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All-integer global optimization for high-dimensional sparse regression, with applications in financial and genomic data | Synapse
March 3, 2026
All-integer global optimization for high-dimensional sparse regression, with applications in financial and genomic data
YN
Yuji Nakagawa
YH
Yoshiko Hanada
Kansai University
YT
Yoichi Takenaka
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Key Points
Global optimization significantly enhances high-dimensional analysis outcomes.
Financial data analysis yielded improved accuracy by 25% using sparse regression techniques.
Observational analysis employs advanced algorithms to optimize sparse regression models effectively.
Further validation may confirm the applicability of these methods in broader settings.
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Nakagawa et al. (Sun,) studied this question.
synapsesocial.com/papers/69a7603ac6e9836116a2cc3e
https://doi.org/https://doi.org/10.1016/j.ejor.2026.01.054