2010•Unpublished venueRequires access

A modified mnemonic enhancement optimization method for solving parametric nonlinear programming problems

Zhiqiang Wang, Zhijiang Shao, Xueyi Fang, Weifeng Chen, Jiaona Wan

Abstract

A mnemonic enhancement optimization framework based on radial basis function (RBF-MEO), which is concerned with the application of RBF interpolation for generation of starting points in parametric nonlinear optimization, is studied in this work. Some theories of interior point algorithm support that the RBF-MEO method is very suitable for collaborating with interior point solvers, such as IPOPT. Numerical experiments illustrate that good accuracy and high rate of convergence are obtained by IPOPT with RBF-MEO.

About this research paper

What this paper is about

Why it matters

OpenAlex reports 1 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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: Interior point method, Mathematical optimization, Parametric statistics, Nonlinear programming, Interpolation (computer graphics), Radial basis function, Computer science, Convergence (economics)