cuda-rpca-admm

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A CUDA implementation of performing Robust PCA for foreground-background separation, using ADMM for optimization.

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GENERATING MATRIX
Use the generate_matrix.m file to generate a random matrix of a specified size and a particular rank

Command-

`generate_matrix(25344, 200, 1);`

Here 25344 corresponds to the image size (144 x 176) and 200 corresponds to the number of frames in the video
Note that the first dimension should be greater than the second one in generate_matrix() for our algorithm to work. (25344 > 200)

This command will generate a 200A.dat file, which will serve as input to our RPCA algorithm

RUNNING RPCA USING ADMM USING MATLAB
From MATLAB, use the admm_example.m file to run ADMM for RPCA. Provide the matrix generated in the previous step as input.

Command-

`admm_example('200A.dat');`

This command will run admm and write the output matrices into three different files named like boyd_X1.dat etc..

RUNNING RPCA USING ADMM USING CUDA
For CUDA code, use the script compile_and_run.sh to compile the code and run the file. Provide the input matrix as an argument to this script.

Command-

`./compile_and_run.sh 200A.dat`

This command will run the CUDA code for RPCA and write output to files named like X1_out.dat, etc..

Other files include MATLAB files to convert output into videos.

cuda-rpca-admm

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