In linear algebra, an n-by-n square matrix A is called invertible (also nonsingular, nondegenerate or —rarely used— regular) if there exists an n-by-n square matrix B such that

where In denotes the n-by-n identity matrix and the multiplication used is ordinary matrix multiplication.[1] If this is the case, then the matrix B is uniquely determined by A, and is called the (multiplicative) inverse of A, denoted by A−1. Matrix inversion is the process of finding the inverse matrix of an invertible matrix.[citation needed]

Over a field, a square matrix that is not invertible is called singular or degenerate. A square matrix with entries in a field is singular if and only if its determinant is zero. Singular matrices are rare in the sense that if a square matrix's entries are randomly selected from any bounded region on the number line or complex plane, the probability that the matrix is singular is 0, that is, it will "almost never" be singular. Non-square matrices, i.e. m-by-n matrices for which mn, do not have an inverse. However, in some cases such a matrix may have a left inverse or right inverse. If A is m-by-n and the rank of A is equal to n, (nm), then A has a left inverse, an n-by-m matrix B such that BA = In. If A has rank m (mn), then it has a right inverse, an n-by-m matrix B such that AB = Im.

While the most common case is that of matrices over the real or complex numbers, all these definitions can be given for matrices over any algebraic structure equipped with addition and multiplication (i.e. rings). However, in the case of a ring being commutative, the condition for a square matrix to be invertible is that its determinant is invertible in the ring, which in general is a stricter requirement than it being nonzero. For a noncommutative ring, the usual determinant is not defined. The conditions for existence of left-inverse or right-inverse are more complicated, since a notion of rank does not exist over rings.

The set of n × n invertible matrices together with the operation of matrix multiplication and entries from ring R form a group, the general linear group of degree n, denoted GLn(R).

Properties edit

The invertible matrix theorem edit

Let A be a square n-by-n matrix over a field K (e.g., the field   of real numbers). The following statements are equivalent, i.e., they are either all true or all false for any given matrix:[2]

  • The matrix A has a left inverse under matrix multiplication (that is, there exists a B such that BA = I)
  • The matrix A has a right inverse under matrix multiplication (that is, there exists a C such that AC = I).
  • A is invertible, i.e. it has an inverse under matrix multiplication (that is, there exists a B such that AB = In = BA).
  • The linear transformation mapping x to Ax has a left inverse under function composition.
  • The linear transformation mapping x to Ax has a right inverse under function composition.
  • The linear transformation mapping x to Ax is invertible, that is, has an inverse under function composition.
  • A is row-equivalent to the n-by-n identity matrix In.
  • A is column-equivalent to the n-by-n identity matrix In.
  • A has n pivot positions.
  • A has full rank: rank A = n.
  • A has a trivial kernel: ker(A) = {0}.
  • The linear transformation mapping x to Ax is surjective; that is, the equation Ax = b has at least one solution for each b in Kn.
  • The linear transformation mapping x to Ax is injective; that is, the equation Ax = b has at most one solution for each b in Kn.
  • The linear transformation mapping x to Ax is bijective; that is, the equation Ax = b has exactly one solution for each b in Kn.
  • The columns of A are linearly independent.
  • The rows of A are linearly independent.
  • The columns of A span Kn.
  • The rows of A span Kn.
  • The columns of A form a basis of Kn.
  • The rows of A form a basis of Kn.
  • The determinant of A is nonzero: det A ≠ 0. (In general, a square matrix over a commutative ring is invertible if and only if its determinant is an element of that ring with a multiplicative inverse (i.e. a unit)
  • The number 0 is not an eigenvalue of A. (More generally, a number   is an eigenvalue of A if the matrix   is singular, where I is the identity matrix.)
  • The transpose AT is an invertible matrix.
  • The matrix A can be expressed as a finite product of elementary matrices.

