2004•Unpublished venueRequires access

Nonlinear Survey Data Processing Based on the Conjugate Gradient Method and the Steepest Descent Method

Ning Wei, Qing Xi-hong

Abstract

The conjugate gradient method and the steepest descent method are combined, and a new method-mixed algorithm method for solving the problem of nonlinear survey data processing is created in this paper. The mixed method makes use of their good convergence merit, and raises the convergence rate of the conjugate gradient method and solves the problem for which the steepest descent method can not solve in the condition with bad characteristics for objective function. Compared with the conjugate method or the steepest descent method, the method has features with quick convergence rate, large convergence range and wide accommodation.

About this research paper

What this paper is about

Why it matters

A significance statement is not available in the OpenAlex record.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available abstract

Key concepts: Nonlinear conjugate gradient method, Conjugate gradient method, Gradient descent, Method of steepest descent, Derivation of the conjugate gradient method, Rate of convergence, Conjugate residual method, Convergence (economics)