Vector projection

Template:Short description Template:For Template:Lead too long The vector projection (also known as the vector component or vector resolution) of a vector Template:Math on (or onto) a nonzero vector Template:Math is the orthogonal projection of Template:Math onto a straight line parallel to Template:Math. The projection of Template:Math onto Template:Math is often written as ${\mathrm{proj}}_{𝐛}𝐚$ or Template:Math.

The vector component or vector resolute of Template:Math perpendicular to Template:Math, sometimes also called the vector rejection of Template:Math from Template:Math (denoted ${\mathrm{oproj}}_{𝐛}𝐚$ or Template:Math), is the orthogonal projection of Template:Math onto the plane (or, in general, hyperplane) that is orthogonal to Template:Math. Since both ${\mathrm{proj}}_{𝐛}𝐚$ and ${\mathrm{oproj}}_{𝐛}𝐚$ are vectors, and their sum is equal to Template:Math, the rejection of Template:Math from Template:Math is given by: $${\mathrm{oproj}}_{𝐛}𝐚=𝐚-{\mathrm{proj}}_{𝐛}𝐚.$$

Projection of Template:Math on Template:Math (a₁), and rejection of Template:Math from Template:Math (a₂).

When Template:Math, Template:Math has an opposite direction with respect to Template:Math.

To simplify notation, this article defines ${𝐚}_1:={\mathrm{proj}}_{𝐛}𝐚$ and ${𝐚}_2:={\mathrm{oproj}}_{𝐛}𝐚.$ Thus, the vector ${𝐚}_1$ is parallel to $𝐛,$ the vector ${𝐚}_2$ is orthogonal to $𝐛,$ and $𝐚={𝐚}_1+{𝐚}_2.$

The projection of Template:Math onto Template:Math can be decomposed into a direction and a scalar magnitude by writing it as ${𝐚}_1=a_1\widehat{𝐛}$ where $a_1$ is a scalar, called the scalar projection of Template:Math onto Template:Math, and Template:Math is the unit vector in the direction of Template:Math. The scalar projection is defined as $$a_1=\Vert 𝐚\Vert \cos \theta =𝐚\cdot \widehat{𝐛}$$ where the operator ⋅ denotes a dot product, ‖a‖ is the length of Template:Math, and θ is the angle between Template:Math and Template:Math. The scalar projection is equal in absolute value to the length of the vector projection, with a minus sign if the direction of the projection is opposite to the direction of Template:Math, that is, if the angle between the vectors is more than 90 degrees.

The vector projection can be calculated using the dot product of $𝐚$ and $𝐛$ as: $${\mathrm{proj}}_{𝐛}𝐚=(𝐚\cdot \widehat{𝐛})\widehat{𝐛}=\frac{𝐚\cdot 𝐛}{\Vert 𝐛\Vert }\frac{𝐛}{\Vert 𝐛\Vert }=\frac{𝐚\cdot 𝐛}{{\Vert 𝐛\Vert }^2}𝐛=\frac{𝐚\cdot 𝐛}{𝐛\cdot 𝐛}𝐛.$$

Notation

This article uses the convention that vectors are denoted in a bold font (e.g. Template:Math), and scalars are written in normal font (e.g. a₁).

The dot product of vectors Template:Math and Template:Math is written as $𝐚\cdot 𝐛$, the norm of Template:Math is written ‖a‖, the angle between Template:Math and Template:Math is denoted θ.

Definitions based on angle θ

Scalar projection

Template:Main The scalar projection of Template:Math on Template:Math is a scalar equal to $$a_1=\Vert 𝐚\Vert \cos \theta ,$$ where θ is the angle between Template:Math and Template:Math.

A scalar projection can be used as a scale factor to compute the corresponding vector projection.

Vector projection

The vector projection of Template:Math on Template:Math is a vector whose magnitude is the scalar projection of Template:Math on Template:Math with the same direction as Template:Math. Namely, it is defined as $${𝐚}_1=a_1\widehat{𝐛}=(\Vert 𝐚\Vert \cos \theta )\widehat{𝐛}$$ where $a_1$ is the corresponding scalar projection, as defined above, and $\widehat{𝐛}$ is the unit vector with the same direction as Template:Math: $$\widehat{𝐛}=\frac{𝐛}{\Vert 𝐛\Vert }$$

Vector rejection

By definition, the vector rejection of Template:Math on Template:Math is: $${𝐚}_2=𝐚-{𝐚}_1$$

Hence, $${𝐚}_2=𝐚-(\Vert 𝐚\Vert \cos \theta )\widehat{𝐛}$$

Definitions in terms of a and b

When Template:Mvar is not known, the cosine of Template:Mvar can be computed in terms of Template:Math and Template:Math, by the following property of the dot product Template:Math $$𝐚\cdot 𝐛=\Vert 𝐚\Vert \Vert 𝐛\Vert \cos \theta$$

Scalar projection

By the above-mentioned property of the dot product, the definition of the scalar projection becomes: $$a_1=\Vert 𝐚\Vert \cos \theta =\frac{𝐚\cdot 𝐛}{\Vert 𝐛\Vert }.$$

