
LINEAR ALGEBRA performs computations with real matrices. Solution of linear systems of equations (even least squares solution of overdetermined or inconsistent systems and solution by LU factors), matrix operations (add, subtract, multiply), finding the determinant, trace, inverse, adjoint, QR or LU factors, eigenvalues and eigenvectors, establish the definiteness of a symmetric matrix, perform scalar multiplication, transposition, shift, create matrices of zeroes or ones, identity, symmetric or general matrices.
LINEAR ALGEBRA performs the following tasks:
Add Finds the sum of two matrices.
Subtract Finds the difference of two matrices.
Multiply Finds the product of two matrices.
Determinant Finds the determinant of a square matrix.
Trace Finds the trace of a square matrix.
Inverse Finds the inverse of a square matrix, if it exists.
Inverse (Compact) Finds the inverse of a square matrix, if it exists. This version is somewhat faster and more memory efficient since it does not do the actual row exchanges for the pivoting, neither does it actually attach an identity matrix to the original one. The inverse computed is placed in the same array as the original matrix.
Adjoint Finds the adjoint of a square matrix.
Adjoint by Inverse Finds the adjoint of a square, nonsingular
matrix.
LU Factors  Finds the LU factors of a matrix [A], that is,
[L] and [U] such that [A]=[L][U], with [L] being lowertriangular and [U] being
uppertriangular. In reality, the factorization can't always be done without exchanging
the rows of [A] in some manner. Because of this, a permutation matrix [P]
is generally involved. This makes the actual factorization [A]=[P][L][U].
Also, since the program allows [A] to be rectangular, [U] in such case would not
be square so instead of uppertriangular we should more accurately call it uppertrapezoidal.
This program should be used before attempting to use the program which solves simultaneous
linear equations using LU factors.
QR Factors (GramSchmidt orthogonalization)  Factors a matrix
[A] with linearly independent columns into the product [A]=[Q][R], with [Q] having
orthonormal columns and [R] being uppertriangular and invertible. Since [A]
must have linearly independent columns, the number of columns can't exceed the number
of rows.
Test Definiteness Establishes the positive or negative definiteness,
positive or negative semidefiniteness, or indefiniteness of a symmetric matrix.
Simultaneous Linear Equations Solves systems of linear equations
using the method of Gaussian Elimination.
Simultaneous Linear Equations by LU Factors solves systems
of linear equations using the LU factorization of the matrix of coefficients. This
allows quicker solution of many systems that share the same matrix of coefficients
but have different righthand sides.
Simultaneous Linear Equations  Overdetermined or Inconsistent Systems
Finds the leastsquares solution to a system of linear equations which may
be inconsistent or overdetermined with more equations than unknowns.
Eigenvalues and Eigenvectors  Finds the real eigenvalues and
corresponding eigenvectors of a matrix. If the matrix has a full set of eigenvectors
(diagonalizable), then a full set will be found even if some eigenvalues are repeated.
Eigenvalues are found using the Shifted QR method. The corresponding eigenvectors
are found using the Shifted Inverse Power method. Matrices are first transformed
into their upperHessenberg form.
Transpose  Finds the transpose of a matrix.
Multiply by a Scalar Performs scalar multiplication.
Shift Shifts the diagonal elements of a matrix (useful in
the eigenvalue problem).
Create Matrix Creates matrices of zeroes, ones, identity matrices, symmetric, random or general matrices.
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See Also: Mathematical Functions  Ordinary Differential Equations
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