What is the next step after LU decomposition for solving Ax=b?

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SUMMARY

The discussion focuses on solving the linear system Ax=b, where A is a 1484x1484 sparse matrix with 95% zeros. After performing LU decomposition, the next steps involve considering iterative methods if A lacks symmetry or banded structure. The participants recommend exploring open-source libraries for sparse linear systems, specifically mentioning SUPERLU from LBNL and netlib as valuable resources for code and installation guidance.

PREREQUISITES
  • Understanding of LU decomposition and its applications in linear algebra.
  • Familiarity with sparse matrix representations and their properties.
  • Knowledge of iterative methods for solving linear systems.
  • Basic skills in using open-source libraries for numerical computations.
NEXT STEPS
  • Research iterative methods for solving sparse linear systems, such as Conjugate Gradient or GMRES.
  • Learn how to install and use SUPERLU for efficient sparse matrix computations.
  • Explore the netlib repository for additional resources and example codes related to sparse matrices.
  • Investigate the Cholesky decomposition for banded and symmetric matrices if applicable.
USEFUL FOR

Mathematicians, data scientists, and software developers working on numerical methods for solving large sparse linear systems.

Milentije
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I have
Ax=b problem
where A 1484x1484 matrix,b 1484x1.
A is sparse(95% zeros) but if I go for LU decomposition what should be the next step?
Or is there any other method,I forgot algebra,learned it when I was undergrad long time ago.
 
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Simple Gauss elimination is mainly a pedagogical tool used in teaching linear systems, and it's far from being the most effective method (especially when talking about specialized problems like sparse systems). If you're writing a code that solves your linear system, you can find free open source libraries/software packages for that. Just Google "sparse linear system open source".
 
Milentije said:
I have
Ax=b problem
where A 1484x1484 matrix,b 1484x1.
A is sparse(95% zeros) but if I go for LU decomposition what should be the next step?
Or is there any other method,I forgot algebra,learned it when I was undergrad long time ago.
1484? :bugeye:

May I ask what this is for, out of curiosity?
 
The best method will depend on several factors. Is A banded and symmetric? If is narrow banded and symmetric, Gauss, Choleski decomp, or other methods may be used. If A is large, sparse, and the zero entries are somewhat randomly distributed, with no symmetry or banded layout of the non-zero terms, then an iterative method might be more suitable.
 
Yes,I am creating input file where velocities need to be calculated for every node.Total number of velocities in model is 1484,quite big.
Regarding software,I have problem to install SUPER Lu from LBNL,are there any links for simple code that to not require libraries(like BLAS( IN THIS CASE?
 
I would make my first stop at the netlib: http://www.netlib.org/

Depending on what method you use, you can always google (or dogpile) and find scads of code.
 
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