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Convex underestimators of polynomials

机译:多项式的凸低估

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Convex underestimators of a polynomial on a box. Given a non convex polynomial f ∈ R[x] and a box B C R~n, we construct a sequence of convex polynomials (fdk) C R[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 ‖f - g‖_1 on B, over all convex degree-d polynomial underestimators g of . On a sample of problems with non convex /, we then compare the lower bounds obtained by minimizing the convex underestimator of / computed as above and computed via the popular αBB method and some of its other refinements. In most of all examples we obtain significantly better results even with the smallest value of k.
机译:盒子上多项式的凸低估量。给定一个非凸多项式f∈R [x]和一个框BCR〜n,我们构造了一个凸多项式(fdk)CR [x]序列,该序列在很强的程度上收敛为“最佳”(凸和度d )f的多项式低估f_d〜*。实际上,在的所有凸度d多项式低估量g上,f_d〜*最小化了B上的L_1范数“ f-g” _1。在非凸/的问题样本中,我们然后比较了通过最小化如上所述的/并通过流行的αBB方法及其一些其他改进而计算出的/的凸低估量而获得的下界。在大多数示例中,即使k最小,我们也可以获得明显更好的结果。

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