Other properties edit

Furthermore, the following properties hold for an invertible matrix A:

  •  
  •   for nonzero scalar k
  •   if A has orthonormal columns, where + denotes the Moore–Penrose inverse and x is a vector
  •  
  • For any invertible n-by-n matrices A and B,   More generally, if   are invertible n-by-n matrices, then  
  •  

The rows of the inverse matrix V of a matrix U are orthonormal to the columns of U (and vice versa interchanging rows for columns). To see this, suppose that UV = VU = I where the rows of V are denoted as   and the columns of U as   for   Then clearly, the Euclidean inner product of any two   This property can also be useful in constructing the inverse of a square matrix in some instances, where a set of orthogonal vectors (but not necessarily orthonormal vectors) to the columns of U are known. In which case, one can apply the iterative Gram–Schmidt process to this initial set to determine the rows of the inverse V.

A matrix that is its own inverse (i.e., a matrix A such that A = A−1, and consequently A2 = I), is called an involutory matrix.

In relation to its adjugate edit

The adjugate of a matrix A can be used to find the inverse of A as follows:

If A is an invertible matrix, then

 

In relation to the identity matrix edit

It follows from the associativity of matrix multiplication that if

 

for finite square matrices A and B, then also

 [3]

Density edit

Over the field of real numbers, the set of singular n-by-n matrices, considered as a subset of   is a null set, that is, has Lebesgue measure zero. This is true because singular matrices are the roots of the determinant function. This is a continuous function because it is a polynomial in the entries of the matrix. Thus in the language of measure theory, almost all n-by-n matrices are invertible.

Furthermore, the n-by-n invertible matrices are a dense open set in the topological space of all n-by-n matrices. Equivalently, the set of singular matrices is closed and nowhere dense in the space of n-by-n matrices.

In practice however, one may encounter non-invertible matrices. And in numerical calculations, matrices which are invertible, but close to a non-invertible matrix, can still be problematic; such matrices are said to be ill-conditioned.

Examples edit

An example with rank of n − 1 is a non-invertible matrix

 

We can see the rank of this 2-by-2 matrix is 1, which is n − 1 ≠ n, so it is non-invertible.

Consider the following 2-by-2 matrix:

 

The matrix   is invertible. To check this, one can compute that  , which is non-zero.

As an example of a non-invertible, or singular, matrix, consider the matrix

 

The determinant of   is 0, which is a necessary and sufficient condition for a matrix to be non-invertible.

Methods of matrix inversion edit

Gaussian elimination edit

Gaussian elimination is a useful and easy way to compute the inverse of a matrix. To compute a matrix inverse using this method, an augmented matrix is first created with the left side being the matrix to invert and the right side being the identity matrix. Then, Gaussian elimination is used to convert the left side into the identity matrix, which causes the right side to become the inverse of the input matrix.

For example, take the following matrix:  

The first step to compute its inverse is to create the augmented matrix  

Call the first row of this matrix   and the second row  . Then, add row 1 to row 2   This yields  

Next, subtract row 2, multiplied by 3, from row 1   which yields  

Finally, multiply row 1 by −1   and row 2 by 2   This yields the identity matrix on the left side and the inverse matrix on the right: 

Thus,  

The reason it works is that the process of Gaussian elimination can be viewed as a sequence of applying left matrix multiplication using elementary row operations using elementary matrices ( ), such as  

Applying right-multiplication using   we get   And the right side   which is the inverse we want.

To obtain   we create the augumented matrix by combining A with I and applying Gaussian elimination. The two portions will be transformed using the same sequence of elementary row operations. When the left portion becomes I, the right portion applied the same elementary row operation sequence will become A−1.

Newton's method edit

A generalization of Newton's method as used for a multiplicative inverse algorithm may be convenient, if it is convenient to find a suitable starting seed:

 

Victor Pan and John Reif have done work that includes ways of generating a starting seed.[4][5] Byte magazine summarised one of their approaches.[6]

Newton's method is particularly useful when dealing with families of related matrices that behave enough like the sequence manufactured for the homotopy above: sometimes a good starting point for refining an approximation for the new inverse can be the already obtained inverse of a previous matrix that nearly matches the current matrix, for example, the pair of sequences of inverse matrices used in obtaining matrix square roots by Denman–Beavers iteration; this may need more than one pass of the iteration at each new matrix, if they are not close enough together for just one to be enough. Newton's method is also useful for "touch up" corrections to the Gauss–Jordan algorithm which has been contaminated by small errors due to imperfect computer arithmetic.

