- Linear Systems (linalg)
- Matrix Functions (linalg)
- Solving Linear Systems (linalg)
- Fits and Regression Analysis (linalg)
- Norms (linalg)
- Matrix Functions (linalg)
- Eigenvalues and Singular Values (linalg)
- Gauss-Jordan Scheme (linalg)
- Additional Matrix Functions (linalg)

Linear algebra and regression routines.

This file contains basic linear algebra routines, like solutions of linear systems, linear fit, polynomial fit, eigenvalues and eigenspaces, decompositions, singular values, norms, and special matrices.

It does also contain methods to solve systems by hand (pivotize).

For an introduction and the core functions see the following links.

functionid(n:index, m:index=none)

Creates a nxn identity matrix. See:

diag (Euler Core),

diag (Maxima Documentation),

eye (Linear Algebra)

functioneye(n:positive integer, m:index=none)

Creates a nxm matrix with 1 on the diagonal If m=none, then it creates an nxn matrix. This is a Matlab function. See:

id (Linear Algebra)

functiondiagmatrix(v:vector, k:integer=0)

Create a diagonal square matrix with v on the k-th diagonal The size of the square matrix is chosen, such that the vector v fits into the matrix. diagmatrix(1:3) diag(3,3,0,1:3) // the same diagmatrix(1:3,1) diag(4,4,1,1:3) // the same See:

diag (Euler Core),

diag (Maxima Documentation)

functiontranspose(A)

returns A' Alias for Maxima

functionimage(A:complex, eps=none)

Computes the image of the quadratic matrix A. Returns a matrix, containing a basis of the image of A in the columns. The image of a matrix is the space spanned by its columns. This functions uses an LU decomposition with the Gauss algorithm to determine a base of the columns. The function "svdimage" returns an orthogonal basis of the image with more effort. >A=redim(1:9,3,3) 1 2 3 4 5 6 7 8 9 >rank(A) 2 >image(A) 0.0592348877759 0.118469775552 0.236939551104 0.29617443888 0.414644214431 0.473879102207 >svdimage(A) -0.214837238368 -0.887230688346 -0.520587389465 -0.249643952988 -0.826337540561 0.38794278237 >kernel(A) 1 -2 1 >svdkernel(A) 0.408248290464 -0.816496580928 0.408248290464 See:

svdimage (Linear Algebra)

functionkernel(A:complex, eps=none)

Computes the kernel of the matrix A. Returns a matrix M, containing a basis of the kernel of A. I.e., A.M=0. This function uses an LU decomposition with the Gauss algorithm. The function "svdkernel" does the same with more effort, but it returns an orthogonal base of the kernel. Add eps=..., if A is almost regular. Use a larger epsilon in this case than the default epsilon(). See:

image (Linear Algebra),

image (Maxima Documentation),

svdkernel (Linear Algebra)

functionrank(A:complex)

Computes the rank of the matrix A. The rank is the dimension of the image, the space spanned by the columns of A. See:

image (Linear Algebra),

image (Maxima Documentation)

functionoverwrite max

max(A) or max(x,y,...) Returns the maximum of the rows of A for one argument, or the maximum of x,y,z,... for several arguments. See:

max (Maxima Documentation),

extrema (Linear Algebra)

functionoverwrite min

min(A) or min(x,y,...) Returns the minimum of the rows of A for one argument, or the minimum of x,y,z,... for several arguments. See:

min (Maxima Documentation),

extrema (Linear Algebra)

functioncomment extrema(A)

Extremal values [min,imin,max,imax] in each row of A >x=random(5) [0.866587, 0.536137, 0.493453, 0.601344, 0.659461] >extrema(x) [0.493453, 3, 0.866587, 1] See:

min (Linear Algebra),

min (Maxima Documentation),

max (Linear Algebra),

max (Maxima Documentation)

functioncomment totalmax(A)

Maximal entry in the matrix A

functioncomment totalmin(A)

Minimal entry in the matrix A

functioncomment flipx(A)

flips a matrix vertically

functioncomment flipy(A)

flips a matrix horizontally

functionflipud(A:numerical)

flips a matrix vertically.

functionfliplr(A:numerical)

flips a matrix horizontally.

functioncomment rotleft(A)

rotates the rows of A to the left

functioncomment rotright(A)

rotates the rows of A to the right

functioncomment shiftleft(A)

shifts the rows of A to the left, last column to 0

functioncomment rotright(A)

shifts the rows of A to the right, first column to 0

functionrot90(A:numerical)

rotates a matrix counterclockwise

functionrot(A:numerical, k:integer=1)

rotates a matrix counterclockwise k times 90 degrees

functionoverwrite drop(A, i, cols=0)

Drop rows i from A, or elements i from row vector cols: If true, the columns i are dropped from the matrix A. Overwrites the built-in function drop, which works for real row vectors only.

