搜索资源列表
single
- 使用奇异值分解来帮助求解最小二乘问题，特别是在方程系数矩阵不满秩的情况下。-SGELSD computes the minimum-norm solution to a real linear least * squares problem: * minimize 2-norm(| b- A*x |) * using the singular value decomposition (SVD) of A. A is an M-by-N * matrix which
Incremental-SVD-updates
- 增量奇异值分解算法，来至MIT大学的wingate教授，含3个源码.-Given the thin SVD of a matrix (X=USV ), update it in a number of interesting ways, while preserving the rank of the result. svd_update.m- update the SVD to be [X+ A *B]=Up*Sp*Vp (a general matrix update). add
mySVD
- 输入矩阵X和维数d进行SVD分解，使得Xhat = U*S*V 是X的所有秩为d的近似中最好的一个- Accelerated singular value decomposition.Xhat = U*S*V is the best approximation (with respect to F norm) of X among all the matrices with rank no larger than ReducedDim.
SVD
- calculate low rank matrix using SVD
MAT
- 仿MATLAB矩阵C++运算库,包括加、减、乘、除、点加、点减、点乘、点除、赋值、转置、rank、det、eig、svd、pinv、power等的运算。inv运算使用pinv运算。最难实现的是非方阵的除法。-MatLab Matrix simulator