INTRODUCTION Both L1 minimization [1] and homotopic L0 minimization [2] techniques have shown success in compressed-sensing MRI reconstruction using reduced k-space data. L1 minimization algorithm is known to usually shrink the magnitude of reconstructions especially for larger coefficients [1, 3] and non-convex penalty used in homotopic L0 minimization is advocated to replace L1 penalty [3]. H...

Recently, l2,1 matrix norm has been widely applied to many areas such as computer vision, pattern recognition, biological study and etc. As an extension of l1 vector norm, the mixed l2,1 matrix norm is often used to find jointly sparse solutions. Moreover, an efficient iterative algorithm has been designed to solve l2,1-norm involved minimizations. Actually, computational studies have showed th...

We investigate sublinear classical and quantum algorithms for matrix games, a fundamental problem in optimization machine learning, with provable guarantees. Given matrix, the game were previously known only two special cases: (1) maximizing vectors live L1-norm unit ball, (2) minimizing either L1- or L2-norm ball. give algorithm that can interpolate smoothly between these any fixed q 1 2, we s...

Underwater acoustic channels (UWA) are usually sparse, which can be exploited for adaptive equalization to improve the system performance. For shallow UWA channels, based on proportional minimum symbol error rate (PMSER) criterion, framework requires sparsity selection. Since of L0 norm is stronger than that L1, we choose it achieve better convergence. However, because leads NP-hard problems, d...

Recently, the design of group sparse regularization has drawn much attentions in group sparse signal recovery problem. Two of the most popular group sparsity inducing regularization are the l1,2 and l1,∞ regularization, defined as the sum of l2 and l∞ norms respectively. Nevertheless, they may fail to simultaneously consider the intra-group and intergroup sparsity characteristic of the signal. ...

INTRODUCTION Both L1 minimization [1] and homotopic L0 minimization [2] techniques have shown success in compressed-sensing MRI reconstruction using reduced k-space data. L1 minimization algorithm is known to usually shrink the magnitude of reconstructions especially for larger coefficients [1, 3] and non-convex penalty used in homotopic L0 minimization is advocated to replace L1 penalty [3]. H...

Too high sampling rate is the bottleneck to wideband spectrum sensing for cognitive radio (CR). As the survey shows that the sensed signal has a sparse representation in frequency domain in the mass, compressed sensing (CS) can be used to transfer the sampling burden to the digital signal processor. An analog to information converter (AIC) can randomly sample the received signal with sub-Nyquis...

By representing the input testing image as a sparse linear combination of the training samples via l1-norm minimization, sparse representation based classification (SRC) has shown promising results for face recognition (FR). Particularly, by introducing an identity occlusion dictionary to code the occluded portions of face images, SRC could lead to robust FR results against face occlusion. Howe...

In this paper, we investigate conditions for the unique recoverability of sparse integer-valued signals from few linear measurements. Both the objective of minimizing the number of nonzero components, the so-called l0-norm, as well as its popular substitute, the l1-norm, are covered. Furthermore, integer constraints and possible bounds on the variables are investigated. Our results show that th...