Fast, approximate solution to linear equations?

Question

I need to solve a system of N linear equations as an intermediate step in a numerical optimizer. AFAIK reasonably simple algorithms for doing so exactly are O(N^3) (though I saw a horibly complicated one in some math paper that could do it in something like O(N^2.8) with a huge constant). In some cases N is huge, i.e. several thousand.

Is there any good way to get a decent approximate solution to a system of linear equations in less than O(N^3)?

Edit:

Here are some more details if it helps at all.

1. My matrix is symmetric, and not sparse.

2. It's the second derivative matrix from Newton-Raphson. I'm trying to optimize something in a 2000-dimensional space.

Answer 1

Yes, if the matrix you get from their coefficients is sparse. There's the method of the "Right lay" (in Bulgarian, not sure about the exact translation) if you have a tri-diagonal matrix, for instance, which operates in O(N). There are other algorithms that are still O(N^3) but achieve incredible results if the matrix conforms to some invariant that they require (sparse, diagonal-prevailing, triangular, etc.).

If you're stuck to a specific method based on your invariant, the only way to further speed things up is to go multi-threaded.

Try this search.

Answer 2

There are iterative methods like Jacobi, Gauss-Seidel, cg, GMRES etc.

Answer 3

For a symmetric matrix, the conjugate gradient method is simple to implement, and will beat most other iterative methods (e.g. Gauss-Seidel, SOR). The main loop consists of a matrix-vector multiply and a few other vector operations.

Once you've got it working, you can use preconditioning to improve the convergence even more.