Items tagged with matrix


first : My English not good,so excuse me.
I have a problem with this:

1)C+Z*(C)*X=D where;

I know D , Z and X are matrix(n*n),(known)
but i can`t find C or Solve (1)finding C(matrix(n*n))

Help Me


Dear Sir

I want to solve system of algebric equations using matrix

can you help me to do that

> restart;

> Digits:=6:

> s:=140:



I have a problem with creating eigenvalues of a 14x14 matrix.

when i execute "LinearAlgebra[Eigenvalues](A)" there are only results like "-3.2211+29.1111I"

The problem is the 'I' at the end. I need numeric values for plotting or stuff like that.

Wheres my mistake? I have no clue.

Im looking forward to any suggestions! At the bottom...



I have a serious problem and im looking forward to any suggestions.

Like i already wrote i wanna create a simple matrix (14x14) out of four matrices(7x7) and i have no clue how to accomplish this task in maple.

to be more specific, i want to create matrix M=(A,B,C,D) each of them is another 7x7 which results in a 14x14 matrix.

I guess its a simple task but i tried hard and didnt get it worked.

thank you  in anticipation

Suppose you want to solve a large dense linear system AX=B over the rationals - what should you do? Well, one thing you should probably not do is directly apply Gaussian elimination. It does O(n^3) arithmetic operations, but the size of the numbers blow up, leading to an exponential bit complexity. Don't believe me? Try it:

for N from 5 to 9 do
  A := RandomMatrix(2^N, 2^N+1,generator=-10^5..10^5):
  TIMER := time(GaussianElimination(A...

"I've seen this element before..." Often we are faced with the problem of building up sets incrementally, by removing pieces one at a time from a larger whole. The bottlenecks in this case are usually: 1) adding a small set X to a large set S (copies S and X, making this ~O(|S|+|X|)) 2) removing elements of the large set S from the small set X (binary search: |X|*log(|S|)) A classic example of this is a breadth-first-search. We start at one vertex of a graph and in each iteration we add the set of new neighbors X to the set of vertices S that have already been found. We can make this more useful by making the program return the sets of new neighbors found in each iteration, that is, the sets of vertices that are distance 1, 2, 3, etc. from the initial vertex.

When working with large sparse linear systems you often want to look at their non-zero structure, however Maple's existing tools are all designed for dense matrices. I wrote a little tool to produce images like this in reasonable time. You can download the code here, and the rest of this post is a quick tutorial on how to use the included command. Maple 11 is required.

What is the largest linear system that Maple can solve? You might be surprised to find out. In this article we present strategies for solving sparse linear systems over the rationals. An example implementation is provided, but first we present a bit of background. Sparse linear systems arise naturally from problems in mathematics, science, and engineering. Typically many quantities are related, but because of an underlying structure only a small subset of the elements appear in most equations. Consider networks, finite element models, structural analysis problems, and linear programming problems.

This tip comes care of Dr. Michael Monagan at Simon Fraser University. Represent your sparse matrix as a list of rows, and represent each row as a linear equation in an indexed name. For example:

A := [[1,0,3],[2,0,0],[0,4,5]];

S := [ 1*x[1] + 3*x[3], 2*x[1], 4*x[2]+5*x[3] ];

To compute the product of the matrix A with a Vector X, assign x[i] := V[i] and evaluate. This can be done inside of a procedure because x is a table.

V := [7,8,9]: for i to 3 do x[i...

This is a quick programming exercise to correct the following problem in Maple:

for n from 8 to 12 do 
  A := Matrix(2^n, 2^n, storage='sparse'):  # zero matrix
  print( 2^n, time(LinearAlgebra:-Transpose(A)) );
end do:

The problem is that the LinearAlgebra:-Transpose command is not sparse. That is, the time it takes is proportional to the overall size of the matrix and not to the number of non-zero entries - even when your matrix uses sparse storage. In this post we will look at what is required to program a new Transpose command which can handle much larger matrices.

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