For sums and products of vectors and rows of matrices have a look at the core functions.

functiontotalsum(A)

Computes the sum of all elements of A.

functionflatten(A)

Reshapes A to a vector

The simple Gauss algorithm is implemented in the A\b operator in Euler and in an algorithm from AlgLib.

The Gauss-Seidel method and the conjugate gradient methods are implemented for full and sparse matrices. Moreover, there are decompositions in LU or LL', and routines to solve systems based on the decompositions.

Euler has functions for sparse systems, exact functions, and interval routines for linear systems.

functionoverwrite alsolve(A:real, B:real, exact=0, check=1)

Solve A.X=B A must be square, and B can have more than one column. The function uses an implementation of the Gauss algorithm from AlgLib. The result is of the same order as the Euler operator A\B. exacter : If set, refinements are tried to get a better solution. check : If set, low condition numbers issue an error exception. Returns {X,info,condition} info : If -3, the matrix was found to be singular condition : If close to 0, the matrix should be singular (inverse condition) >A=normal(200,200); b=sum(A); >longest totalmax(abs(alsolve(A,b)-1)) 1.321165399303936e-014 >longest totalmax(abs(A\b-1)) 3.708144902248023e-014

functioninv(A:complex)

Computes the inverse of A. See:

xinv (Numerical Algorithms),

svdsolve (Linear Algebra),

xlgs (Numerical Algorithms),

fit (Linear Algebra),

pinv (Linear Algebra)

functioninvert(A)

Computes the inverse of A. Alias to inv.

functioncomment lu(A)

LU-decomposition of A. The result is {B,r,c,det}. B is the result of the Gauss algorithm with the factors written below the diagonal, r is the index rearrangement of the rows of A, c is 1 for each linear independent column and det is the determinant of A. There is a utility function LU, which computes the LU-decomposiotn of a matrix (L,R,P such that LR=PA). To get a true LU-decomposition using lu for a non-singular square A, take the lower part of B (plus the identity-matrix) as L and the upper part as U. Then A[r] is L.U, i.e. for quadratic A, A[r] is >(band(B[r],-n,-1)+id(n)).band(B[r],0,n) To solve A.x=b for quadratic A and an x quickly, use lusolve(B[r],b[r]). >A=normal(20,20); b=sum(A); >{B,r,c,det}=lu(A); det, -2.61073e+008 >longest totalmax(abs((band(B[r],-20,-1)+id(20)).band(B[r],0,20)-A[r])) 2.220446049250313e-015 >longest totalmax(abs(lusolve(B[r],b[r])-1)) 1.06581410364015e-014 See:

lusolve (Linear Algebra)

functioncomment lusolve(R,b)

Solve linear system LUx=b. L has ones on the diagonal. It is stored in the lower left part of R. U is stored in the upper right part of R. This is the way that lu returns the matrices. >R=[1,2,3;0,4,5;0,0,6] 1 2 3 0 4 5 0 0 6 >L=[0;1;1,2,0] 0 0 0 1 0 0 1 2 0 >A=(L+id(3)).R 1 2 3 1 6 8 1 10 19 >A\sum(A) 1 1 1 >lusolve(R+L,sum(A)) 1 1 1

functionseidel(A:real, b:real column, x=0, om=1.2, eps=none)

Solve Ax=b using the Gauss-Seidel method. A must be diagonal dominant, at least not have 0 on the diagonal. The method will converge for all positive definite matrices. However, larger dominance in the diagonal speeds up the convergence. For thin matrices use seidelX(). om : This is a relaxation parameter between 1 and 2. The default is 1.2, and works in many cases. x : start value You can specify the accuracy with an optional parameter eps. See:

seidelX (Numerical Algorithms)

functioncg(A:real, b:real column, x0:column=none, f:index=10, eps=none)

Conjugate gradient method to solve Ax=b. This function is the function of choice for large matrices. There is a variant "cgX" for large, sparse matrices. Stops as soon as the error gets larger. See:

cgX (Numerical Algorithms),

cpxfit (Numerical Algorithms)

functionsolvespecial(A:complex, b:complex)

