Globally solving the fractional squared least squares model for GPS localization

This study presents a new branch and bound algorithm designed for the global optimization of the fractional squared least squares model for GPS localization. The algorithm incorporates a novel underestimation approach that provides theoretically superior lower bounds while requiring a comparable com...

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Bibliographic Details
Published inNumerical algorithms Vol. 99; no. 4; pp. 1855 - 1870
Main Authors Cen, Xiaoli, Xia, Yong
Format Journal Article
LanguageEnglish
Published New York Springer US 01.08.2025
Springer Nature B.V
Subjects
Algebra
Algorithms
Branch & bound algorithms
Branch and bound methods
Computer Science
Global optimization
Global positioning systems
GPS
Least squares
Linear programming
Localization
Lower bounds
Normal distribution
Numeric Computing
Numerical Analysis
Optimization
Theory of Computation
Global optimization
Fractional programming
Branch and bound
GPS localization
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ISSN1017-1398
1572-9265
DOI10.1007/s11075-024-01935-4

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Summary:This study presents a new branch and bound algorithm designed for the global optimization of the fractional squared least squares model for GPS localization. The algorithm incorporates a novel underestimation approach that provides theoretically superior lower bounds while requiring a comparable computational effort to the current approach. Numerical results demonstrate the substantial efficiency enhancements of the proposed algorithm over the existing algorithm.
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ISSN:1017-1398
1572-9265
DOI:10.1007/s11075-024-01935-4