Inverse matrix Definition and 40 Threads

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





A
B

=

B
A

=


I


n





{\displaystyle \mathbf {AB} =\mathbf {BA} =\mathbf {I} _{n}\ }
where In denotes the n-by-n identity matrix and the multiplication used is ordinary matrix multiplication. 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 matrix B that satisfies the prior equation for a given invertible matrix A.
A square matrix that is not invertible is called singular or degenerate. A square matrix 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 finite 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 (m-by-n matrices for which m ≠ n) 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 (n ≤ m), then A has a left inverse, an n-by-m matrix B such that BA = In. If A has rank m (m ≤ n), 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 ring. However, in the case of the 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 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).

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  1. jv07cs

    I Do Metric Tensors Always Have Inverses?

    I am reading about musical isomorphisms and for the demonstration of the index raising operation from the sharp isomorphism, we have to multiply the equation by the inverse matrix of the metric. Can we assume that this inverse always exists? If so, how could I prove it?
  2. karush

    MHB 311.2.2.6 use inverse matrix to solve system of equations

    $\tiny{311.2.2.6}$ Use the inverse to solve the system $\begin{array}{rrrrr} 7x_1&+3x_2&=-9\\ -2x_1&+x_2&=10 \end{array}$ the thing I could not get here without a calculator is $A^{-1}$
  3. karush

    MHB Calculating the Inverse Matrix for a 3x3 Matrix

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  4. patric44

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  5. U

    A Solve Badly Scaled Problem with Newton-Chord Technique

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  6. M

    MHB Calculation of the inverse matrix - Number of operations

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  7. MrsM

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  8. D

    Find inverse matrix using determinants and adjoints

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  9. ognik

    MHB Uniqueness of Inverse Matrices: Proof and Explanation

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  10. B

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  11. S

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  12. M

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  13. I

    Inverse matrix word problem, matrix arithmetic

    Homework Statement Hello! Please, take a look at the attached picture - there is a quote of the exercise and below is my attempt to make a matrix. Is my matrix correct? I have tried many times to convert it to inverse one, but I can't figure out how to do it - I keep getting "inconvenient"...
  14. S

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  15. genxium

    Call for help in finding approximate inverse matrix

    I'm looking for solutions to this problem: Matrices A(m,n) and B(n,m) satisfy AB=I(m,m) where n isn't equal to m. Can I find a matrix S(m,n) such that SA=I(n,n) or SA approximates I(n,n)? By approximate I don't have preferred definition, hence any suggestion is welcome!
  16. Y

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  17. Z

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  18. D

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  19. E

    Inverse matrix mass computation

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  20. N

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  21. U

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  22. matqkks

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  23. P

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  24. T

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  25. B

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  26. Demon117

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  27. S

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  28. L

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  29. D

    Lineal Algebra: Inverse Matrix of Symmetric Matrix

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  30. S

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  31. P

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  32. N

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  33. U

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  34. Q

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  35. L

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  36. H

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  37. B

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  38. P

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  39. C

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  40. P

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