Special solution of Ax=b using the LU decomposition. Returns a vector, such that A.x=b, even for singular, or non-square matrices A. This function uses the Gauss algorithm to determine the LU decomposition. It then extracts a base from the columns, and uses this base to get a special solution. Works only, if the matrix has maximal rank, i.e., the rows are linear independent. For matrices, which have a solution, the functions "svdsolve" or "fit" provide the same functionality with more effort. See:

svdsolve (Linear Algebra),

fit (Linear Algebra),

kernel (Linear Algebra)

functiondet(A:complex, eps=none)

Determinant of A. Uses the Gauss algorithm to compute the determinant of A.

functioncholesky(A)

Decompose a real matrix A, such that A=L.L'. Returns L. A must be a positive definite symmetric and at least 2x2 matrix. A is not checked for symmetry, See:

LU (Linear Algebra),

lu (Linear Algebra)

functionlsolve(L,b)

Solve L.L'.x=b See:

cholesky (Linear Algebra),

cholesky (Maxima Documentation)

functionpivotize(A:numerical, i:index, j:index)

Make A[i,j]=1 and all other elements in column j equal to 0. The function is using elementary operations on the rows of A. Use this functions for the demonstration of the Gauss algorithm. The function modifies A, but returns A, so that it can be printed easily. See:

normalizeRow (Linear Algebra),

swapRows (Linear Algebra),

gjstep (Linear Algebra)

functionpivotizeRows(A:numerical, i:index, j:index)

Alias to pivotize

functionnormalizeRow(A:numerical, i:index, j:index)

Divide row j by A[i,j]. The function is using elementary operations. Use this functions for the demontration of the Gauss algorithm. See:

pivotizeRows (Linear Algebra),

swapRows (Linear Algebra)

functionswapRows(A:numerical, i1:index, i2:index)

Swap rows i1 and i2 of A. The function is using elementary operations. Use this functions for the demontration of the Gauss algorithm. See:

pivotizeRows (Linear Algebra),

normalizeRow (Linear Algebra)

hilbertfactor:=3*3*3*5*5*7*11*13*17*19*23*29;

functionhilbert(n:index, f=hilbertfactor)

Scaled nxn Hilbert matrix. The Hilbert matrix is the matrix (1/(i+j-1)). This function multplies all entries with a factor, so thatthe matrix can accurately be stored up to the 30x30 Hilbert matrix.

functionhilb(n:integer, f=hilbertfactor)

Scaled nxn Hilbert matrix. Alias for hilbert(n). See:

hilbert (Linear Algebra)

functioncomment givensrot(j,i1,i2,A,B)

One Givens rotation of A and B Returns (X=GA,Y=GB), such that X[l2,j]=0, and G is an orthogonal Matrix, changing only the rows i1 and i2 of A and B. >shortformat; A=random(3,3), 0.77473 0.89357 0.5985 0.70968 0.36746 0.80751 0.59486 0.67635 0.26406 >B=id(3) 1 0 0 0 1 0 0 0 1 >{X,Y}=givensrot(1,1,2,A,B); X -1.0506 -0.90712 -0.98678 0 0.33263 -0.19118 0.59486 0.67635 0.26406 >Y.Y' 1 0 0 0 1 0 0 0 1 See:

givensqr (Linear Algebra)

functioncomment givensqr(A,B)

QR decomposition of A and B Returns {R=GA,Y=GB,c}, such that R is upper triangle, and c shows the linear independent columns of A. See:

qrdecomp (Linear Algebra)

functionqrdecomp(A:real)

Decompose QA=R using Givens rotations. The function returns {R,Q,c}. Q is an orthogonal matrix, and R is an upper triangle matrix. c is a vector with ones in the basis columns of A. This function uses orthogonal transformations to compute Q and R. It works for non-square matrices A. >A=normal(5,5); >{R,Q}=qrdecomp(A); >longest totalmax(abs(Q.A-R)) 4.440892098500626e-016 see: givensqr, givensrot

functioncomment orthogonal(A)

Compute an orthogonal basis of the columns of A This functions uses the Gram-Schmid method to compute an orthogonal basis of the columns of A. c is a row vector with 1 for the indices of the basis columns of A. >shortformat; A=random(3,5) 0.883227 0.270906 0.704419 0.217693 0.445363 0.308411 0.914541 0.193585 0.463387 0.095153 0.595017 0.431184 0.72868 0.465164 0.323032 >{B,c}=orthogonal(A); c [1, 1, 1, 0, 0] >O=B[,nonzeros(c)] 0.796621 -0.370762 -0.477421 0.278169 0.92606 -0.25502 0.536672 0.0703505 0.840853 >O.O' 1 0 0 0 1 0 0 0 1 See:

svdimage (Linear Algebra)

