New Fast Non-Alternating Inertial Matrix Factorization Algorithms

Beyond Alternating Updates for Matrix Factorization with Inertial Bregman Proximal Gradient Algorithms

by Mahesh Chandra Mukkamala and Peter Ochs, Mathematical Optimization Group, Saarland University.

Part 2: Matrix Completion Code

Algorithms Implemented:

BPG-MF: Bregman Proximal Gradient (BPG) for Matrix Factorization
CoCaIn BPG-MF: Convex Concave Inertial (CoCaIn) BPG for Matrix Factorization
BPG-MF-WB: BPG for Matrix Factorization with Backtracking
PALM: Proximal Alternating Linearized Minimization
iPALM: Inertial Proximal Alternating Linearized Minimization

Dependencies

• numpy, matplotlib and nimfa

If you have installed above mentioned packages you can skip this step. Otherwise run (maybe in a virtual environment):

pip install -r requirements.txt

Reproduce results

To generate results

python run_automated.py 

Then to create the plots

chmod +x generate_plots.sh
./generate_plots.sh

To generate statistical evaluation results

python run_seed_exps.py 

Then to create the plots

chmod +x generate_seed_plots.sh
./generate_seed_plots.sh

Now you can check figures folder for various figures.

Description of results

The function number is denoted as fun_num. The plots corresponding to fun_num 0,1,2 are for synthetic dataset. And, the plots corresponding to Medulloblastoma dataset are denoted with fun_num 3,4,5.

In fun_num 0,3 : No-Regularization is used.
In fun_num 1,4 : L2-Regularization is used.
In fun_num 2,5 : L1-Regularization is used.

Results

Simple Illustration

An excerpt from Page 2 of the paper.

Citation

@techreport{MO19b,
  title        = {Beyond Alternating Updates for Matrix Factorization with Inertial Bregman Proximal Gradient Algorithms},
  author       = {M.C. Mukkamala and P. Ochs},
  year         = {2019},
  journal      = {ArXiv e-prints, arXiv:1905.09050},
}

Contact

Mahesh Chandra Mukkamala ([email protected])

References

M.C. Mukkamala, P. Ochs: Beyond Alternating Updates for Matrix Factorization with Inertial Bregman Proximal Gradient Algorithms. ArXiv e-prints, arXiv:1905.09050, 2019.

M. C. Mukkamala, P. Ochs, T. Pock, and S. Sabach: Convex-Concave Backtracking for Inertial Bregman Proximal Gradient Algorithms in Non-Convex Optimization. ArXiv e-prints, arXiv:1904.03537, 2019.

J. Bolte, S. Sabach, M. Teboulle, and Y. Vaisbourd. First order methods beyond convexity and Lipschitz gradient continuity with applications to quadratic inverse problems. SIAM Journal on Optimization, 28(3):2131–2151, 2018.

J. Bolte, S. Sabach, and M. Teboulle. Proximal alternating linearized minimization for nonconvex and nonsmooth problems. Mathematical Programming, 146(1-2):459–494, 2014.

T. Pock and S. Sabach. Inertial proximal alternating linearized minimization (iPALM) for nonconvex and nonsmooth problems. SIAM Journal on Imaging Sciences, 9(4):1756–1787, 2016.

License

Check here.