In Exercises 33–38, find projᵥᵥ v. Then decompose v into two vectors, v₁ and v₂, where v₁ is parallel to w and v₂ is orthogonal to w. v = i + 2j, w = 3i + 6j
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Identify the vectors given: \( \mathbf{v} = \mathbf{i} + 2\mathbf{j} \) and \( \mathbf{w} = 3\mathbf{i} + 6\mathbf{j} \). Express them in component form as \( \mathbf{v} = (1, 2) \) and \( \mathbf{w} = (3, 6) \).
Recall the formula for the projection of \( \mathbf{v} \) onto \( \mathbf{w} \):
\[
\text{proj}_{\mathbf{w}} \mathbf{v} = \left( \frac{\mathbf{v} \cdot \mathbf{w}}{\mathbf{w} \cdot \mathbf{w}} \right) \mathbf{w}
\]
where \( \mathbf{v} \cdot \mathbf{w} \) is the dot product of \( \mathbf{v} \) and \( \mathbf{w} \).
Substitute the dot products into the projection formula to find \( \text{proj}_{\mathbf{w}} \mathbf{v} \). This gives the vector \( \mathbf{v}_1 \) which is parallel to \( \mathbf{w} \).
Find the vector \( \mathbf{v}_2 \) which is orthogonal to \( \mathbf{w} \) by subtracting the projection from \( \mathbf{v} \):
\[
\mathbf{v}_2 = \mathbf{v} - \mathbf{v}_1
\]
This completes the decomposition of \( \mathbf{v} \) into \( \mathbf{v}_1 \) (parallel to \( \mathbf{w} \)) and \( \mathbf{v}_2 \) (orthogonal to \( \mathbf{w} \)).
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Key Concepts
Here are the essential concepts you must grasp in order to answer the question correctly.
Vector Projection
Vector projection of v onto w, denoted proj_w v, is the component of v that points in the direction of w. It is calculated using the formula proj_w v = [(v · w) / (w · w)] * w, where '·' denotes the dot product. This concept helps in finding how much of one vector lies along another.
The dot product is an algebraic operation that takes two vectors and returns a scalar. It is computed as v · w = v₁w₁ + v₂w₂ for 2D vectors. The dot product is essential for finding projections and determining angles between vectors.
Vector Decomposition into Parallel and Orthogonal Components
Any vector v can be decomposed into two components: v₁ parallel to w and v₂ orthogonal to w. Here, v₁ = proj_w v, and v₂ = v - v₁. This decomposition is useful in many applications, such as resolving forces or simplifying vector problems.