CS: A new question, Solving A Low-Rank Factorization Model for Matrix Completion by A Nonlinear Successive Over-Relaxation Algorithm, CS imaging

Following up on interesting question in A Puzzle of No Return? Bob Sturm has a part 2 to that question.

Yin Zhang just let me know that they just released a new paper entitled: Solving A Low-Rank Factorization Model for Matrix Completion by A Nonlinear Successive Over-Relaxation Algorithm by Zaiwen Wen , Wotao Yin and Yin Zhang. The abstract reads:

The matrix completion problem is to recover a low-rank matrix from a subset of its entries. The main solution strategy for this problem has been based on nuclear-norm minimization which requires computing singular value decompositions -- a task that is increasingly costly as matrix sizes and ranks increase. To improve the capacity of solving large-scale problems, we propose a low-rank factorization model and construct a nonlinear successive over-relaxation (SOR) algorithm that only requires solving a linear least squares problem per iteration. Convergence of this nonlinear SOR algorithm is analyzed. Numerical results show that the algorithm can reliably solve a wide range of problems at a speed at least several times faster than many nuclear-norm minimization algorithms.

Thanks Yin!

Optically compressed sensing by under sampling the polar Fourier plane by Adi Stern, O Levi, and Yair Rivenson. The abstract reads:

In a previous work we presented a compressed imaging approach that uses a row
of rotating sensors to capture indirectly polar strips of the Fourier transform of the image. Here we present further developments of this technique and present new results. The advantages of our technique, compared to other optically compressed imaging techniques, is that its optical implementation is relatively easy, it does not require complicate calibrations and that it can be implemented in near-real time.

They even quote Nuit Blanche, woohoo!