There are functions for linear regression using the normal equation, orthogonal transformations, and singular value decomposition.

functionfitnormal(A:complex, b:complex)

Computes x such that ||A.x-b|| is minimal using the normal equation. A is an nxm matrix, and b is a nx1 vector. The function uses the normal equation A'.A.x=A'.b. This works only, if A has full rank. E.g., if n>m, then the m columns of A must be linear independent. The function "fit" provides a faster and better solution. "svdsolve" has the additional advantage that it produces the solution with minimal norm. For sparse matrices use "cpxfit" For badly conditioned A, you should use svdsolve instead. A : real or complex matrix (nxm) b : column vector or matrix (mx1 or mxk) See:

svdsolve (Linear Algebra),

cpxfit (Numerical Algorithms)

functionfit(A:real, b:real)

Computes x such that ||A.x-b||_2 is minimal using Givens rotations. A is a nxm matrix, and b is a nxk vector (usually k=1). This function works even if A has no full rank. Since it uses orthogonal transformations, it us also quite stable. The function "svdsolve" does the same, but minimizes the norm of the solution too. A : real matrix (nxm) b : real column vector or matrix (mx1 or mxk) See:

fitnormal (Linear Algebra),

svdsolve (Linear Algebra)

functiondivideinto(A: real, B: real)

Divide A into B yielding fit(B',A')' This function is used in Matlab mode for A/B. See:

fit (Linear Algebra)

functionpolyfit(x:complex vector, y:complex vector, .. n:nonnegative integer scalar, w:real vector=none)

Fits a polynomial in L_2-norm to data. Given data x[i] and y[i], this function computes a polynomial p of given degree such that the sum over the squared errors (p(x[i])-y[1])^2 is minimal. The fit uses the normal equation solved with an orthogonal decomposition. w : Optional weights (squared) for the fit.

functionnorm(A:numerical, p:nonnegative real=2)

Euclidean norm of the vector A. For p=2, the function computes the square root of the sum of squares of all elements of A, if A is a matrix. For p>0 it computes the p-norm. For p=0 it computes the sup-norm. Note: The function does not compute the matrix norm. See:

maxrowsum (Linear Algebra)

functionmaxrowsum(A:numerical)

Maximal row sum of A

functioncomment scalp(v,w)

Scalar product of v an w

functioncrossproduct(v:real, w:real)

Cross product between two 3x1 or 1x3 vectors. The function will work for two column or row vectors. The result is of the same form as v. It will also work for a matrix of 1x3 columns v, and a matrix of 1x3 columns w. See:

vectorproduct (Linear Algebra),

scalp (Euler Core),

scalp (Linear Algebra),

scalp (Geometry in Euler)

functionvectorproduct(A:real)

Multiplication of the columns of an nx(n-1) matrix A. Generalized cross product of n-1 vectors of length n. The result is perpendicular to the vectors, and its length is the volume of the polyhedron spanned by the vectors squared. A : real matrix of size n times (n-1) See:

crossproduct (Euler Core),

crossproduct (Linear Algebra),

scalp (Euler Core),

scalp (Linear Algebra),

scalp (Geometry in Euler),

gramdet (Linear Algebra)

functiongramdet(A)

Gram determinant of the columns of A. The Gram determinant is the volume of the polyhedron spanned by the columns of A. A : real matrix of size kxn (k<n) See:

vectorproduct (Linear Algebra)

functionpower(A:numerical, n:integer scalar)

A^n for integer n for square matrices A. The function uses a binary recursive algorithm. For n<0, it will compute the inverse matrix first. For n<0, the matrix must be real or complex, not interval. For scalar values, use the faster x^n. Moreover, power(x,n) will not map to the elements of x or n. See:

matrixpower (Linear Algebra)

functionmatrixpospower(A:numerical, n:natural)

Returns A^n for natural n>=0 and a matrix A. Alias for power(A,n)

functionmatrixpower(A:complex, n:integer scalar)

Returns A^n for integer n and a matrix A. Alias for power(A,n)

functionmatrixfunction(f:string, A:complex)

