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最小化l1范数的Matlab代码
- 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: minimize ||A*x-y||^2+ lambd
l1_ls
- 基于最小二乘思想解决l1范最小问题，对于初学压缩感知的同学有一定的帮助 -L1 norm minimization problem based on least squares ideology, compressed sensing for the beginner students some help
l1_ls_matlab_(new-2008)
- l1_ls 是一个目前最好的求解稀疏矩阵方程解的算法之一，这是作者发布的最新 MATLAB 源代码。-l1_ls: A Matlab solver for l1-regularized least squares problems. BETA version, May 10 2008 COPYRIGHT (c) 2007 Kwangmoo Koh, Seung-Jean Kim and Stephen Boyd. Permission to use, copy,
l1_ls
- 稀疏表示分类算法，用于样本分类的数学算法-Sparse that classification algorithms, mathematical algorithms for sample classification
l1_ls
- 求解l1范式的值，用于压缩感知中的稀疏表示。进行分类-Solving the value of l1 paradigm for compressed sensing of sparse representation. Classification
l1_ls_matlab
- 线性规划的内点法实现，里面包含有一个简单的例子。-l1_ls: A Matlab solver for l1-regularized least squares problems.
l1_ls.m
- l1_ls正则化算法可以用于图像重建，矩阵迭代求解运算，普遍优于其他同类算法。-li_ls
l1_ls_matlab
- 压缩感知，利用l1范数的重建方法：l1_ls.m，以及两个简单例子和说明文档-Using the l1 norm reconstruction method: l1_ls.m
l1_ls
- 基追踪算法，通用程序，稀疏性恢复、压缩感知两大算法之一-Base tracking algorithm, common procedures, sparsity recovery, one of the two compression algorithms perception
SR-survey-released
- 解各种优化方法的matlab开发包 Orthogonal Matching Pursuit (OMP) toolbox : http://www.cs.technion.ac.il/~ronrubin/software.html l1_ls toolbox: http://stanford.edu/~boyd/l1_ls/ FISTA : http://www.eecs.berkeley.edu/~yang/software/l1benchmark/ l1-Ho
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_magic
- l1_magic用于稀疏表示求解L1稀疏系数，特点是本程序是用于求解稀疏系数比l1_ls快-this procedure is used to solve the following problem。 min ||x||1 s.t. y=Ax
l1_ls
- 稀疏表示l1_ls算法,样本分类中很好用-Sparse representation l1_ls algorithm, sample classification is very good
l1_ls
- l1范数约束的使用最小二乘法计算观测信号的稀疏编码，(L1 norm constraint using the least squares method to calculate the observed signal sparse coding,)