![]() In general, expect these functions to be slower than simply using built-ins unless you are sure that your sub-matrices (but not the full matrix) have the kind of structure exploited by mldivide. In certain cases, this means that the built-ins are able to exploit structure in the sub-matrices for very fast inversion and quickly combine the results together. These functions implement matrix inversion (`blockinv`) and division (`blockmldivide` and `blockmrdivide`) by extracting sub-matrices of a user-defined size and calling the matlab built-ins on them. A frequent misuse of inv arises when solving the system of linear equations. ![]() In practice, it is seldom necessary to form the explicit inverse of a matrix. A warning message is printed if X is badly scaled or nearly singular. Gauss-Elimination method allows us to create the upper triangular matrix, and it can be further used in augmentation with an identity matrix of the same order, to calculate the inverse of a given matrix. An extension of Gauss Elimination method, it computes the Inverse of a matrix. The functions provided here were initially written to support a latent Gaussian Process inference implementation, where we frequently encounter large matrices which have sub-matrices with "nice" structure, but the full matrix does not. Y inv(X) returns the inverse of the square matrix X. Gauss-Jordan Method for Matrix Inversion. See the algorithms section of the documentation on `mldivide` for more information: Matlab has very good built-in support for fast matrix inversion exploiting the structure of a matrix.
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