重新审视安全分支定界算法中的上界过程
Revisiting the upper bounding process in a safe Branch and Bound algorithm
AI总结：
针对安全分支定界算法中可行点求解困难的问题，提出一种利用牛顿法处理欠约束系统并结合线性松弛最优解高效计算上界的新策略，在Coconuts基准测试中表现优异。
摘要：
在处理连续问题的安全分支定界算法中，找到证明成功的可行点是一个关键问题。本文介绍了一种新策略，用于计算可行点的非常精确的近似值。该策略利用了牛顿法处理欠约束方程组和不等式系统。更准确地说，它利用问题的线性松弛的最优解来高效计算有前景的上界。在Coconuts基准测试上的初步实验表明，这种方法非常有效。
英文摘要：
Finding feasible points for which the proof succeeds is a critical issue in safe Branch and Bound algorithms which handle continuous problems. In this paper, we introduce a new strategy to compute very accurate approximations of feasible points. This strategy takes advantage of the Newton method for under-constrained systems of equations and inequalities. More precisely, it exploits the optimal solution of a linear relaxation of the problem to compute efficiently a promising upper bound. First experiments on the Coconuts benchmarks demonstrate that this approach is very effective.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
