In linear algebra and functional analysis, a **projection** is a linear transformation from a vector space to itself (an endomorphism) such that . That is, whenever is applied twice to any vector, it gives the same result as if it were applied once (i.e. is idempotent). It leaves its image unchanged.^{ [1] } This definition of "projection" formalizes and generalizes the idea of graphical projection. One can also consider the effect of a projection on a geometrical object by examining the effect of the projection on points in the object.

- Definitions
- Projection matrix
- Examples
- Orthogonal projection
- Oblique projection
- Properties and classification
- Idempotence
- Open map
- Complementarity of image and kernel
- Spectrum
- Product of projections
- Orthogonal projections
- Oblique projections
- Finding projection with an inner product
- Canonical forms
- Projections on normed vector spaces
- Applications and further considerations
- Generalizations
- See also
- Notes
- References
- External links

A **projection** on a vector space is a linear operator such that .

When has an inner product and is complete (i.e. when is a Hilbert space) the concept of orthogonality can be used. A projection on a Hilbert space is called an **orthogonal projection** if it satisfies for all . A projection on a Hilbert space that is not orthogonal is called an **oblique projection**.

- In the finite-dimensional case, a square matrix is called a
**projection matrix**if it is equal to its square, i.e. if .^{ [2] }^{: p. 38 } - A square matrix is called an
**orthogonal projection matrix**if for a real matrix, and respectively for a complex matrix, where denotes the transpose of and denotes the adjoint or Hermitian transpose of .^{ [2] }^{: p. 223 } - A projection matrix that is not an orthogonal projection matrix is called an
**oblique projection matrix**.

The eigenvalues of a projection matrix must be 0 or 1.

For example, the function which maps the point in three-dimensional space to the point is an orthogonal projection onto the *xy*-plane. This function is represented by the matrix

The action of this matrix on an arbitrary vector is

To see that is indeed a projection, i.e., , we compute

Observing that shows that the projection is an orthogonal projection.

A simple example of a non-orthogonal (oblique) projection is

Via matrix multiplication, one sees that

showing that is indeed a projection.

The projection is orthogonal if and only if because only then

By definition, a projection is idempotent (i.e. ).

Every projection is an open map, meaning that it maps each open set in the domain to an open set in the subspace topology of the image.^{[ citation needed ]} That is, for any vector and any ball (with positive radius) centered on , there exists a ball (with positive radius) centered on that is wholly contained in the image .

Let be a finite-dimensional vector space and be a projection on . Suppose the subspaces and are the image and kernel of respectively. Then has the following properties:

- is the identity operator on :
- We have a direct sum . Every vector may be decomposed uniquely as with and , and where

The image and kernel of a projection are *complementary*, as are and . The operator is also a projection as the image and kernel of become the kernel and image of and vice versa. We say is a projection along onto (kernel/image) and is a projection along onto .

In infinite-dimensional vector spaces, the spectrum of a projection is contained in as

Only 0 or 1 can be an eigenvalue of a projection. This implies that an orthogonal projection is always a positive semi-definite matrix. In general, the corresponding eigenspaces are (respectively) the kernel and range of the projection. Decomposition of a vector space into direct sums is not unique. Therefore, given a subspace , there may be many projections whose range (or kernel) is .

If a projection is nontrivial it has minimal polynomial , which factors into distinct linear factors, and thus is diagonalizable.

The product of projections is not in general a projection, even if they are orthogonal. If two projections commute then their product is a projection, but the converse is false: the product of two non-commuting projections may be a projection.

If two orthogonal projections commute then their product is an orthogonal projection. If the product of two orthogonal projections is an orthogonal projection, then the two orthogonal projections commute (more generally: two self-adjoint endomorphisms commute if and only if their product is self-adjoint).

When the vector space has an inner product and is complete (is a Hilbert space) the concept of orthogonality can be used. An **orthogonal projection** is a projection for which the range and the null space are orthogonal subspaces. Thus, for every and in , . Equivalently:

A projection is orthogonal if and only if it is self-adjoint. Using the self-adjoint and idempotent properties of , for any and in we have , , and

where is the inner product associated with . Therefore, and are orthogonal projections.^{ [3] } The other direction, namely that if is orthogonal then it is self-adjoint, follows from

for every and in ; thus .