Cayley–Hamilton method edit

The Cayley–Hamilton theorem allows the inverse of A to be expressed in terms of det(A), traces and powers of A:[7]

 

where n is size of A, and tr(A) is the trace of matrix A given by the sum of the main diagonal. The sum is taken over s and the sets of all   satisfying the linear Diophantine equation

 

The formula can be rewritten in terms of complete Bell polynomials of arguments   as

 

This is described in more detail under Cayley–Hamilton method.

Eigendecomposition edit

If matrix A can be eigendecomposed, and if none of its eigenvalues are zero, then A is invertible and its inverse is given by

 

where Q is the square (N × N) matrix whose ith column is the eigenvector   of A, and Λ is the diagonal matrix whose diagonal entries are the corresponding eigenvalues, that is,   If A is symmetric, Q is guaranteed to be an orthogonal matrix, therefore   Furthermore, because Λ is a diagonal matrix, its inverse is easy to calculate:

 

Cholesky decomposition edit

If matrix A is positive definite, then its inverse can be obtained as

 

where L is the lower triangular Cholesky decomposition of A, and L* denotes the conjugate transpose of L.

Analytic solution edit

Writing the transpose of the matrix of cofactors, known as an adjugate matrix, can also be an efficient way to calculate the inverse of small matrices, but this recursive method is inefficient for large matrices. To determine the inverse, we calculate a matrix of cofactors:

 

so that

 

where |A| is the determinant of A, C is the matrix of cofactors, and CT represents the matrix transpose.

Inversion of 2 × 2 matrices edit

The cofactor equation listed above yields the following result for 2 × 2 matrices. Inversion of these matrices can be done as follows:[8]

 

This is possible because 1/(adbc) is the reciprocal of the determinant of the matrix in question, and the same strategy could be used for other matrix sizes.

The Cayley–Hamilton method gives

 

Inversion of 3 × 3 matrices edit

A computationally efficient 3 × 3 matrix inversion is given by

 

(where the scalar A is not to be confused with the matrix A).

If the determinant is non-zero, the matrix is invertible, with the entries of the intermediary matrix on the right side above given by

 

The determinant of A can be computed by applying the rule of Sarrus as follows:

 

The Cayley–Hamilton decomposition gives

 

The general 3 × 3 inverse can be expressed concisely in terms of the cross product and triple product. If a matrix   (consisting of three column vectors,  ,  , and  ) is invertible, its inverse is given by

 

The determinant of A, det(A), is equal to the triple product of x0, x1, and x2—the volume of the parallelepiped formed by the rows or columns:

 

The correctness of the formula can be checked by using cross- and triple-product properties and by noting that for groups, left and right inverses always coincide. Intuitively, because of the cross products, each row of A–1 is orthogonal to the non-corresponding two columns of A (causing the off-diagonal terms of   be zero). Dividing by

 

causes the diagonal entries of I = A−1A to be unity. For example, the first diagonal is:

 

Inversion of 4 × 4 matrices edit

With increasing dimension, expressions for the inverse of A get complicated. For n = 4, the Cayley–Hamilton method leads to an expression that is still tractable:

 

Blockwise inversion edit

Matrices can also be inverted blockwise by using the following analytic inversion formula:[9]

 

 

 

 

 

(1)

where A, B, C and D are matrix sub-blocks of arbitrary size. (A must be square, so that it can be inverted. Furthermore, A and DCA−1B must be nonsingular.[10]) This strategy is particularly advantageous if A is diagonal and DCA−1B (the Schur complement of A) is a small matrix, since they are the only matrices requiring inversion.

