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ABSTAYLOR: upper bounding with inner regions in nonlinear continuous global optimization problems

机译:ABStaylor:与非线性连续全球优化问题的内部区域的上限

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摘要

In this paper we propose AbsTaylor, a simple and quick method for extracting inner polytopes, i.e., entirely feasible convex regions in which all points satisfy the constraints. The method performs an inner linearization of the nonlinear constraints by using a Taylor form. Unlike a previous proposal, the expansion point of the Taylor form is not limited to the bounds of the domains, thus producing, in general, a tighter approximation. For testing the approach, AbsTaylor was introduced as an upper bounding method in a state-of-the-art global branch & bound optimizer. Furthermore, we implemented a local search method which extracts feasible inner polytopes for iteratively finding better solutions inside them. In the studied instances, the new method finds in average four times more inner regions and significantly improves the optimizer performance.
机译:在本文中,我们提出了ABStaylor,一种简单而快速地提取内部多粒子的方法,即,完全可行的凸起区域,其中所有点都满足约束。该方法通过使用泰勒形式执行非线性约束的内部线性化。与先前的提议不同,泰勒形式的扩张点不限于域的范围,从而产生一般来说,通常是更严格的近似。为了测试该方法,在最先进的全球分支和结合优化器中引入ABStaylor作为上限方法。此外,我们实现了一种本地搜索方法,其提取可行的内部多台,以便在它们内部迭代地找到更好的解决方案。在研究的实例中,新方法平均发现了四倍内部区域,并显着提高了优化器性能。

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