Optimizing a Linearly Constrained Quadratic Programming Problem Using Eigen-Value Decomposition and Resultant Vector Ascent Method

This paper suggests an iterative method for optimizing a Quadratic Programming Problem, constrained with a set of Linear Inequalities (less than type), regardless of the nature of the square matrix ( B ) within the objective function. To reach the global solution the entire travel is devised with a...

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Bibliographic Details
Published in:	International journal of applied and computational mathematics Vol. 11; no. 6; p. 231
Main Author:	Sarkar, Subhadip
Format:	Journal Article
Language:	English
Published:	New Delhi Springer India 01.12.2025 Springer Nature B.V
Subjects:	Applications of Mathematics Approximation Computational Science and Engineering Constraints Eigenvalues Eigenvectors Iterative methods Linear functions Literature reviews Mathematical and Computational Physics Mathematical Modeling and Industrial Mathematics Mathematics Mathematics and Statistics Matrices (mathematics) Methods Nuclear Energy Operations Research/Decision Theory Optimization Original Paper Production planning Quadratic programming Resultants Slack variables Theoretical Variables Global optimal solution Resultant Vector Ascent Method Quadratic Programming Null space Eigen Value Decomposition
ISSN:	2349-5103, 2199-5796
Online Access:	Get full text
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