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matrixGaussianEliminate
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The matrixGaussianEliminate function triangulates the M by M+1 coefficient matrix representing
a system of M simultaneous linear equations in M variables.
The input argument {NumMatrix} must be an M by M+1 number matrix representing the original independent
variable observations with the dependent variable in the last column in the form of: The output will be the M by M+1 matrix after triangulation. The triangulated result matrix is
now ready for Gaussian substitution. When to use The matrixGaussianEliminate function is a non-destructive function useful
when you want to create a triangulated Gaussian matrix in preparation for primal form
regression. See Sedgewick[2] chap 37.
x x x x... y
x x x x... y
....
x x x x... y
(matrixGaussianEliminate NumMatrix) A new triangulated number Matrix object after Gaussian elimination.
Here are a number of links to Lambda coding examples which contain this instruction in various use cases.
Example_NumMatrix_matrixGaussianEliminate_001
Here are the links to the data types of the function arguments. Here are also a number of links to functions having arguments with any of these data types.
You can always talk with the AIS at aiserver.sourceforge.net.
Name
Description
AIS Types NumMatrix Matrix containing the original independent and dependent observations NumMatrix
Returns:
Examples
Argument Types
NumMatrix
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