This technique was reinvented several times and is due to Hans Boltz (1923),[citation needed] who used it for the inversion of geodetic matrices, and Tadeusz Banachiewicz (1937), who generalized it and proved its correctness.

The nullity theorem says that the nullity of A equals the nullity of the sub-block in the lower right of the inverse matrix, and that the nullity of B equals the nullity of the sub-block in the upper right of the inverse matrix.

The inversion procedure that led to Equation (1) performed matrix block operations that operated on C and D first. Instead, if A and B are operated on first, and provided D and ABD−1C are nonsingular,[11] the result is

 

 

 

 

 

(2)

Equating Equations (1) and (2) leads to

 

 

 

 

 

(3)

where Equation (3) is the Woodbury matrix identity, which is equivalent to the binomial inverse theorem.

If A and D are both invertible, then the above two block matrix inverses can be combined to provide the simple factorization

 

 

 

 

 

(2)

By the Weinstein–Aronszajn identity, one of the two matrices in the block-diagonal matrix is invertible exactly when the other is.

Since a blockwise inversion of an n × n matrix requires inversion of two half-sized matrices and 6 multiplications between two half-sized matrices, it can be shown that a divide and conquer algorithm that uses blockwise inversion to invert a matrix runs with the same time complexity as the matrix multiplication algorithm that is used internally.[12] Research into matrix multiplication complexity shows that there exist matrix multiplication algorithms with a complexity of O(n2.3727) operations, while the best proven lower bound is Ω(n2 log n).[13]

This formula simplifies significantly when the upper right block matrix B is the zero matrix. This formulation is useful when the matrices A and D have relatively simple inverse formulas (or pseudo inverses in the case where the blocks are not all square. In this special case, the block matrix inversion formula stated in full generality above becomes

 

By Neumann series edit

If a matrix A has the property that

 

then A is nonsingular and its inverse may be expressed by a Neumann series:[14]

 

Truncating the sum results in an "approximate" inverse which may be useful as a preconditioner. Note that a truncated series can be accelerated exponentially by noting that the Neumann series is a geometric sum. As such, it satisfies

 .

Therefore, only 2L − 2 matrix multiplications are needed to compute 2L terms of the sum.

More generally, if A is "near" the invertible matrix X in the sense that

 

then A is nonsingular and its inverse is

 

If it is also the case that AX has rank 1 then this simplifies to

 

p-adic approximation edit

If A is a matrix with integer or rational entries and we seek a solution in arbitrary-precision rationals, then a p-adic approximation method converges to an exact solution in O(n4 log2 n), assuming standard O(n3) matrix multiplication is used.[15] The method relies on solving n linear systems via Dixon's method of p-adic approximation (each in O(n3 log2 n)) and is available as such in software specialized in arbitrary-precision matrix operations, for example, in IML.[16]

Reciprocal basis vectors method edit

Given an n × n square matrix  ,  , with n rows interpreted as n vectors   (Einstein summation assumed) where the   are a standard orthonormal basis of Euclidean space   ( ), then using Clifford algebra (or geometric algebra) we compute the reciprocal (sometimes called dual) column vectors:

 

as the columns of the inverse matrix   Note that, the place " " indicates that " " is removed from that place in the above expression for  . We then have  , where   is the Kronecker delta. We also have  , as required. If the vectors   are not linearly independent, then   and the matrix   is not invertible (has no inverse).

Derivative of the matrix inverse edit

Suppose that the invertible matrix A depends on a parameter t. Then the derivative of the inverse of A with respect to t is given by[17]

 

To derive the above expression for the derivative of the inverse of A, one can differentiate the definition of the matrix inverse   and then solve for the inverse of A:

 

Subtracting   from both sides of the above and multiplying on the right by   gives the correct expression for the derivative of the inverse:

 

Similarly, if   is a small number then

 

More generally, if

 

then,

 

Given a positive integer  ,

 

Therefore,

 

Generalized inverse edit

Some of the properties of inverse matrices are shared by generalized inverses (for example, the Moore–Penrose inverse), which can be defined for any m-by-n matrix.[18]

Applications edit

For most practical applications, it is not necessary to invert a matrix to solve a system of linear equations; however, for a unique solution, it is necessary that the matrix involved be invertible.

