A revision of the rectangular algorithm for a class of DC optimization problems

Takahito Kuno ()
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Takahito Kuno: University of Tsukuba

Journal of Global Optimization, 2022, vol. 83, issue 2, No 1, 187-200

Abstract: Abstract Every continuously differentiable function can be represented as a difference between a convex function and an additively separable convex function. We show that a DC function with this structure can be optimized using the rectangular algorithm for separable nonconvex optimization, and develop a revision to this algorithm for practical use. We also report some numerical results which indicate the effectiveness of the revision.

Keywords: Global optimization; DC optimization; Branch-and-bound; Rectangular algorithm; $$\omega $$ ω -subdivision (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s10898-021-01102-2

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