Development of a Global Optimization Methodology to Support Engineering Design and Manufacturing

9414615 萨欣什 该奖项为寻找非凸非线性问题（NLP）和混合整数问题（MINLP）的全局解决方案的算法研究提供资金。 该方法是基于一系列的凸低估子问题所产生的进化细分的搜索区域的解决方案。 该算法的关键组成部分是新的基于最优性和可行性的范围减少测试。 前者使用已知的可行解和扰动结果，以排除考虑的搜索区域的劣部分，而后者分析约束，以获得有效的不等式。 该算法将这些设备与一个有效的本地搜索启发式。 在工程设计中，非线性优化方法常用于设计参数的选择。 如果成功的话，该算法可以更快地以更低的成本找到更优化的设计。

9414615 Sahinidis This award provides funding for research toward an algorithm for finding global solutions to nonconvex, nonlinear problem (NLPs) and mixed-integer problems (MINLPs). The approach is based on the solution of a sequence of convex underestimating subproblems generated by evolutionary subdivision of the search region. The key components of the algorithm are new optimality-based and feasibility-based range reduction tests. The former use known feasible solutions and perturbation results to exclude inferior parts of the search region from consideration, while the latter analyze constraints to obtain valid inequalities. The algorithm integrates these devices with an efficient local search heuristic. Nonlinear optimization is often useful in engineering design for the selection of design parameters. If successful, the algorithm could enable more optimal designs to be found more rapidly and at lower cost.