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Incomplete Cholesky Decomposition

version 1.0.0 (33 KB) by Royi Avital
Implementation of the Incomplete Cholesky Decomposition with Thresholding

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Updated 24 Sep 2021

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Sparse Incomplete Cholesky Decomposition

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Implementation of the Incomplete Cholesky Decomposition with few methods.
The project includes a C implementation with a MATLAB MEX wrapper.

The aim is to have 3 variants of the incomplete decomposition:

  1. Threshold (IC(\tau))
    Using a threshold, $ \tau $ to define which elements will be kept from the decomposition.
    It can be using global threshold or by a column.
    Implemented
  2. Pattern (IC(l))
    Filling elements which are up to l steps in the graph of the matrix A. For l = 0 called Zero Fill where filling zeros in elements not defined by the pattern.
    Also could be filled by a given pattern of sparsity (So given A as the pattern it matches l = 0).
    Not Implemented
  3. Number of Non Zero Elements (IC(p))
    Keeps the largest p elements per column.
    Not Implemented

Generating MATLAB MEX

  1. Download the repository.
  2. Run MakeMex in MATLAB with pre defined MATLAB MEX Compiler.
  3. Go through the Unit Tests and the Run Time Analysis.

The MEX Wrapper supports only Sparse Real Matrices of Type Double.

Performance

Comparing the performance with MATLAB's functions.

Decomposition

The MEX file and MATLAB's ICT were the most memory efficient.

Pre Conditioning (Solving the Linear System)

To Do

  • Move the array sorting related code to a dedicated repository with complete run time analysis.

References

Cite As

Royi Avital (2021). Incomplete Cholesky Decomposition (https://github.com/RoyiAvital/IncompleteCholeskyDecomposition), GitHub. Retrieved .

MATLAB Release Compatibility
Created with R2021a
Compatible with any release
Platform Compatibility
Windows macOS Linux

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To view or report issues in this GitHub add-on, visit the GitHub Repository.
To view or report issues in this GitHub add-on, visit the GitHub Repository.