2) total least squares problem
多重整体最小二乘问题
3) least square problem
最小二乘问题
1.
This approach can guarantee that the identified parameters are optimal,by solving a one-dimensional polynomial equation instead of a nonlinear least square problem.
这种方法能够保证辨识出的参数是最佳的;而且不用求解对应的非线性最小二乘问题,只需求一元多项式的根,从而大大减少计算量。
2.
This survey is concerned with some recent results on convergence of the Newton method for solving nonlinear operator (equation) and the Gauss-Newton method for solving the least square problems and the convex composite optimization problems.
文章就求解方程最为重要的Newton法以及解非线性最小二乘问题和解非光滑复合凸优化问题的Gauss-Newton法的收敛性等问题的研究成果和进展作介绍。
3.
For solving large-scale sparse least square problems,the author discusses the convergence of two-block AOR iterative method;and gives the necessary and sufficient conditions and the domain of its convergence;and further demonstrates that the spectral radius p (L(2)γ,ω) of two-block AOR optimal iterative matrix.
讨论用2-块AOR迭代法解大型稀疏最小二乘问题的收敛性,给出其收敛的充要条件及其收敛域。
4) least squares problem
最小二乘问题
1.
In this paper,we study the convergence of USSOR iterative methods for solving least squares problems.
将文 [1 ]中求解最小二乘问题的 SOR迭代法推广到 USSOR迭代法 ,给出了 6种分裂形式下 ,USSOR迭代法的收敛域 。
2.
This paper deals with the problem of finding a solution of a constrained least squares problem from the solution set of its unconstrained problem.
本文主要研究线性矩阵方程CZC~T=T的最小二乘解与其若干线性约束最小二乘问题(包括AXA~T+BYB~T=T及其对称与反对称约束问题)解的关系。
5) least squares problem (Procrustes problem)
最小二乘问题(Procrustes问题)
6) Scaled Total Least Squares(STLS)
标度总体最小二乘
1.
The Scaled Total Least Squares(STLS) approach is a unification and generalization of the LS,Data Least Squares(DLS) and Total Least Squares (TLS) approaches,but its relation with the LM algorithm is not clear.
Levenberg-Marquard(tLM)算法与最小二乘(Least Square,LS)方法关系密切,标度总体最小二乘(Scaled Total Least Square,STLS)是最小二乘,数据最小二乘(Data Least Square,DLS)与总体最小二乘(Total Least Square,TLS)的统一与推广,但是它与LM算法的关系尚不清楚。
补充资料:非线性最小二乘拟合
分子式:
CAS号:
性质:用最小二乘法拟合非线性方程。有些变量之间的非线性模型,通过变量变换可以化为线性模型,此称为外在线性。而有些变量之间的非线性模型,通过变量变换不能化为线性模型,通称为内在非线性。对于非线性模型y=f(ξ,θ)+ε,其残差平方和。S(θ)是θ的函数,当模型关于θ是非线性的,正规方程关于θ也是非线性的。基于使残差平方和s(θ)达到极小的原理求出θ的估计值,拟合非线性回归方程。
CAS号:
性质:用最小二乘法拟合非线性方程。有些变量之间的非线性模型,通过变量变换可以化为线性模型,此称为外在线性。而有些变量之间的非线性模型,通过变量变换不能化为线性模型,通称为内在非线性。对于非线性模型y=f(ξ,θ)+ε,其残差平方和。S(θ)是θ的函数,当模型关于θ是非线性的,正规方程关于θ也是非线性的。基于使残差平方和s(θ)达到极小的原理求出θ的估计值,拟合非线性回归方程。
说明:补充资料仅用于学习参考,请勿用于其它任何用途。
参考词条