In two dimensions, this becomes $$a_1=\frac{{𝐚}_x{𝐛}_x+{𝐚}_y{𝐛}_y}{\Vert 𝐛\Vert }.$$

Vector projection

Similarly, the definition of the vector projection of Template:Math onto Template:Math becomes: $${𝐚}_1=a_1\widehat{𝐛}=\frac{𝐚\cdot 𝐛}{\Vert 𝐛\Vert }\frac{𝐛}{\Vert 𝐛\Vert },$$ which is equivalent to either $${𝐚}_1=(𝐚\cdot \widehat{𝐛})\widehat{𝐛},$$ or $${𝐚}_1=\frac{𝐚\cdot 𝐛}{{\Vert 𝐛\Vert }^2}𝐛=\frac{𝐚\cdot 𝐛}{𝐛\cdot 𝐛}𝐛.$$

Scalar rejection

In two dimensions, the scalar rejection is equivalent to the projection of Template:Math onto ${𝐛}^{\perp }=\left(\begin{array}{cc}-{𝐛}_y& {𝐛}_x\end{array}\right)$, which is $𝐛=\left(\begin{array}{cc}{𝐛}_x& {𝐛}_y\end{array}\right)$ rotated 90° to the left. Hence, $$a_2=\Vert 𝐚\Vert \sin \theta =\frac{𝐚\cdot {𝐛}^{\perp }}{\Vert 𝐛\Vert }=\frac{{𝐚}_y{𝐛}_x-{𝐚}_x{𝐛}_y}{\Vert 𝐛\Vert }.$$

Such a dot product is called the "perp dot product."

Vector rejection

By definition, $${𝐚}_2=𝐚-{𝐚}_1$$

Hence, $${𝐚}_2=𝐚-\frac{𝐚\cdot 𝐛}{𝐛\cdot 𝐛}𝐛.$$

By using the Scalar rejection using the perp dot product this gives

$${𝐚}_2=\frac{𝐚\cdot {𝐛}^{\perp }}{𝐛\cdot 𝐛}{𝐛}^{\perp }$$

Properties

If 0° ≤ θ ≤ 90°, as in this case, the scalar projection of Template:Math on Template:Math coincides with the length of the vector projection.

Scalar projection

Template:Main The scalar projection Template:Math on Template:Math is a scalar which has a negative sign if 90 degrees < θ ≤ 180 degrees. It coincides with the length Template:Math of the vector projection if the angle is smaller than 90°. More exactly:

• Template:Math if Template:Math,
• Template:Math if Template:Math.

Vector projection

The vector projection of Template:Math on Template:Math is a vector Template:Math which is either null or parallel to Template:Math. More exactly:

• Template:Math if Template:Math,
• Template:Math and Template:Math have the same direction if Template:Math,
• Template:Math and Template:Math have opposite directions if Template:Math.

Vector rejection

The vector rejection of Template:Math on Template:Math is a vector Template:Math which is either null or orthogonal to Template:Math. More exactly:

• Template:Math if Template:Math or Template:Math,
• Template:Math is orthogonal to Template:Math if Template:Math,

Matrix representation

The orthogonal projection can be represented by a projection matrix. To project a vector onto the unit vector Template:Math, it would need to be multiplied with this projection matrix: $$P_{𝐚}=𝐚{𝐚}^{\mathsf{\text{T}}}=\left[\begin{array}{c}{a}_x\\ a_y\\ a_z\end{array}\right]\left[\begin{array}{ccc}{a}_x& a_y& a_z\end{array}\right]=\left[\begin{array}{ccc}{a}_x^2& a_x{a}_y& a_x{a}_z\\ a_x{a}_y& a_y^2& a_y{a}_z\\ a_x{a}_z& a_y{a}_z& a_z^2\end{array}\right]$$

Uses

The vector projection is an important operation in the Gram–Schmidt orthonormalization of vector space bases. It is also used in the separating axis theorem to detect whether two convex shapes intersect.

Generalizations

Since the notions of vector length and angle between vectors can be generalized to any n-dimensional inner product space, this is also true for the notions of orthogonal projection of a vector, projection of a vector onto another, and rejection of a vector from another.

In some cases, the inner product coincides with the dot product. Whenever they don't coincide, the inner product is used instead of the dot product in the formal definitions of projection and rejection. For a three-dimensional inner product space, the notions of projection of a vector onto another and rejection of a vector from another can be generalized to the notions of projection of a vector onto a plane, and rejection of a vector from a plane. The projection of a vector on a plane is its orthogonal projection on that plane. The rejection of a vector from a plane is its orthogonal projection on a straight line which is orthogonal to that plane. Both are vectors. The first is parallel to the plane, the second is orthogonal.

For a given vector and plane, the sum of projection and rejection is equal to the original vector. Similarly, for inner product spaces with more than three dimensions, the notions of projection onto a vector and rejection from a vector can be generalized to the notions of projection onto a hyperplane, and rejection from a hyperplane. In geometric algebra, they can be further generalized to the notions of projection and rejection of a general multivector onto/from any invertible k-blade.

See also

• Scalar projection
• Vector notation

External links

• Projection of a vector onto a plane