Let be a complete metric space with an inner product, and let be a closed linear subspace of (and hence complete as well).

For every the following set of non-negative norm-values has an infimum, and due to the completeness of it is a minimum. We define as the point in where this minimum is obtained.

Obviously is in . It remains to show that satisfies and that it is linear.

Let us define . For every non-zero in , the following holds:

By defining we see that unless vanishes. Since was chosen as the minimum of the aforementioned set, it follows that indeed vanishes. In particular, (for ): .

Linearity follows from the vanishing of for every :

By taking the difference between the equations we have

But since we may choose (as it is itself in ) it follows that . Similarly we have for every scalar .

An orthogonal projection is a bounded operator. This is because for every in the vector space we have, by the Cauchy–Schwarz inequality:

Thus .

For finite-dimensional complex or real vector spaces, the standard inner product can be substituted for .

A simple case occurs when the orthogonal projection is onto a line. If is a unit vector on the line, then the projection is given by the outer product

(If is complex-valued, the transpose in the above equation is replaced by a Hermitian transpose). This operator leaves **u** invariant, and it annihilates all vectors orthogonal to , proving that it is indeed the orthogonal projection onto the line containing **u**.^{ [4] } A simple way to see this is to consider an arbitrary vector as the sum of a component on the line (i.e. the projected vector we seek) and another perpendicular to it, . Applying projection, we get

by the properties of the dot product of parallel and perpendicular vectors.

This formula can be generalized to orthogonal projections on a subspace of arbitrary dimension. Let be an orthonormal basis of the subspace , and let denote the matrix whose columns are , i.e., . Then the projection is given by:^{ [5] }

which can be rewritten as

The matrix is the partial isometry that vanishes on the orthogonal complement of and is the isometry that embeds into the underlying vector space. The range of is therefore the *final space* of . It is also clear that is the identity operator on .

The orthonormality condition can also be dropped. If is a (not necessarily orthonormal) basis, and is the matrix with these vectors as columns, then the projection is:^{ [6] }^{ [7] }

The matrix still embeds into the underlying vector space but is no longer an isometry in general. The matrix is a "normalizing factor" that recovers the norm. For example, the rank-1 operator is not a projection if After dividing by we obtain the projection onto the subspace spanned by .

In the general case, we can have an arbitrary positive definite matrix defining an inner product , and the projection is given by . Then

When the range space of the projection is generated by a frame (i.e. the number of generators is greater than its dimension), the formula for the projection takes the form: . Here stands for the Moore–Penrose pseudoinverse. This is just one of many ways to construct the projection operator.

If is a non-singular matrix and (i.e., is the null space matrix of ),^{ [8] } the following holds:

If the orthogonal condition is enhanced to with non-singular, the following holds:

All these formulas also hold for complex inner product spaces, provided that the conjugate transpose is used instead of the transpose. Further details on sums of projectors can be found in Banerjee and Roy (2014).^{ [9] } Also see Banerjee (2004)^{ [10] } for application of sums of projectors in basic spherical trigonometry.

The term *oblique projections* is sometimes used to refer to non-orthogonal projections. These projections are also used to represent spatial figures in two-dimensional drawings (see oblique projection), though not as frequently as orthogonal projections. Whereas calculating the fitted value of an ordinary least squares regression requires an orthogonal projection, calculating the fitted value of an instrumental variables regression requires an oblique projection.

Projections are defined by their null space and the basis vectors used to characterize their range (which is the complement of the null space). When these basis vectors are orthogonal to the null space, then the projection is an orthogonal projection. When these basis vectors are not orthogonal to the null space, the projection is an oblique projection. Let the vectors form a basis for the range of the projection, and assemble these vectors in the matrix . The range and the null space are complementary spaces, so the null space has dimension . It follows that the orthogonal complement of the null space has dimension . Let form a basis for the orthogonal complement of the null space of the projection, and assemble these vectors in the matrix . Then the projection is defined by

This expression generalizes the formula for orthogonal projections given above.^{ [11] }^{ [12] }

Note that is also a oblique projection. The singular values of and can be computed by an orthonormal basis of . Let be an orthonormal basis of and let be the orthogonal complement of . Denote the singular values of the matrix by the positive values . With this, the singular values for are:^{ [13] }

and the singular values for are

This implies that the largest singular values of and are equal, and thus that the matrix norm of the oblique projections are the same. However, the condition number satisfies the relation , and is therefore not necessarily equal.

