Abstract
Convex underestimators of a polynomial on a box. Given a non convex polynomial \({f\in \mathbb{R}[{\rm x}]}\) and a box \({{\rm B}\subset \mathbb{R}^n}\), we construct a sequence of convex polynomials \({(f_{dk})\subset \mathbb{R}[{\rm x}]}\), which converges in a strong sense to the “best” (convex and degree-d) polynomial underestimator \({f^{*}_{d}}\) of f. Indeed, \({f^{*}_{d}}\) minimizes the L 1-norm \({\Vert f-g\Vert_1}\) on B, over all convex degree-d polynomial underestimators g of f. On a sample of problems with non convex f, we then compare the lower bounds obtained by minimizing the convex underestimator of f computed as above and computed via the popular α BB method and some of its other refinements. In most of all examples we obtain significantly better results even with the smallest value of k.
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Lasserre, J.B., Thanh, T.P. Convex underestimators of polynomials. J Glob Optim 56, 1–25 (2013). https://doi.org/10.1007/s10898-012-9974-4
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DOI: https://doi.org/10.1007/s10898-012-9974-4