ {\begin{array}{*{20}{c}}{ - 4}&2\\1&6\\0&1\end{array}} \right]\left[ {\begin{array}{*{20}{c}}3\\{ - 2}\\7\end{array}} \right]\)

Short Answer

Expert verified

The product is not defined because the number of columns in the matrix does not match the number of entries in the vector.

Step by step solution

01

Write the condition for the product of a vector and a matrix

According to the definition, the weights in a linear combination of matrix A columns are represented by the entries in vector x.

Also, the product by using the row-vector rule is defined as shown below:

\(\begin{array}{c}A{\bf{x}} = \left[ {\begin{array}{*{20}{c}}{{a_1}}&{{a_2}}&{ \cdot \cdot \cdot }&{{a_n}}\end{array}} \right]\left[ {\begin{array}{*{20}{c}}{{x_1}}\\ \vdots \\{{x_n}}\end{array}} \right]\\ = {x_1}{a_1} + {x_2}{a_2} + \cdots + {x_n}{a_n}\end{array}\)

The number of columns in matrix \(A\) should be equal to the number of entries in vector x so that \(A{\bf{x}}\) can be defined.

02

Obtain the number of columns in matrix A

Consider matrix \(A = \left[ {\begin{array}{*{20}{c}}{ - 4}&2\\1&6\\0&1\end{array}} \right]\).

It is observed that the number of columns in matrix \(A\) is 2.

03

Obtain the number of entries in vector x

Consider matrix \(x = \left[ {\begin{array}{*{20}{c}}3\\{ - 2}\\7\end{array}} \right]\).

It is observed that the number of entries in vector x is 3.

04

Check if \(Ax\) is defined or not

Since the number of columns in matrix \(A\) is not equal to the number of entries in vector x; so \(A{\bf{x}}\) cannot be defined.

Therefore, the product of \(\left[ {\begin{array}{*{20}{c}}{ - 4}&2\\1&6\\0&1\end{array}} \right]\left[ {\begin{array}{*{20}{c}}3\\{ - 2}\\7\end{array}} \right]\) is undefined.

Hence, the product is not defined.

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Most popular questions from this chapter

In Exercises 15 and 16, list five vectors in Span \(\left\{ {{v_1},{v_2}} \right\}\). For each vector, show the weights on \({{\mathop{\rm v}\nolimits} _1}\) and \({{\mathop{\rm v}\nolimits} _2}\) used to generate the vector and list the three entries of the vector. Do not make a sketch.

16. \({{\mathop{\rm v}\nolimits} _1} = \left[ {\begin{array}{*{20}{c}}3\\0\\2\end{array}} \right],{v_2} = \left[ {\begin{array}{*{20}{c}}{ - 2}\\0\\3\end{array}} \right]\)

Suppose a linear transformation \(T:{\mathbb{R}^n} \to {\mathbb{R}^n}\) has the property that \(T\left( {\mathop{\rm u}\nolimits} \right) = T\left( {\mathop{\rm v}\nolimits} \right)\) for some pair of distinct vectors u and v in \({\mathbb{R}^n}\). Can Tmap \({\mathbb{R}^n}\) onto \({\mathbb{R}^n}\)? Why or why not?

In Exercises 23 and 24, key statements from this section are either quoted directly, restated slightly (but still true), or altered in some way that makes them false in some cases. Mark each statement True or False, and justify your answer.(If true, give the approximate location where a similar statement appears, or refer to a definition or theorem. If false, give the location of a statement that has been quoted or used incorrectly, or cite an example that shows the statement is not true in all cases.) Similar true/false questions will appear in many sections of the text.

24.

a. Elementary row operations on an augmented matrix never change the solution set of the associated linear system.

b. Two matrices are row equivalent if they have the same number of rows.

c. An inconsistent system has more than one solution.

d. Two linear systems are equivalent if they have the same solution set.

In Exercises 9, write a vector equation that is equivalent to

the given system of equations.

9. \({x_2} + 5{x_3} = 0\)

\(\begin{array}{c}4{x_1} + 6{x_2} - {x_3} = 0\\ - {x_1} + 3{x_2} - 8{x_3} = 0\end{array}\)

Describe the possible echelon forms of the matrix A. Use the notation of Example 1 in Section 1.2.

a. A is a \({\bf{2}} \times {\bf{3}}\) matrix whose columns span \({\mathbb{R}^{\bf{2}}}\).

b. A is a \({\bf{3}} \times {\bf{3}}\) matrix whose columns span \({\mathbb{R}^{\bf{3}}}\).

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