Let be a vector space (in this case a plane) spanned by orthogonal vectors . Let be a vector. One can define a projection of onto as

where repeated indices are summed over (Einstein sum notation). The vector can be written as an orthogonal sum such that . is sometimes denoted as . There is a theorem in linear algebra that states that this is the smallest distance (the * orthogonal distance *) from to and is commonly used in areas such as machine learning.

Any projection on a vector space of dimension over a field is a diagonalizable matrix, since its minimal polynomial divides , which splits into distinct linear factors. Thus there exists a basis in which has the form

where is the rank of . Here is the identity matrix of size , and is the zero matrix of size . If the vector space is complex and equipped with an inner product, then there is an *orthonormal* basis in which the matrix of *P* is^{ [14] }

where . The integers and the real numbers are uniquely determined. Note that . The factor corresponds to the maximal invariant subspace on which acts as an *orthogonal* projection (so that *P* itself is orthogonal if and only if ) and the -blocks correspond to the *oblique* components.

When the underlying vector space is a (not necessarily finite-dimensional) normed vector space, analytic questions, irrelevant in the finite-dimensional case, need to be considered. Assume now is a Banach space.

Many of the algebraic results discussed above survive the passage to this context. A given direct sum decomposition of into complementary subspaces still specifies a projection, and vice versa. If is the direct sum , then the operator defined by is still a projection with range and kernel . It is also clear that . Conversely, if is projection on , i.e. , then it is easily verified that . In other words, is also a projection. The relation implies and is the direct sum .

However, in contrast to the finite-dimensional case, projections need not be continuous in general. If a subspace of is not closed in the norm topology, then the projection onto is not continuous. In other words, the range of a continuous projection must be a closed subspace. Furthermore, the kernel of a continuous projection (in fact, a continuous linear operator in general) is closed. Thus a *continuous* projection gives a decomposition of into two complementary *closed* subspaces: .

The converse holds also, with an additional assumption. Suppose is a closed subspace of . If there exists a closed subspace such that *X* = *U* ⊕ *V*, then the projection with range and kernel is continuous. This follows from the closed graph theorem. Suppose *x _{n}* →

The above argument makes use of the assumption that both and are closed. In general, given a closed subspace , there need not exist a complementary closed subspace , although for Hilbert spaces this can always be done by taking the orthogonal complement. For Banach spaces, a one-dimensional subspace always has a closed complementary subspace. This is an immediate consequence of Hahn–Banach theorem. Let be the linear span of . By Hahn–Banach, there exists a bounded linear functional such that *φ*(*u*) = 1. The operator satisfies , i.e. it is a projection. Boundedness of implies continuity of and therefore is a closed complementary subspace of .

Projections (orthogonal and otherwise) play a major role in algorithms for certain linear algebra problems:

- QR decomposition (see Householder transformation and Gram–Schmidt decomposition);
- Singular value decomposition
- Reduction to Hessenberg form (the first step in many eigenvalue algorithms)
- Linear regression
- Projective elements of matrix algebras are used in the construction of certain K-groups in Operator K-theory

As stated above, projections are a special case of idempotents. Analytically, orthogonal projections are non-commutative generalizations of characteristic functions. Idempotents are used in classifying, for instance, semisimple algebras, while measure theory begins with considering characteristic functions of measurable sets. Therefore, as one can imagine, projections are very often encountered in the context of operator algebras. In particular, a von Neumann algebra is generated by its complete lattice of projections.

More generally, given a map between normed vector spaces one can analogously ask for this map to be an isometry on the orthogonal complement of the kernel: that be an isometry (compare Partial isometry); in particular it must be onto. The case of an orthogonal projection is when *W* is a subspace of *V.* In Riemannian geometry, this is used in the definition of a Riemannian submersion.

