搜索资源列表
spgl1-1.9
- SPGL1，matlab toolbox 用于求解大尺度的L1范数的优化问题：BPDN，BP，LASSO。目前，这个是最新版本1.9.-SPGL1 is a Matlab solver for large-scale one-norm regularized least squares. It is designed to solve any of the following three problems:BPDN，BP，LASSO.SPGL1 relies only on matrix-vec
LPLQ
- 稀疏重构中的L1L0范数几何意义示意图，参数可调，对新入门学生具有很高的参考价值。-sparse representation algorithms based on l1 and l0, which is useful for the beginners.
H
- H无穷控制，使得控制状态对干扰的2范数比最优,应用于非线性控制，还有绘图程序-H infinity control, so that the control state 2 norm than optimal nonlinear control applied to the disturbance, and drawing program
ROF
- 利用TV作为正则项，L2范数作为数据项去噪的变分模型的代码-TV exploit code variational denoising model
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
cvx
- cvx toolbox 求解1范数稀疏问题-cvx toolbox
gampmatlab
- 最新的压缩感知算法：Generalized Approximate Message Passing及其衍生算法，性能逼近l1范数凸优化方法的阈值迭代算法。压缩包中包含了诸多例程。-The latest compressed sensing algorithm: Generalized Approximate Message Passing its derivative algorithms, performance approaching l1 norm convex optimization
algorithm-of-two-dimensional-random
- 基于1范数优化和二维随机映射的一种新的算法，实现图像识别，内容涉及到最先进的压缩感知采样-Based on 1 norm optimization and a new algorithm of two-dimensional random maps, image recognition, the content involves the most advanced compression perception sampling based on 1 norm optimization and a
NSL0-2D
- NSL0:基于光滑l0范数和修正牛顿法的压缩感知重建算法，是本人精心编写调试好的，请放心使用。-NSL0: compressive sensing reconstruction algorithm based on smooth l0 norm and modified Newton method
NSL0_2D_2
- 2维光滑l0范数和修正牛顿法的压缩感知重建算法，涉及两个测量矩阵，两个方向。是我精心编写的，能通过测试。-2D-NSL0: based on the smooth l0 norm and the modified Newton method of 2 dimensional compressive sensing reconstruction algorithm, involving two measurement matrix, two directions.
proximal
- 基于近邻算子的凸优化函数。实现一范数等不可导问题的优化。-Optimization function based on neighbor operator convex. Optimize a norm and other non-lead issues.
DNCV_CR_BF
- 对于声矢量圆阵，比较双重范数约束法和自适应抵消稳健波束形成法得到的波束图。-For acoustic vector circular array, dual comparator norm constraint offset robust and adaptive beamforming method by beam pattern.
SMI_FDL_NCCB_BF
- 对于声矢量圆阵，比较固定对角加载法（FDL）和双重范数约束法（NCCB）和常规（SMI）的自适应波束形成器的性能。-For acoustic vector circular array, relatively fixed diagonal loading method (FDL) and double norm constraint method (NCCB) and conventional (SMI) adaptive beamformer performance.
SINR_N
- 对于声矢量圆阵，比较固定对角加载法（FDL）、加权向量范数约束法（NCCB）两种算法输出信干噪比随快拍数变化。-For acoustic vector circular array, relatively fixed diagonal loading method (FDL), weighted vector norm constraint method (NCCB) two algorithms output SINR with snapshots change.
time_fractional-Eq
- 数值求解时间分数阶微分方程，空间部分采用有限元离散，最后可以验证误差的L2范数达到最优收敛阶-Numerical Solution of the Time Fractional differential equations, finite element discrete space segment, the last error can verify the L2 norm of optimal order of convergence
LRR-and-WNNM-LRR
- 该程序可实现低秩子空间聚类和加权核范数最小化低秩子空间聚类。参考文献：Guangcan Liu, Zhouchen Lin, Shuicheng Yan, Ju Sun, Yong Yu, Yi Ma, Robust recovery of subspace structures by low-rank representation, IEEE T. Pattern Anal. 35(1) (2013) 171-184.-This program can realize subspace clu
SL0MatlabCodeV2
- L0范数平滑处理算法：将无法优化的L0问题用平滑函数来近似，使得原问题可以进行优化处理 -matlab版-L0 norm smoothing algorithm
matlab_cvx
- 用于L1范数凸优化的一款matlab工具包，对压缩感知学者有一定帮助。-L1 norm for a convex optimization matlab toolkit, compression perception scholars have some help.
deburring
- 基于多层紧框架变换的L0范数正则化图像去模糊-L0 norm regularized image deblurring based on multilayer compact frame transformation
CS-recovery-LevelSet-Normals
- 压缩感知恢复算法，使用新的范数来提升图像恢复能力，包含论文和代码。-We propose a compressive sensing algorithm that exploits geometric properties of images to recover images of high quality few measurements. The image reconstruction is done by iterating the two following steps: 1) e