搜索资源列表
l1_ls
- 最小化l1范数的Matlab代码。求解模型为： min lambda*|x|_1+||A*x-y||_2。其中，|x|_1表示x的1-范数，||*||_2表示2-范数。该模型在稀疏成分分析、压缩传感器等领域有广泛的用途。- l1-Regularized Least Squares Problem Solver l1_ls solves problems of the following form:
l1magic-1.1
- 最小化L1范数求解，通过L1-LS工具包。-L1 norm minimization solution, through the L1-LS kit.
l1_OMP_matlab
- 压缩感知 L1范数最小化算法正交匹配追踪法重构信号-compressive sensing L1-norm OMP signal reconstruction
YALL1-v1.3
- 求解L1范数最小化问题的凸优化工具包，共含有6个模型的求解方法-Solving the L1-norm minimization problem of convex optimization toolkit contains a total of six methods of solving the model
FPC_AS_v1.21
- 本程序是利用固定点迭代求解L1范数最小化的算法-This procedure is to use fixed-point iteration to solve the L1-norm minimization algorithm
L1_homotopy
- 本程序是利用同伦方法求解L1范数最小化的数值算法-This procedure is the use of homotopy methods to solve the L1-norm minimization numerical algorithm
BIHT-l1
- 为了解决二进制CS而编写的算法，使用了用l1范数最小化-an algorithm for binary CS，use l1 norm
YALL1-v1.4
- 新版的求解L1范数最小化问题的凸优化工具包-new version for solve L1-norm minimization problem of convex optimization
L1_homotopy_v2.0
- 同伦法L1范数最小化算法程序实现，并与其它最小化算法做比较-scr ipts for different problems are also included in this package to demonstrate the use of l1homotopy:
L1_norm_least_square_solver
- L1范数正则化最小二乘问题求解器，stanford大学Boyd教授出品。-Simple Matlab Solver for l1-regularized Least Squares Problems
l1eq_pd
- 稀疏表示，最小化l1范数求解表示系数的函数-sparse representation,minimum
l1_ls_matlab
- 稀疏表达中最小化l1范数问题的求解过程，matlab编写-Sparse expression l1 minimization process of solving the problem of norm, matlab write
YALL1-v1.4
- 求解压缩感知L1范数最小化的yall算法，基于内点法，-Solving compressed sensing L1 norm minimization yall algorithm, based on interior point methods,
l1_ls
- L1范数正则化最小二乘计算min||y-Ax||^2+lambd||x||问题最优解-Least square optimal solution for L1 regulation problem min||y-Ax||^2+lambd||x||
l1-Norm-Minimization
- 该文章介绍了L1范数最小化问题稀疏求解的快速算法-This article describes a quick way L1 norm minimization problem solving sparse
l1magic-1.1
- 对l1最小化的处理，其中包括全面的l1范数的解得算法，运用tv全变分最小的解决方法，适合于单像素以及图像处理的研究者参考。(L1 minimization, including the full L1 norm solution algorithm, the use of TV total variation, the smallest solution, suitable for single pixel and image proc
l1magic
- 实现压缩感知的稀疏信号恢复，采用L1范数约束最小化策略(Sparse signal recovery with compressed sensing, by using the L1 norm constraint minimization strategy)
L1范数代码
- 动态压缩感知（DSC）是压缩感知领域中一个重要的研究分支，它是近几年新兴起的一种信号处理与分析方法，与传统的压缩感知理论不同，DSC研究的对象是稀疏时变信号，并且已在视频信号处理和动态核磁共振成像等方面显示出了强大的应用潜力。本节正是在此基础上，提出了一种用于多普勒频率跟踪估计的DSC方法。首先，通过前一跟踪时刻所得到的先验DOA稀疏信息，获得当前跟踪时刻信号向量中各位置非零元素的分布概率，继而建立起动态DOA的稀疏概率模型。然后，采用