Difference between revisions of "Matrix"
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Matrix multiplication is [[associativity|associative]]. However, matrix multiplication is ''not'' [[commutativity|commutative]]. That is, it is possible for <math>AB \neq BA</math>. | Matrix multiplication is [[associativity|associative]]. However, matrix multiplication is ''not'' [[commutativity|commutative]]. That is, it is possible for <math>AB \neq BA</math>. | ||
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| + | The <math>n \times n</math> [[identity matrix]] <math>I_n</math> satisfies the property: | ||
| + | <center><math>AI_n = I_n A = A</math></center> | ||
| + | for all <math>n \times n</math> matrices <math>A</math>. | ||
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| + | Moreover, every square matrix <math>A</math> with nonzero [[determinant]] has an inverse matrix <math>B</math> such that | ||
| + | <center><math>AB = BA = I_n</math></center> | ||
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| + | This means that for every positive integer <math>n</math>, the set of all <math>n \times n</math> matrices with nonzero determinant form a [[group]] under matrix multiplication. This group is known as the [[general linear group]] <math>GL_{n}(\mathbb{R})</math>. | ||
===Matrix concepts=== | ===Matrix concepts=== | ||
Revision as of 00:53, July 1, 2012
For the 1999 film, see The Matrix.
A matrix (pl.: "matrices," Latin origin) is a complex ordering, in deliberate fashion, of numerals. In mathematics, a "matrix" is a regular grid of numbers, which may be manipulated and solved through intermediate-level algebra. Matrix algebra is usually taught in sophomore high school level mathematics.
More formally, a matrix is an example of a rank-2 tensor.
Alternately, a matrix may also be a complex ordering of a group of equivalent objects, especially where the order is imposed to gain incidental benefit from the synergy of the networked objects.
Mathematics
In mathematics, matrices can be manipulated in a variety of ways, including addition and multiplication.
Addition of matrices
To add two matrices, one would add their respective elements. For example:
would equal
Multiplication of matrices
To multiply two matrices, use the rule for finding the product of two matrices:
However, not every pair of matrices can be multiplied. In order for matrices and to be compatible for multiplication, the number of columns in must equal the number of rows in . If is an matrix and is an matrix, the product matrix will have rows and columns.
Matrix multiplication is associative. However, matrix multiplication is not commutative. That is, it is possible for .
The identity matrix satisfies the property:
for all matrices .
Moreover, every square matrix Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle A} with nonzero determinant has an inverse matrix Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle B} such that
This means that for every positive integer Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle n} , the set of all Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle n \times n} matrices with nonzero determinant form a group under matrix multiplication. This group is known as the general linear group Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle GL_{n}(\mathbb{R})} .
Matrix concepts
Basic concepts
- Adjoint
- Determinant
- Diagonal matrix
- Identity matrix
- Inverse matrix
- Null, column and row space
- Scalar
- Trace
- Transpose matrix
- Vector
- Zero matrix
Advanced concepts
- Basis
- Diagonalizable
- Eigenspace
- Eigenvalue
- Eigenvector
- Gram-Schmidt process
- Hermitian matrix
- Jordan canonical form
- Laplacian
- Linear independence
- Matrix diagonalization
- Matrix reformation
- Matrix transformation
- Orthogonal matrix
- Orthonormal matrix
- Resultant
- Span
- Systems of linear equations
- Transcriptor
- Wronskian