An Iterative Least-Square Method Suitable for Solving Large Sparse Matrices
نویسندگان
چکیده
منابع مشابه
Using an Efficient Penalty Method for Solving Linear Least Square Problem with Nonlinear Constraints
In this paper, we use a penalty method for solving the linear least squares problem with nonlinear constraints. In each iteration of penalty methods for solving the problem, the calculation of projected Hessian matrix is required. Given that the objective function is linear least squares, projected Hessian matrix of the penalty function consists of two parts that the exact amount of a part of i...
متن کاملFast Global Motion Estimation Via Iterative Least-Square Method
This paper presents a fast algorithm for global motion estimation based on Iterative LeastSquare Estimation (ILSE) technique. Compared with the traditional framework, three improvements were made to accelerate the computation progress. First, a new 3-parameter linear model, together with its solution using modified ILSE method, is proposed to describe and estimate global motion, which is simple...
متن کاملLSMR: An iterative algorithm for sparse least-squares problems
An iterative method LSMR is presented for solving linear systems Ax = b and leastsquares problems min ‖Ax−b‖2, with A being sparse or a fast linear operator. LSMR is based on the Golub-Kahan bidiagonalization process. It is analytically equivalent to the MINRES method applied to the normal equation ATAx = ATb, so that the quantities ‖Ark‖ are monotonically decreasing (where rk = b−Axk is the re...
متن کاملIterative-Promoting Variable Step-size Least Mean Square Algorithm For Adaptive Sparse Channel Estimation
Least mean square (LMS) type adaptive algorithms have attracted much attention due to their low computational complexity. In the scenarios of sparse channel estimation, zero-attracting LMS (ZA-LMS), reweighted ZA-LMS (RZA-LMS) and reweighted -norm LMS (RL1-LMS) have been proposed to exploit channel sparsity. However, these proposed algorithms may hard to make tradeoff between convergence speed ...
متن کاملInexact trust region method for large sparse nonlinear least squares
The main purpose of this paper is to show that linear least squares methods based on bidiagonalization, namely the LSQR algorithm, can be used for generation of trust region path. This property is a basis for an inexact trust region method which uses the LSQR algorithm for direction determination. This method is very efficient for large sparse nonlinear least squares as it is supported by numer...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: The Computer Journal
سال: 1963
ISSN: 0010-4620,1460-2067
DOI: 10.1093/comjnl/6.2.202