Decompose \( \mathbf{v} \) into two vectors \( \mathbf{v}_{1} \) and \( \mathbf{v}_{2} \), where \( \mathbf{v}_{1} \) is parallel to \( \mathbf{w} \) and \( \mathbf{v}_{2} \) is orthogonal to \( \mathbf{w} \). \( \mathbf{v}=4 \mathbf{i}-6 \mathbf{j} \) and \( \mathbf{w}=\mathbf{i}+\mathbf{j} \)
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To decompose \( \mathbf{v} \) into \( \mathbf{v}_{1} \) and \( \mathbf{v}_{2} \), we start by finding the projection of \( \mathbf{v} \) onto \( \mathbf{w} \). The formula for the projection is: \[ \text{proj}_{\mathbf{w}} \mathbf{v} = \frac{\mathbf{v} \cdot \mathbf{w}}{\mathbf{w} \cdot \mathbf{w}} \mathbf{w} \] First, we calculate the dot products: \[ \mathbf{v} \cdot \mathbf{w} = (4 \mathbf{i} - 6 \mathbf{j}) \cdot (\mathbf{i} + \mathbf{j}) = 4 \cdot 1 + (-6) \cdot 1 = 4 - 6 = -2 \] \[ \mathbf{w} \cdot \mathbf{w} = (\mathbf{i} + \mathbf{j}) \cdot (\mathbf{i} + \mathbf{j}) = 1^2 + 1^2 = 1 + 1 = 2 \] Now we can compute the projection: \[ \text{proj}_{\mathbf{w}} \mathbf{v} = \frac{-2}{2} (\mathbf{i} + \mathbf{j}) = -1 (\mathbf{i} + \mathbf{j}) = -\mathbf{i} - \mathbf{j} \] This gives us \( \mathbf{v}_{1} = -\mathbf{i} - \mathbf{j} \). To find \( \mathbf{v}_{2} \), we use the decomposition: \[ \mathbf{v} = \mathbf{v}_{1} + \mathbf{v}_{2} \] Therefore, \[ \mathbf{v}_{2} = \mathbf{v} - \mathbf{v}_{1} = (4 \mathbf{i} - 6 \mathbf{j}) - (-\mathbf{i} - \mathbf{j}) = 4 \mathbf{i} - 6 \mathbf{j} + \mathbf{i} + \mathbf{j} = (4 + 1) \mathbf{i} + (-6 + 1) \mathbf{j} = 5 \mathbf{i} - 5 \mathbf{j} \] To summarize: \[ \mathbf{v}_{1} = -\mathbf{i} - \mathbf{j}, \quad \mathbf{v}_{2} = 5 \mathbf{i} - 5 \mathbf{j} \]