Evaluates the function f for a matrix A. The function f must be an analytic function in the spectrum of A. A must have a basis of eigenvectors. This function will decompose A into M.D.inv(M), where D is a diagonal matrix, and apply f to the diagonal of D. See:

matrixpower (Linear Algebra)

functionLU(A)

Returns {L,R,P} such that L.R=P.A This function uses the Gauss-Algorithm in lr(A) to find the LR decomposition of a regular square matrix A. L is a lower triangle matrix with 1 on the diagonal, R an upper right triangle matrix, and P a permutation matrix. See:

lu (Linear Algebra)

functionechelon(A)

Forms A into echelon form using the Gauss algorithm See:

LU (Linear Algebra),

lu (Linear Algebra)

For eigenvalues and eigenvectors, Euler has algorithms based on the AlgLib library, and algorithms based on the characteristic polynomial.

functioncomment charpoly(A)

Characteristic polynomial of A Euler uses an orthogonal transformation to a simpler matrix, and a recursion to compute the characteristic polynomial. Note that polynomials in Euler start with the constant coefficient.

functioneigenvalues(A:complex, usecharpoly=0, check=1)

Eigenvalues of A. Returns a complex vector of eigenvalues of A. For real matrices, the functions uses an algorithm from AlgLib and LAPACK to find the eigenvalues, unless usecharpoly is true. If usecharpoly is true or the matrix is complex, this function computes the characteristic polynomial using orthogonal transformations to an upper diagonal matrix, with additional elements below the diagonal. It then computes the zeros of this polynomial with "polysolve". See:

charpoly (Linear Algebra),

charpoly (Maxima Documentation),

svd (Euler Core),

jacobi (Linear Algebra),

jacobi (Maxima Documentation),

svdeigenvalues (Linear Algebra),

mxmeigenvalues (Maxima Functions for Euler),

aleigen (Linear Algebra)

functioneigenspace(A:complex, l:complex vector)

Returns the eigenspace of A to the eigenvaue l. The eigenspace is computed with kernel(A-l*id(n)). Since the eigenvalue may be inaccurate, the function tries several epsilons to find a non-zero kernel. A more stable function is "svdeigenspace". See:

eigenvalues (Linear Algebra),

eigenvalues (Maxima Documentation),

svdeigenspace (Linear Algebra)

functioneigen(A:complex, usekernel=0, usecharpoly=0, check=1)

Eigenvectors and a basis of eigenvectors of A. Returns {l,x}, where l is a vector of eigenvalues, x is a basis of eigenvectors. For real matrices A an algorithm from AlgLib and LAPACK is used, unless usekernel is true. usekernel : Compute the eigenvalues, then the kernels. usecharpoly : Use the characteristic polynomial for the eigenvalues. check : Check the return code of AlgLib. See:

eigenvalues (Linear Algebra),

eigenvalues (Maxima Documentation),

eigenspace (Linear Algebra),

mxmeigenvectors (Maxima Functions for Euler)

functioncomment aleigen(A,flag)

AlgLib routine to compute the eigenvalues of A This function returns the eigenvalues {v,res}, of the eigenvalues and the eigenvectors {v,res,W}, if flag is true. A must be real vector. The res flag is true, if the algorithm succeeds (from AlgLib and LAPACK). It is easier to use the functions eigenvalues() and eigen(). See:

eigenvalues (Linear Algebra),

eigenvalues (Maxima Documentation)

functioncjacobi(A:complex, eps=sqrt(epsilon))

Jacobi method to compute the eigenvalus of a Hermitian matrix A. Returns a vector of eigenvalues. This function computes the eigenvalues of a real or complex matrix A, using an iteration with orthogonal transformations. It works only for Hermitian matrices A, i.e., A=conj(A'). See:

jacobi (Maxima Documentation)

functionoverwrite jacobi(A:real, eps=none)

Jacobi method to compute the eigenvalus of a symmetric matrix A. Returns a vector of eigenvalues. This function computes the eigenvalues of a real matrix A, using an iteration with orthogonal transformations. It works only for symmetric matrices A, i.e., A=A'. See:

cjacobi (Linear Algebra)

functioncholeskyeigen(A, eps=none)

Iterates the Cholesky-iteration, until the diagonal converges. A must be positive definite symmetric and at least 2x2. See:

cholesky (Maxima Documentation)

The singular value decomposition of Euler is based on the builtin function svd. It is used to compute an orthogonal basis of the kernel and the image of a matrix, the condition of a matrix, or the pseudo-inverse.

