搜索资源列表
Algebra
- 基本矩阵运算 : + - *, power, transpose, trace, determinant, minor, matrix of minor, cofactor, matrix of cofactor, adjoint, inverse, gauss, gaussjordan, linear transformation, LU decomposition , Gram-Schmidt process, similarity. b) Basic vectors functions :
MINNORM
- mini Norm算法Matlab代码, DOA方向估计中最常用最简单的算法之一-mini Norm algorithm Matlab code, DOA direction of the most commonly used estimate the simplest algorithm
h12
- C环境下矩阵运算类,包含矩阵的基本操作,另有其范数,均值等特殊函数-C environment matrix category, including the basic matrix operations, otherwise its norm, the mean number of special function
linear_system_identification.tar
- The main features of the considered identification problem are that there is no an a priori separation of the variables into inputs and outputs and the approximation criterion, called misfit, does not depend on the model representation. The misfit is
weighted_total_least_squares_approximation
- The toolbox solves a variety of approximate modeling problems for linear static models. The model can be parameterized in kernel, image, or input/output form and the approximation criterion, called misfit, is a weighted norm between the given data an
TVAL3
- %TVDENOISE Total variation grayscale and color image denoising % u = TVDENOISE(f,lambda) denoises the input image f. The smaller % the parameter lambda, the stronger the denoising. % % The output u approximately minimizes the Rudin-Osher-Fatemi (ROF)
TVL1_HCS_v1
- % May 2010 % This matlab code implements TVL1 based Hybrid Compressive Sensing using LSQR. % Only suitable the small scale data due to the costly storage and computation. % % A - M x N measurement matrix: random sampling alone or hybrid sampling (ran
TwIST_v2
- % demo_l2_l1 - This demo illustrates the TwIST % algorithm in the l2-l1 optimization problem % % xe = arg min 0.5*||A x-y||^2 + tau ||x||_1 % x % % where A is a generic matrix and ||.||_1 is the l1 norm. % After obtaining the solution we implement a
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。
Source
- l1-norm, compress sensing
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
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
vector
- class for work with 3d Vectors: add, sub, saclar multiple, cross multiple, norm, div
Mapack_for_NET
- Mapack可用来做矩阵运算 Mapack is a .NET class library for basic linear algebra computations. It supports the following matrix operations and properties: Multiplication, Addition, Subtraction, Determinant, Norm1, Norm2, Frobenius Norm, Infinity Norm, Rank,
Compressive_Sensing
- lp范数最小化求解的问题，关于压缩感知的最新文档-lp-norm minimization problem solving, perception of the latest documentation on the compression
DOA
- methods of Direction of arrival estimation. (DOA) such as MUSIC, MVDR, Min-Norm and Classical Beamforme.
minimum-norm-Doppler-frequency
- 基于LPNM信道模型，通过最小范数求多径不相关信道的各径的各个载波的多普勒频率。-minimum norm multipath frequency by LPNM
Norm-of-matrix
- 各类矩阵的范数，包含行范数，列范数，以及2范数，有利于解决各种范数问题-Norm of matrix norm, contains the row, column norm, and 2 norm, is conducive to resolving the various norm problem
L1-norm-unliner
- 最优化一范数的线性拟合，巧妙转化为线性回归问题，避免了一范数不可微的缺点-Optimization of a linear fit the norm, cleverly converted into a linear regression problem, avoiding a non-differentiable norm shortcomings
l1-norm-recovery
- the recovery of the 2D SAR image with l1-norm minimization