Sparse Matrix Algorithms |
Example: Biconjugate Gradient Algorithm |
The biconjugate gradient algorithm is meant for general sparse linear systems. Matrix symmetry is no longer assumed, and a complete list of nonzero coefficients must be provided. Consider the following matrix:
with
The code for this example is as follows:
/* biconjugate gradient algorithm */ /* value row column */ A = { 10 1 1, 3 2 2, 4 3 3, 0.1 2 1, 0.2 1 3 }; /* vector of right-hand sides */ b = {1, 1, 1}; /* desired solution tolerance */ tol = 1e-9; /* maximum number of iterations */ maxit = 10000; /* allocate history/progress */ hist = j(50, 1); /* initial guess (optional) */ start = {2, 3, 4}; /* call biconjugate gradient subroutine */ call itsolver ( x, st, it, /* output parameters */ 'bicg', a, b, 'milu', /* input parameters */ tol, /* optional control parameters */ maxit, start, hist); /* Print results */ print x; print st; print it;
Here is the output:
X 0.095 0.3301667 0.25 ST 1.993E-16 IT 3
It is important to observe the resultant tolerance in order to know how effective the solution is.
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