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Gauss_Seidel_iterative
- 迭代法是解线性代数方程组的另一类方法，特别适用于解大型稀疏线性方程组。它的基本思想是针对求解问题预先设计好某种迭代格式，从而产生求解问题的近似解迭代序列，在迭代序列收敛于精确解的情况下，按精度要求取某个迭代值作为问题解的近似值。迭代法具有原始系数举证始终不变，算法简单，编写程序较方便，所需存储单元较少的优点。-iterative method was the linear algebraic equations of the other methods, particularly applica
matlab
- 包括牛顿法，拉格朗日法，ＬＵ分解法，１００*１００阶稀疏矩阵方程组的解法等-Including the Newton method, Lagrangian method, LU decomposition method, 100* 100 band sparse matrix equations of the solution, etc.
SVregression
- In kernel ridge regression we have seen the final solution was not sparse in the variables ® . We will now formulate a regression method that is sparse, i.e. it has the concept of support vectors that determine the solution. The thing to not
multi-ctp1
- 一个基于阈值的粒子比较准则，用于处理多目标约束优化问题,该准则可以保留一部分序值较小且约束违反度在允许范围内的不可行解微粒,从而达到由不可行解向可行解进化的目的；一个新的拥挤度函数,使得位于稀疏区域和Pareto前沿边界附近的点有较大的拥挤度函数值,从而被选择上的概率也较大 从而构成解决多目标约束优化问题的混合粒子群算法。-A comparison based on the threshold criteria for the particle to handle multi-objective
Method-for-solving-EM-problems
- 提出了一种求解电磁场有限元 边界元混合法所生成的线性方程组的有效方法 ———内观 法结合多波前法.由于该线性方程组的系数是一个部分稀疏部分满填充的矩阵,为了加速求解,应用内观法将系数矩阵分为 2块,一块是有限元法形成的稀疏矩阵,另一块是边界元法生成的满阵,然后用多波前法求解稀疏矩阵方程,用高斯 约当消去法解满阵方程. 采用该方法,计算了二维多层介质柱体的雷达散射截面.计算结果表明,该方法的计算效率远远高于传统的高斯法.-Proposed for solving mixed finite el
demo_nnls
- PLS - DN是一个有限的牛顿为解决非退化的分段线性系统的算法。 PLS - DN的展品可证明半迭代 财产即在全球范围内的精确解在有限数量的迭代算法收敛。被证明是收敛速度 至少前终止线性。 广泛的算法是在我们的AISTATS 2011纸描述：“一个非退化的分段线性系统的有限牛顿算法”。此演示包重新运行 在解决非负最小二乘法（NNLS）问题上的三个稀疏的设计矩阵，从哈威尔波音收集（达夫等人，1989年）第4.2节的实验。 随着PLS - DN，此演示包建
lasso
- This code implements a variety of ways to solve sparse constraint solution
gauss-seidel
- The Gauss-Seidel technique is intuitive and easy to use on large or small problems. However, you should beware that the convergence tolerance must be carefully selected. Because the convergence criterion is based on the error between iterations,
gmres
- gmres 方法。视图求解线性方程组A*x=b的解x。nXn的稀疏矩阵A必须是方程且应是大型稀疏矩阵。列向量b的长度必须为n。-gmres method. View solving linear equations A* x = b the solution x. nXn sparse matrix A must be equation and should be large sparse matrix. The length of the column vector b must be to
Unitary--ESPRIT
- 新的2维角度估计的高分辨方法。该方法首先建立基于范数约束的最优化问题的目标函数；然后用迭代算法沿均匀面阵接收数据的方位向求最小化目标函数的稀疏解，得到方位、俯仰角耦合的空间角频率，并分离信号；最后对每个分离的信号，沿面阵俯仰向求稀疏解，得到信号的俯仰角，进而求得对应的方位角。-The new high-resolution 2-D angle estimation method. Firstly, the establishment of an objective function optimi
denoising
- The BEADS toolbox jointly addresses the problem of simulateous baseline correction and noise reduction, for positive and sparse signals arising in analytical chemistry (Raman, infrared, XRD, etc.), here applied to gas chromatography signals. The base
my_sparse
- 稀疏性最小二乘支持向量机，针对最小二乘支持向量机缺少稀疏性的问题，以特征提取为基础提出具有稀疏性解的最小瓶二乘支持向量机-Sparsity LSSVM for LSSVM lack sparsity issues to feature extraction is proposed based on the solution of sparse least squares support vector machine bottle
Approximate-Message-Passing-master
- 近似信息传递稀疏约束算法用来进行稀疏约束重建以及方程求解(Approximate-Message-Passing-algorithm which can be used to find the sparse solution of the equation)