functionsvdkernel(A:real)

Computes the kernel of the quadratic matrix A This function is using the singular value decomposition, and works for real matrices only. Returns an orthogonal basis of the kernel as columns of a matrix. See:

svd (Euler Core)

functionsvdimage(A:real)

Computes the image of the quadratic matrix A This function is using the singular value decomposition, and works for real matrices only. Returns an orthogonal basis of the image as columns of a matrix. This can be used to compute an orthogonal basis of vectors. >shortformat; A=random(3,2) 0.493453 0.601344 0.659461 0.967468 0.193151 0.935921 >svdimage(A) -0.441518 -0.45828 -0.357159 -0.69891 0.823103 -0.549094 See:

orthogonal (Linear Algebra),

svd (Euler Core),

kernel (Linear Algebra),

image (Maxima Documentation),

svdkernel (Linear Algebra)

functionsvdcondition(A:real)

Condition number based on a singular value decomposition of A Returns the quotient of the largest singular value divided by the smallest. 0 means singularity. A : real matrix

functionsvddet(A:real)

Determinant based on a singular value decomposition of A A : real matrix See:

svd (Euler Core)

functionsvdeigenvalues(A:real)

Eigenvalues or singular values of A For a symmetric A, this returns the eigenvalues of A For a non-symmetric A, the singular values. A : real matrix See:

eigenvalues (Maxima Documentation)

functionsvdeigenspace(A:real,l:real)

Returns the eigenspace of A to the eigenvalue l

functionsvdsolve(A:real,b:real)

Minimize |A.x-b| with smallest norm for x The singular value decomposition is one way to solve the fit problem. It has the advantage, that the result will be the result with smallest norm, if there is more than one solution. A : real matrix See:

fit (Linear Algebra)

functionsvdinv(A:real)

The pseudo-inverse of A. The pseudo-inverse B of a matrix solves the fit problem to minimize |Ax-b| by x=B.b. It is computed in this function using an svd decomposition.

functionpinv(A:real)

The pseudo-inverse of A. Alias to svdinv See:

svdinv (Linear Algebra)

functionginv(A:real)

Gilbert inverse of a matrix A This inverse has the property A.G.A=A

Some functions for the demonstration of the Gauss-Jordan algorithm. These functions can be used for linear systems or for the simplex algorithm.

defaultgjdigits:=3; defaultgjlength:=10;

functiongjprint(A : real, n : positive integer vector, v : string vector, digits:nonnegative integer=none, length:index=none)

Print the matrix A in Gauss-Jordan form. The scheme A is printed in the form x y ... a 1 2 ... b 3 4 ... ... n : index vector with a permumatin of the variables v : variable names with the column variables first digits : number of digits for each number length : output length of each number See:

gjstep (Linear Algebra)

functiongjstep(A:numerical, i:index, j:index, n:positive integer vector, v:string vector=none, digits:nonnegative integer=none, length:index=none)

Make A[i,j]=1 and all other elements in column j equal to 0. The function is using elementary operations on the rows of A. Use this functions for the demonstration of the Gauss_Jordan algorithm. The function modifies A and n. n : A row vector of indices, the function assumes the Gauss-Jordan form. n contains the indices of the variables, first the indices of the variables in the rows, and then the indices of the variables in the columns. The row variable i is exchanged with the column variable j. This is the same as making the j-th column to an canonical vector e[i], and inserting in the j-th column the result of this operation applied to e[i]. You can add a vector v of strings, which contains the names of the variables. The problem will then be printed using gjprint. See:

pivotize (Linear Algebra),

gjprint (Linear Algebra)

functiongjsolution(M : real, n : positive integer vector)

Read the values of the solution from the Gauss-Jordan scheme. For this, we assume that the last column contains the values, and the variables in the top row are set to zero. See:

gjstep (Linear Algebra)

functiongjvars(M : real, simplex=false)

Generate variable names for M simplex : If true the last row variable will be named "" and the last column will name will be missing. Returns {v,n} where n=1:(rows+cols), v=["s1"...,"x1",...] See:

gjprint (Linear Algebra)

functiongjaddcondition(M:real, n:positive integer vector, v:string vector, line:real vector, varname:string)

Add a condition to the Gauss-Jordan Form The line for the condition (left side <= right side) and the variable name must be provided. The condition uses the current top row variables. Return {M,n,v}

functioncomment magic(n)

Magic square of size nxn.