- Centering matrix, which is an example of a projection matrix.
- Dykstra's projection algorithm to compute the projection onto an intersection of sets
- Invariant subspace
- Least-squares spectral analysis
- Orthogonalization
- Properties of trace

- ↑ Meyer, pp 386+387
- 1 2 Horn, Roger A.; Johnson, Charles R. (2013).
*Matrix Analysis, second edition*. Cambridge University Press. ISBN 9780521839402. - ↑ Meyer, p. 433
- ↑ Meyer, p. 431
- ↑ Meyer, equation (5.13.4)
- ↑ Banerjee, Sudipto; Roy, Anindya (2014),
*Linear Algebra and Matrix Analysis for Statistics*, Texts in Statistical Science (1st ed.), Chapman and Hall/CRC, ISBN 978-1420095388 - ↑ Meyer, equation (5.13.3)
- ↑ See also Linear least squares (mathematics) § Properties of the least-squares estimators.
- ↑ Banerjee, Sudipto; Roy, Anindya (2014),
*Linear Algebra and Matrix Analysis for Statistics*, Texts in Statistical Science (1st ed.), Chapman and Hall/CRC, ISBN 978-1420095388 - ↑ Banerjee, Sudipto (2004), "Revisiting Spherical Trigonometry with Orthogonal Projectors",
*The College Mathematics Journal*,**35**(5): 375–381, doi:10.1080/07468342.2004.11922099, S2CID 122277398 - ↑ Banerjee, Sudipto; Roy, Anindya (2014),
*Linear Algebra and Matrix Analysis for Statistics*, Texts in Statistical Science (1st ed.), Chapman and Hall/CRC, ISBN 978-1420095388 - ↑ Meyer, equation (7.10.39)
- ↑ Brust, J. J.; Marcia, R. F.; Petra, C. G. (2020), "Computationally Efficient Decompositions of Oblique Projection Matrices",
*SIAM Journal on Matrix Analysis and Applications*,**41**(2): 852–870, doi:10.1137/19M1288115 - ↑ Doković, D. Ž. (August 1991). "Unitary similarity of projectors".
*Aequationes Mathematicae*.**42**(1): 220–224. doi:10.1007/BF01818492. S2CID 122704926.

In mathematics, an **inner product space** is a real vector space or a complex vector space with an operation called an **inner product.** The inner product of two vectors in the space is a scalar, often denoted with angle brackets such as in . Inner products allow formal definitions of intuitive geometric notions, such as lengths, angles, and orthogonality of vectors. Inner product spaces generalize Euclidean vector spaces, in which the inner product is the dot product or *scalar product* of Cartesian coordinates. Inner product spaces of infinite dimension are widely used in functional analysis. Inner product spaces over the field of complex numbers are sometimes referred to as **unitary spaces**. The first usage of the concept of a vector space with an inner product is due to Giuseppe Peano, in 1898.

**Linear algebra** is the branch of mathematics concerning linear equations such as:

In mathematics, and more specifically in linear algebra, a **linear subspace**, also known as a **vector subspace** is a vector space that is a subset of some larger vector space. A linear subspace is usually simply called a *subspace* when the context serves to distinguish it from other types of subspaces.

In mathematics, particularly linear algebra and numerical analysis, the **Gram–Schmidt process** is a method for orthonormalizing a set of vectors in an inner product space, most commonly the Euclidean space **R**^{n} equipped with the standard inner product. The Gram–Schmidt process takes a finite, linearly independent set of vectors *S* = {**v**_{1}, ..., **v**_{k}} for *k* ≤ *n* and generates an orthogonal set *S′* = {**u**_{1}, ..., **u**_{k}} that spans the same *k*-dimensional subspace of **R**^{n} as *S*.

In linear algebra, the **column space** of a matrix *A* is the span of its column vectors. The column space of a matrix is the image or range of the corresponding matrix transformation.

In mathematics, the **dot product** or **scalar product** is an algebraic operation that takes two equal-length sequences of numbers, and returns a single number. In Euclidean geometry, the dot product of the Cartesian coordinates of two vectors is widely used. It is often called the **inner product** of Euclidean space, even though it is not the only inner product that can be defined on Euclidean space.

In mathematics, particularly in linear algebra, a **skew-symmetric****matrix** is a square matrix whose transpose equals its negative. That is, it satisfies the condition

In mathematics, a **Hermitian matrix** is a complex square matrix that is equal to its own conjugate transpose—that is, the element in the i-th row and j-th column is equal to the complex conjugate of the element in the j-th row and i-th column, for all indices i and j:

In mathematics, a **linear form** is a linear map from a vector space to its field of scalars.

