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Vector projection

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The vector resolute (also known as the vector projection) of two vectors, \mathbf{a} in the direction of \mathbf{b} (also "\mathbf{a} on \mathbf{b}"), is given by:

(\mathbf{a}\cdot\mathbf{\hat b})\mathbf{\hat b}\text{ or }(|\mathbf{a}|\cos\theta)\mathbf{\hat b}

where θ is the angle between the vectors \mathbf{b} and \mathbf{a}; the operator \cdot is the dot product; and \hat{\mathbf{b}} is the unit vector in the direction of \mathbf{b}.

The vector resolute is a vector, and is the orthogonal projection of the vector \mathbf{a} onto the vector \mathbf{b}. The vector resolute is also said to be a component of vector \mathbf{a} in the direction of vector \mathbf{b}.

The other component of \mathbf{a} (perpendicular to \mathbf{b}) is given by:

\mathbf{a}\ -\ (\mathbf{a}\cdot\mathbf{\hat b})\mathbf{\hat b}.

The vector resolute is also the scalar resolute multiplied by \mathbf{\hat b} (in order to convert it into a vector, or give it direction).

Contents

[edit] Vector resolute overview

If A and B are two vectors, the projection (C) of A on B is the vector that has the same slope as B with the length:

|C| = |A| \cos \theta\,

To calculate C use the definition of the dot product:  A \cdot B = |A| \, |B| \cos \theta \,

Using the above equation:

|C| = |A| \cos \theta\,

Multiply and divide by | B | at the same time:

|C| = \frac {|A| |B| \cos \theta} {|B| }\,

In the resulting fraction, the top term is the same as the dot product, hence:

|C| = \frac {A \cdot B} {|B| }\,

To find the length of | C | with an unknown θ, and unknown direction, multiply it with the unit vector B:

C = \frac {A \cdot B} {|B| } \frac {B} {|B|} = \frac {A \cdot B} {B \cdot B} B,

giving the final formula:

C = \frac {A \cdot B} {B \cdot B} B.

[edit] Matrix representation

The orthogonal projection can be represented by a projection matrix. To project a vector onto the unit vector a = (ax, ay, az), it would need to be multiplied with this projection matrix:

 P_a = a^T a = 
\begin{bmatrix} a_1 \\ a_2 \\ a_3 \end{bmatrix}
\begin{bmatrix} a_1 & a_2 & a_3 \end{bmatrix} = 
\begin{bmatrix}
a_1^2 & a_1 a_2 & a_1 a_3  \\
a_1 a_2 & a_2^2 & a_2 a_3  \\
a_1 a_3 & a_2 a_3 & a_3^2  \\
\end{bmatrix}.

[edit] Uses

The vector projection is an important operation in the Gram-Schmidt orthonormalization of vector space bases.

[edit] See also

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