搜索资源列表
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最小化L1范数求解，通过L1-LS工具包。-L1 norm minimization solution, through the L1-LS kit.
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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
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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
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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
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Logistic Loss with the L1-norm Regularization subject to non-negative constraint
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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
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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
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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
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the recovery of the 2D SAR image with l1-norm minimization
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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,
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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
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l1 norm minimization
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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)
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