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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| Published in: | International journal of applied and computational mathematics Vol. 11; no. 6; p. 231 |
|---|---|
| Main Author: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
New Delhi
Springer India
01.12.2025
Springer Nature B.V |
| Subjects: | |
| ISSN: | 2349-5103, 2199-5796 |
| Online Access: | Get full text |
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| Summary: | 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 set of specially designed (
n – m
) (>
m
) vectors while incorporating a Resultant Vector Ascent Method (where
n
and
m represent the
number of variables (including slack variables) and constraints). These vectors are
located
in the null space of the
constraint
matrix and half of them are dependent on the Eigenvectors of
B
. The prescribed movements can optimize the problem with a time complexity of
and
without causing a looping problem during degeneracy
. |
|---|---|
| Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ISSN: | 2349-5103 2199-5796 |
| DOI: | 10.1007/s40819-025-02048-9 |