Though permanent magnets are ubiquitous in science and everyday society, we still lack a systematic analysis of how to optimally place orient large set them (much larger) possible locations. This study reformulates the problem terms sparse regression, offers an algorithm that can effectively solve for nonconvex systems with over 10${}^{6}$ optimizable variables constraints. The authors then obt...

Regularized sparse learning with the ℓ0-norm is important in many areas, including statistical and signal processing. Iterative hard thresholding (IHT) methods are state-of-the-art for nonconvex-constrained due to their capability of recovering true support scalability large datasets. The current theoretical analysis IHT assumes use centralized IID data. In realistic large-scale scenarios, howe...

In this paper, we propose a new framework for segmenting feature-based multiple moving objects with subspace models in affine views. Since the feature data is high-dimensional and complex in the real video sequences, most traditional approaches for motion segmentation use the conventional PCA to obtain a low-dimensional representation, while our proposed framework applies the sparse PCA (SPCA) ...

Some thoughts about algorithms for minimizing l and TV norms for signal and image recovery. In particular, I put the emphasis on the connections between Lagrangian and l constrained optimizations, and between analysis and synthesis priors. 1 Lagrangian and Constrained l1 Pursuits Lagrangian l pursuit reads ã = argmin b 1 2 ‖f − Φ∗b‖2 + T‖b‖1. (1) It is a Lagrangian formulation of the following ...