In linear algebra, a **QR decomposition**, also known as a **QR factorization** or **QU factorization**, is a decomposition of a matrix *A* into a product *A* = *QR* of an orthogonal matrix *Q* and an upper triangular matrix *R*. QR decomposition is often used to solve the linear least squares problem and is the basis for a particular eigenvalue algorithm, the QR algorithm.

In linear algebra, a square matrix with complex entries is said to be **skew-Hermitian** or **anti-Hermitian** if its conjugate transpose is the negative of the original matrix. That is, the matrix is skew-Hermitian if it satisfies the relation

In linear algebra, linear transformations can be represented by matrices. If is a linear transformation mapping to and is a column vector with entries, then

In linear algebra, a **rotation matrix** is a transformation matrix that is used to perform a rotation in Euclidean space. For example, using the convention below, the matrix

In the mathematical fields of linear algebra and functional analysis, the **orthogonal complement** of a subspace *W* of a vector space *V* equipped with a bilinear form *B* is the set *W*^{⊥} of all vectors in *V* that are orthogonal to every vector in *W*. Informally, it is called the **perp**, short for **perpendicular complement**. It is a subspace of *V*.

In mathematics, the **kernel** of a linear map, also known as the **null space** or **nullspace**, is the linear subspace of the domain of the map which is mapped to the zero vector. That is, given a linear map *L* : *V* → *W* between two vector spaces V and W, the kernel of L is the vector space of all elements **v** of V such that *L*(**v**) = **0**, where **0** denotes the zero vector in W, or more symbolically:

In mathematics, the **conjugate gradient method** is an algorithm for the numerical solution of particular systems of linear equations, namely those whose matrix is positive-definite. The conjugate gradient method is often implemented as an iterative algorithm, applicable to sparse systems that are too large to be handled by a direct implementation or other direct methods such as the Cholesky decomposition. Large sparse systems often arise when numerically solving partial differential equations or optimization problems.

This article derives the main properties of rotations in 3-dimensional space.

In statistics and signal processing, the **orthogonality principle** is a necessary and sufficient condition for the optimality of a Bayesian estimator. Loosely stated, the orthogonality principle says that the error vector of the optimal estimator is orthogonal to any possible estimator. The orthogonality principle is most commonly stated for linear estimators, but more general formulations are possible. Since the principle is a necessary and sufficient condition for optimality, it can be used to find the minimum mean square error estimator.

In mathematics, **Hilbert spaces** allow generalizing the methods of linear algebra and calculus from (finite-dimensional) Euclidean vector spaces to spaces that may be infinite-dimensional. A Hilbert space is a vector space equipped with an inner product which defines a distance function for which it is a complete metric space. Hilbert spaces arise naturally and frequently in mathematics and physics, typically as function spaces.

In mathematics, **orthogonality** is the generalization of the geometric notion of *perpendicularity* to the linear algebra of bilinear forms.

- Banerjee, Sudipto; Roy, Anindya (2014),
*Linear Algebra and Matrix Analysis for Statistics*, Texts in Statistical Science (1st ed.), Chapman and Hall/CRC, ISBN 978-1420095388 - Dunford, N.; Schwartz, J. T. (1958).
*Linear Operators, Part I: General Theory*. Interscience. - Meyer, Carl D. (2000).
*Matrix Analysis and Applied Linear Algebra*. Society for Industrial and Applied Mathematics. ISBN 978-0-89871-454-8.

- MIT Linear Algebra Lecture on Projection Matrices on YouTube, from MIT OpenCourseWare
- Linear Algebra 15d: The Projection Transformation on YouTube, by Pavel Grinfeld.
- Planar Geometric Projections Tutorial – a simple-to-follow tutorial explaining the different types of planar geometric projections.

This page is based on this Wikipedia article

Text is available under the CC BY-SA 4.0 license; additional terms may apply.

Images, videos and audio are available under their respective licenses.

Text is available under the CC BY-SA 4.0 license; additional terms may apply.

Images, videos and audio are available under their respective licenses.