Decomposition techniques like LU decomposition are much faster than inversion, and various fast algorithms for special classes of linear systems have also been developed.

Invertible Matrices in Engineering edit

1. Structural Analysis edit

In structural engineering, invertible matrices prove invaluable when analyzing the stability and behavior of structures. Transforming the structural equations into matrix form allows engineers to efficiently solve complex problems and ensure the safety of designs.

2. Control Systems edit

In control engineering, invertible matrices are utilized to model and analyze dynamic systems. Their application enables engineers to design control systems that respond predictably to various inputs, ensuring stability and optimal performance.

Invertible Matrices in Computer Science edit

1. Graphics and Image Processing edit

In computer graphics and image processing, invertible matrices are fundamental for transformations. Whether rotating, scaling, or translating images, these matrices facilitate seamless manipulation, contributing to the creation of visually appealing graphics.

2. Cryptography edit

The world of cybersecurity relies on invertible matrices for encryption and decryption processes. Algorithms that involve matrix operations play a crucial role in securing sensitive information, making invertible matrices indispensable in cryptographic protocols.

Invertible Matrices in Economics and Finance edit

1. Input-Output Analysis edit

Economists use invertible matrices to model input-output relationships in various industries. This application aids in understanding the interconnectedness of different sectors, enabling policymakers to make informed decisions.

2. Portfolio Optimization edit

In finance, invertible matrices find applications in portfolio optimization. Efficiently managing diverse assets and risk requires mathematical models that involve invertible matrices, ensuring robust financial strategies.

Invertible Matrices in Data Science edit

1. Machine Learning Algorithms edit

The backbone of many machine learning algorithms is formed by invertible matrices. From feature engineering to training models, these matrices contribute to the stability and efficiency of algorithms, enhancing predictive accuracy.

2. Dimensionality Reduction edit

In data science, invertible matrices are employed for dimensionality reduction techniques. Simplifying complex datasets without losing crucial information is achieved through the application of these matrices, aiding in more manageable and insightful analyses.

Invertible Matrices in Statistics edit

1. Regression Analysis edit

Statisticians utilize invertible matrices in regression analysis, providing a robust framework for modeling relationships between variables. This application ensures accurate and reliable predictions based on statistical data.

2. Experimental Design edit

In experimental design, invertible matrices play a role in constructing efficient experimental plans. Optimizing resources and obtaining meaningful results are achieved through the application of these matrices, enhancing the scientific validity of experiments.

Regression/least squares edit

Although an explicit inverse is not necessary to estimate the vector of unknowns, it is the easiest way to estimate their accuracy, found in the diagonal of a matrix inverse (the posterior covariance matrix of the vector of unknowns). However, faster algorithms to compute only the diagonal entries of a matrix inverse are known in many cases.[19]

Matrix inverses in real-time simulations edit

Matrix inversion plays a significant role in computer graphics, particularly in 3D graphics rendering and 3D simulations. Examples include screen-to-world ray casting, world-to-subspace-to-world object transformations, and physical simulations.

Matrix inverses in MIMO wireless communication edit

Matrix inversion also plays a significant role in the MIMO (Multiple-Input, Multiple-Output) technology in wireless communications. The MIMO system consists of N transmit and M receive antennas. Unique signals, occupying the same frequency band, are sent via N transmit antennas and are received via M receive antennas. The signal arriving at each receive antenna will be a linear combination of the N transmitted signals forming an N × M transmission matrix H. It is crucial for the matrix H to be invertible for the receiver to be able to figure out the transmitted information.

