搜索资源列表
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-slove
- 压缩感知中求解最优L1范数问题的BP算法内含指导文章-Compressed sensing in L1 norm to solve the problem of optimal BP algorithm article contains guidance
rw_l1
- 用reweighted L1优化进行压缩感知的信号重建算法-Optimized by reweighted L1 signal is compressed sensing reconstruction algorithm
nnLogisticR
- Logistic Loss with the L1-norm Regularization subject to non-negative constraint
SolveHomotopy
- SolveHomotopy.m- l1 minimization Algorithm-SolveHomotopy.m-l1 minimization Algorithm
l1eq_pd
- SPARSE ESTIMATION Sparse signal recovery via L_1 minimization. This code is the basis in the L1 magic toolbox for sampling signals that are sparse in the time domain.
l1magic-1.1
- L1 magic L1eq_pd. This code solve linear problem with l1 minimization method.
Final_Face_recog
- Face recognition using L1 norm minimization 1.0 :Read the following paper for details of the algorithm - Robust Face Recognition via Sparse Representation by John Wright, Arvind Ganesh, and Yi Ma , Coordinated Science Laboratory, University of Illino
Spectral Projected Gradient for L1 minimization
- SPGL1 is a Matlab solver for large-scale one-norm regularized least squares.It is designed to solve any of the following three problems: 1. Basis pursuit denoise (BPDN): minimize ||x||_1 subject to ||Ax - b||_2 <= sigma, 2. Basis pursuit (BP): min
l1-algorithm
- 该软件包包含了合并执行在MATLAB9升-1的最小化算法。每个函数都使用一组参数是一致的（如停止准则和公差）与我们的基准脚本接口。 正交匹配追踪：SolveOMP.m 原对偶内点法：SolvePDIPA.m 梯度投影：SolveL1LS.m 同伦：SolveHomotopy.m 迭代阈值：SolveSpaRSA.m 近梯度：SolveFISTA.m TFOCS：SolveTFOCS.m SesopPCD：SolveSesopPCD.m 原始增强拉格朗日乘子：S
l1 minimization
- minimization algorithm for cs recovery
L1Solvers
- 文章《Fast l-1 Minimization Algorithms: Homotopy and Augmented Lagrangian Method Implementation Fixed-Point MPUs to Many-Core CPUs/GPUs》提供的benchmark，解决一些L1范数优化问题。-Article " Fast l-1 Minimization Algorithms: Homotopy and Augmented Lagrangian Metho
OMPmatlab-compression-0075_www.matlabsite.com
- l1 norm minimization
SolveAMP
- L1范最小化算法，匹配追踪算法，MATLAB语言实现，可以直接用(L1 norm Minimization Algorithm)