A Brief Description of the Levenberg-Marquardt Algorithm Implemented by levmar
A Brief Description of the Levenberg-Marquardt Algorithm Implemented by levmar
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发表时间：
2005
影响因子：
5.3
通讯作者：
Manolis I. A. Lourakis
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文献类型：
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作者：
Manolis I. A. Lourakis
Levenberg-Marquardt （LM）算法是一种迭代技术，它定位函数的最小值，该函数表示为非线性函数的平方和。它已经成为非线性最小二乘问题的标准技术，可以被认为是最陡下降法和高斯-牛顿法的结合。本文档简要描述了levmar背后的数学原理，这是一个免费的LM C/ c++实现，可以在http://www.ics.forth.gr/ / lourakis/levmar找到。
The Levenberg-Marquardt (LM) algorithm is an iterative technique that locates the minimum of a function that is expressed as the sum of squares of nonlinear functions. It has become a standard technique for nonlinear least-squares problems and can be thought of as a combination of steepest descent and the Gauss-Newton method. This document briefly describes the mathematics behind levmar, a free LM C/C++ implementation that can be found at http://www.ics.forth.gr/ ̃lourakis/levmar.