See also edit

References edit

  1. ^ Axler, Sheldon (18 December 2014). Linear Algebra Done Right. Undergraduate Texts in Mathematics (3rd ed.). Springer Publishing (published 2015). p. 296. ISBN 978-3-319-11079-0.
  2. ^ Weisstein, Eric W. "Invertible Matrix Theorem". mathworld.wolfram.com. Retrieved 2020-09-08.
  3. ^ Horn, Roger A.; Johnson, Charles R. (1985). Matrix Analysis. Cambridge University Press. p. 14. ISBN 978-0-521-38632-6..
  4. ^ Pan, Victor; Reif, John (1985), Efficient Parallel Solution of Linear Systems, Proceedings of the 17th Annual ACM Symposium on Theory of Computing, Providence: ACM
  5. ^ Pan, Victor; Reif, John (1985), Harvard University Center for Research in Computing Technology Report TR-02-85, Cambridge, MA: Aiken Computation Laboratory
  6. ^ "The Inversion of Large Matrices". Byte Magazine. 11 (4): 181–190. April 1986.
  7. ^ A proof can be found in the Appendix B of Kondratyuk, L. A.; Krivoruchenko, M. I. (1992). "Superconducting quark matter in SU(2) color group". Zeitschrift für Physik A. 344 (1): 99–115. Bibcode:1992ZPhyA.344...99K. doi:10.1007/BF01291027. S2CID 120467300.
  8. ^ Strang, Gilbert (2003). Introduction to linear algebra (3rd ed.). SIAM. p. 71. ISBN 978-0-9614088-9-3., Chapter 2, page 71
  9. ^ Tzon-Tzer, Lu; Sheng-Hua, Shiou (2002). "Inverses of 2 × 2 block matrices". Computers & Mathematics with Applications. 43 (1–2): 119–129. doi:10.1016/S0898-1221(01)00278-4.
  10. ^ Bernstein, Dennis (2005). Matrix Mathematics. Princeton University Press. p. 44. ISBN 978-0-691-11802-4.
  11. ^ Bernstein, Dennis (2005). Matrix Mathematics. Princeton University Press. p. 45. ISBN 978-0-691-11802-4.
  12. ^ T. H. Cormen, C. E. Leiserson, R. L. Rivest, C. Stein, Introduction to Algorithms, 3rd ed., MIT Press, Cambridge, MA, 2009, §28.2.
  13. ^ Ran Raz. On the complexity of matrix product. In Proceedings of the thirty-fourth annual ACM symposium on Theory of computing. ACM Press, 2002. doi:10.1145/509907.509932.
  14. ^ Stewart, Gilbert (1998). Matrix Algorithms: Basic decompositions. SIAM. p. 55. ISBN 978-0-89871-414-2.
  15. ^ Haramoto, H.; Matsumoto, M. (2009). "A p-adic algorithm for computing the inverse of integer matrices". Journal of Computational and Applied Mathematics. 225 (1): 320–322. Bibcode:2009JCoAM.225..320H. doi:10.1016/j.cam.2008.07.044.
  16. ^ "IML - Integer Matrix Library". cs.uwaterloo.ca. Retrieved 14 April 2018.
  17. ^ Magnus, Jan R.; Neudecker, Heinz (1999). Matrix Differential Calculus : with Applications in Statistics and Econometrics (Revised ed.). New York: John Wiley & Sons. pp. 151–152. ISBN 0-471-98633-X.
  18. ^ Roman, Stephen (2008), Advanced Linear Algebra, Graduate Texts in Mathematics (Third ed.), Springer, p. 446, ISBN 978-0-387-72828-5.
  19. ^ Lin, Lin; Lu, Jianfeng; Ying, Lexing; Car, Roberto; E, Weinan (2009). "Fast algorithm for extracting the diagonal of the inverse matrix with application to the electronic structure analysis of metallic systems". Communications in Mathematical Sciences. 7 (3): 755–777. doi:10.4310/CMS.2009.v7.n3.a12.

Further reading edit

External links edit