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L1-Norm 优化求解
- 稀疏性方程组的优化求解算法，利用L1-Norm求解最稀疏的方程组解。主要用于压缩感知领域。
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
基于matlab的稀疏表示中L1范数计算源码
- 基于matlab的稀疏表示中L1范数计算源码,the codes for L1 in sparsty representation with matlab
l1magic
- 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
LogisticR
- Logistic Loss with the L1-norm Regularization
l1magic-1.1
- L1算法稀疏重构算法的matlab工具箱-L1 sparse reconstruction algorithm matlab toolbox
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
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
code
- 稀疏编码的工具包，用matlab实现，数学上是求解l1 norm最小化-toolkit for sparse coding
l1benchmark
- l1benchmark 这个算法包提供了十种求解带稀疏约束的矩阵方程 AX=b 的 MATLAB 实现代码，并提供了一个比较各种算法求解结果的演示。-An L1-norm minimization benchmark package, which contains an implementation of ten L1-norm minimization algorithms in MATLAB. The package also provides a test scr ipt for comp
libsvm-2.89
- 是一種線性方成的分類器。SVM透過統計的方式將雜亂的資料以NN的方式分成兩類，以便處理。LIBLINEAR is a linear classifier for data with millions of instances and features. It supports L2-regularized logistic regression (LR), L2-loss linear SVM, and L1-loss linear SVM. -Main features of LIBLINEA
l1magic-1.1.tar
- l1-magic Compressive Sensing 简单工具包-l1-magic Compressive Sensing
CS
- L1-MAGIC is a collection of MATLAB routines for solving the convex optimization programs central to compressive sampling. The algorithms are based on standard interior-point methods, and are suitable for large-scale problems
l1magic-1.1-(new)
- L1-magic 是一个求解矩阵方程稀疏解的工具包，这是原作者发布的最新 MATLAB 源代码。- l1 magic (new) -------- This package contains code for solving seven optimization problems. A detailed explanation is given in the file l1magic.pdf.
l1_cs
- 对lena.map先分块处理，然后做cs变换，观测矩阵用随机高斯矩阵，重构算法用l1算法-On lena.map first block processed, and then do cs transform, random Gaussian matrix with the observation matrix, reconstruction algorithm algorithm using l1
L1precision
- matlab L1 precision using MAP
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
LogisticC
- Logistic Loss with the L1 ball constraint
SparseDOA
- 使用l1-svd的方法求解DOA估计问题(Using the l1-svd method to solve the DOA estimation problem)