doi:http://hdl.handle.net/1903/14122Wang, RongrongDissertations & Theses - GradworksRongrong Wang. Global Geometric Conditions on Sensing Matrices for the Success of L1 Minimization Algorithm. PhD thesis, University of Maryland, College Park, 2013....
We develop a corresponding fast iterative truncation algorithm (FITRA) with l1 minimization and show numerical results to demonstrate tremendous PAR-reduction capability The significantly reduced linearity requirements ultimately enable the use of low-cost RF components for the large-scale MU-MIMO-OFDM ...
The algorithm consists of solving a sequence of weighted L1-minimization problems where the weights used for the next iteration are computed from the value of the current solution. We present a series of experiments demonstrating the remarkable performance and broad applicability of this algorithm in ...
L1 minimization 1. Gradient Based Sparse Coding (GB-SC). Refer to the paper "Transformation Invariant Sparse Coding". 2. Fast Iterative Shrinkage/Thresholding Algorithm (FISTA). Refer to the paper "A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems"...
Linearized Bregman algorithm is effective on solving l1-minimization problem, but its parameter's selection must rely on prior information. In order to ameliorate this weakness, we proposed a new algorithm in this paper, which combines the proximal point algorithm and the linearized Bregman iterative...
interior-point algorithm(IPA) based on preconditioned conjugate gradients(PCG) algorithm for solving L1-Min process in [3], which accounts for the major portion of computing time. ADM can be traced to the early works of [8], and its good performance is seen in [7], in which ADM sh...
L1 minimization 1. Gradient Based Sparse Coding (GB-SC). Refer to the paper "Transformation Invariant Sparse Coding". 2. Fast Iterative Shrinkage/Thresholding Algorithm (FISTA). Refer to the paper "A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems" 好文要顶 关注我 ...
It was proved that the iteration algorithm converges to the augmented minimization problem [4]. This paper mainly considers the measurement matrix which is generated by the Weibull random distribution. With the optimal number of the measurements, the stability of the augmented minimization model is ...
The main algorithm contains three phases. The first phase is to identify the outlier candidates which are likely to be corrupted by impulse noise. The second phase is to recover the image via dictionary learning on the free-outlier pixels. Finally, an alternating minimization algorithm is employed...
Under the sparse assumption,the problem of underdetermined blind source separation can be solved byl1-norm minimizationalgorithms such as the linear programming,the shortest-path algorithm,the combinatorial algo-rithm and so on. 基于稀疏假设,欠定盲源分离问题一般可采用线性规划、最短路径法和组合算法等l1...