Open AccessImproved Algorithm for Nonlinear Programming with Inequality Constraints Based on Big Data Framework
Author(s) -
Yuying Zhou
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1881/2/022060
Subject(s) - convergence (economics) , algorithm , iterative method , mathematical optimization , computer science , nonlinear programming , nonlinear system , mathematics , physics , quantum mechanics , economics , economic growth
With the development of network technology and electronic information technology, the nonlinear programming problem (NPP) with inequality constraints in the BD framework has become one of the research hotspots and one of the trends that need improvement. Due to the introduction of BD technology, there is still a lack of research on the situation where the objective function is a NPP. The purpose of this article is to use the analysis and computing power of BD technology to simulate and calulate nonlinear programming (NP) with inequality constraints and improve it. In this paper, with the help of stability conditions, matrix splitting and iteration theory, the alternate direction method is used to solve the NPP with inequality constraints, and the principle of compressed mapping is used to finally prove the convergence of the alternate direction method. Matrix splitting techniques and Gauss-Seidel iterative ideas construct an iterative algorithm for solving linear systems. The iterative format of the alternating direction method for solving NPPs with inequality constraints and the equivalent matrix format are given to prepare for further proof of the convergence of the algorithm. Experimental research shows that the NPP model with inequality constraints designed by the dynamic BP technology error correction GPC algorithm used in this article has faster response time and smaller overshoot, and the control effect is better than 20% of the traditional GPC algorithm.