搜索资源列表
CVX
- 斯坦福工具箱 压缩感知 凸优化算法 必备 完整凸优化算法(Standford toolbox, compressed sensing, convex optimization algorithm, necessary complete convex optimization algorithm)
lmi-cvx
- cognitive radio beamfor
data_SVD
- 阵列信号处理方面，基于相关矩阵的稀疏重构，利用cvx工具箱求解；DOA估计(In array signal processing, sparse reconstruction based on correlation matrix is solved by CVX toolbox, and DOA estimation is used)
cvx-w64
- CS BP算法的matlab实现程序，结构清晰，注释完整(CS BP algorithm matlab implementation procedures, clear structure, complete notes)
cvx-w64
- Digital Signal and Image processing using MATLAB Gerard Blanchet
cvx
- 可以用于重建密度矩阵，计算速度较慢，但是精确度较高(this can be used for the density reconstruction)
cvxCertificate
- 具体的，用cvx重建密度矩阵，精确度较高，运算速度较慢(this can be used for the density matrix reconstruction)
TV simulation
- simple cvx problem total variation tval3
cvx
- 凸优化工具，matlab中初始化后可以用来构造转换滤波器。(tool box to design filter)
Matlab_SVM
- SVM算法实现+数据 (要用到一些包，按照代码里面的import到网站下就行） 1.读取数据：在Matlab中调用textread可读取UCI数据集，这里读取的文件是iris.data，因为文件中以逗号为分隔符，所以还要在读取方法中添加参数“‘delimiter’，‘，’”，从而在读数据的时候自动跳过分隔符。 2.调用cvx工具箱中的方法：首先需要下载cvx工具箱的压缩文件，在其目录下运行cvx_setup指令，然后调用其方法，以cvx_begin开头，cvx_end为终止符号，所有需
YALMIP-master
- DESCRIBES THE SOLVER USED IN THE CVX
main_uca_cvx
- DOA ESTIMATION - COMPERES SENSING - UCA - CVX
respect
- sdp cvx for toa in mimo
Optimization II
- Finding the global solution
cvx-w64
- I want to simulate the MAC channel with rayleigh fading using successive interference cancellation showing that the pantagon region is achievable.
cvx-toolbox
- 凸优化问题的工具箱，内附使用说明，可以参考学习(The toolbox of convex optimization problem is enclosed with instructions for reference)
CVX
- 一种很好的凸优化工具箱，自带说明文档及实例，非常好用！(A good convex optimization toolbox, with explanatory documents.)
Code
- 凸优化教程学习以及Matlab例程使用，里面有各种方法的structure和example(convex optimization learning programs in Matlab, including structure guides and examples of various methods)
MIMO实验
- MIMO通信系统最优信号传输问题,注水算法的实验(Optimal signal transmission in MIMO communication system and experiment of water injection algorithm)
凸优化cvx软件包
- 确定目标函数为凸函数后，对函数进行最优化处理(After determining that the objective function is convex, the function is optimized)