Let a = (1,3,–2), b=(2,2, –1) . 2. Find la- 36| a) b) a•b c) The angle (in degrees) between a and b

Algebra & Trigonometry with Analytic Geometry
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ISBN:9781133382119
Author:Swokowski
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Chapter10: Sequences, Series, And Probability
Section10.7: Distinguishable Permutations And Combinations
Problem 21E
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2. Let \(\vec{a} = \langle 1, 3, -2 \rangle\), \(\vec{b} = \langle 2, 2, -1 \rangle\). Find

a) \[ \left| \vec{a} - 3\vec{b} \right| \]

b) \[ \vec{a} \cdot \vec{b} \]

c) The angle (in degrees) between \(\vec{a}\) and \(\vec{b}\).
Transcribed Image Text:2. Let \(\vec{a} = \langle 1, 3, -2 \rangle\), \(\vec{b} = \langle 2, 2, -1 \rangle\). Find a) \[ \left| \vec{a} - 3\vec{b} \right| \] b) \[ \vec{a} \cdot \vec{b} \] c) The angle (in degrees) between \(\vec{a}\) and \(\vec{b}\).
**Vectors and Projections**

In this section, we will discuss two important concepts in vector mathematics: vector projection and tensor product.

**Vector Projection**

d) \(\text{proj}_{\mathbf{a}} \mathbf{b}\)

The projection of vector \(\mathbf{b}\) onto vector \(\mathbf{a}\), denoted as \(\text{proj}_{\mathbf{a}} \mathbf{b}\), is a vector that represents the shadow or image of \(\mathbf{b}\) onto the line defined by \(\mathbf{a}\). Mathematically, this can be expressed using the formula:
\[ \text{proj}_{\mathbf{a}} \mathbf{b} = \frac{\mathbf{a} \cdot \mathbf{b}}{\mathbf{a} \cdot \mathbf{a}} \mathbf{a} \]
where \(\mathbf{a} \cdot \mathbf{b}\) represents the dot product between \(\mathbf{a}\) and \(\mathbf{b}\).

**Tensor Product**

e) \(\mathbf{a} \otimes \mathbf{b}\)

The tensor product (or outer product) of vectors \(\mathbf{a}\) and \(\mathbf{b}\), denoted as \(\mathbf{a} \otimes \mathbf{b}\), results in a matrix. If vector \(\mathbf{a}\) has elements \(a_1, a_2, ..., a_m\) and vector \(\mathbf{b}\) has elements \(b_1, b_2, ..., b_n\), then the tensor product is an \(m \times n\) matrix formed by multiplying each element of \(\mathbf{a}\) with each element of \(\mathbf{b}\). The result is:
\[ \mathbf{a} \otimes \mathbf{b} = \begin{bmatrix}
a_1 b_1 & a_1 b_2 & \dots & a_1 b_n \\
a_2 b_1 & a_2 b_2 & \dots & a_2 b_n \\
\vdots & \vdots & \ddots & \vdots \\
a_m b_1 & a_m b_2 & \dots & a_m b_n \\
\end{bmatrix} \]
Transcribed Image Text:**Vectors and Projections** In this section, we will discuss two important concepts in vector mathematics: vector projection and tensor product. **Vector Projection** d) \(\text{proj}_{\mathbf{a}} \mathbf{b}\) The projection of vector \(\mathbf{b}\) onto vector \(\mathbf{a}\), denoted as \(\text{proj}_{\mathbf{a}} \mathbf{b}\), is a vector that represents the shadow or image of \(\mathbf{b}\) onto the line defined by \(\mathbf{a}\). Mathematically, this can be expressed using the formula: \[ \text{proj}_{\mathbf{a}} \mathbf{b} = \frac{\mathbf{a} \cdot \mathbf{b}}{\mathbf{a} \cdot \mathbf{a}} \mathbf{a} \] where \(\mathbf{a} \cdot \mathbf{b}\) represents the dot product between \(\mathbf{a}\) and \(\mathbf{b}\). **Tensor Product** e) \(\mathbf{a} \otimes \mathbf{b}\) The tensor product (or outer product) of vectors \(\mathbf{a}\) and \(\mathbf{b}\), denoted as \(\mathbf{a} \otimes \mathbf{b}\), results in a matrix. If vector \(\mathbf{a}\) has elements \(a_1, a_2, ..., a_m\) and vector \(\mathbf{b}\) has elements \(b_1, b_2, ..., b_n\), then the tensor product is an \(m \times n\) matrix formed by multiplying each element of \(\mathbf{a}\) with each element of \(\mathbf{b}\). The result is: \[ \mathbf{a} \otimes \mathbf{b} = \begin{bmatrix} a_1 b_1 & a_1 b_2 & \dots & a_1 b_n \\ a_2 b_1 & a_2 b_2 & \dots & a_2 b_n \\ \vdots & \vdots & \ddots & \vdots \\ a_m b_1 & a_m b_2 & \dots & a_m b_n \\ \end{bmatrix} \]
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