<ul><li>Best subset selection in linear regression is a nonconvex problem that is challenging to solve.</li><li>Finding the global optimal solution via an exact optimization method may be impractical for high-dimensional problems.</li><li>This study introduces a new suboptimal procedure for best subset selection in linear regression.</li><li>Comparative experiments with synthetic and real data show the competitive performance of the new algorithm.</li></ul>

Solving the Best Subset Selection Problem via Suboptimal Algorithms

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