Scalable Algorithms for Deterministic Global Optimization With Parallel Architectures

使用并行架构实现确定性全局优化的可扩展算法

• 批准号: 2330054
• 负责人: Matthew Stuber
• 金额: $ 34.7万
• 依托单位: University of Connecticut
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-03-01 至 2027-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2330054&HistoricalAwards=false
• 关键词: Scalable; Algorithms; Deterministic; Global; Optimization

Complex systems are everywhere: from agricultural supply chains, wastewater treatment and public water systems, to the energy/power infrastructure that heats, cools, and light residential and industrial buildings. Decarbonizing process industries, especially as they relate to food, energy, and water, is of particular and timely importance. Engineers have an ever-constant mission of improving the health, safety, and robustness of these complex systems that support and improve society. However, innovation inherently increases complexity and, therefore, the efforts to solve complex and interconnected challenges, which include designing new systems and improving existing systems, rely heavily on computational modeling, simulation, and optimization-based approaches. There are two major challenges that this proposal aims to address: (1) the current performance of computational optimization approaches limits their applicability to simplified, lower-complexity problems, and (2) university engineering programs often lack cohesive computational-thinking activities throughout their curricula. Solving (1) will alleviate the current computational bottlenecks and broaden the scale and scope of complex problems that can be solved. Solving (2) will not only help train the next generation of engineers on computational modeling approaches but improve their overall problem-solving skills.The research objective of this project is to develop scalable deterministic global optimization (DGO) algorithms and open-source software implementations by exploiting alternative stream computing architectures for parallelization, to enable the solution of higher complexity models that include first-principles models and machine learning elements involving nonlinear (partial) differential and algebraic equations. The significance of the proposed work lies in unlocking the massive parallel computing performance of graphical processing units (GPUs) for DGO with the development of a new branch-and-bound deterministic search algorithm. The result will be a significant speedup over the current state of the art, which will enable the solution of larger-scale higher-complexity problems that arise in food-energy-water (FEW) applications, among others. The first major innovation is a method for automatically generating source code representations of convex/concave relaxations of nonconvex functions in the optimization formulation, and subgradients thereof, on arbitrary domains of interest. The second major innovation is a scalable GPU-compatible parallel DGO algorithm and open-source software implementation for the guaranteed solution of nonconvex programs. This project will align the proposed research with educational activities aimed at transforming a diverse cohort of students into skilled computational thinkers. This project will support the training of students to understand the complexity of systems models from an optimization context to better understand the practicality of optimization-based approaches. This project will deliver methods, tools, and training modules to serve the immediate and future technology workforce training needs of engineering fields that will increasingly depend on optimization for innovation.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

复杂的系统无处不在：从农业供应链，废水处理和公共供水系统到加热，冷却和轻型住宅和工业建筑的能源/电力基础设施。脱碳工艺行业，尤其​​是与食品，能源和水相关的行业，特别及时。工程师的任务是改善这些支持和改善社会的复杂系统的健康，安全性和鲁棒性。但是，创新固有地提高了复杂性，因此，解决复杂和相互联系的挑战的努力，包括设计新系统并改善现有系统，在很大程度上依赖计算建模，模拟和基于优化的方法。该提案旨在解决：（1）计算优化方法的当前表现将其适用性限制在简化，低复杂性问题上，并且（2）大学工程计划在整个课程中通常缺乏凝聚力的计算思维活动。解决（1）将减轻当前的计算瓶颈，并扩大可以解决的复杂问题的规模和范围。解决（2）不仅将帮助培训下一代工程师在计算建模方法上的培训，而且可以提高其整体问题解决技能。该项目的研究目标是开发可扩展的确定性全球优化（DGO）算法（DGO）算法（DGO）算法和开放源代码软件实现，并通过利用替代流动流量的替代流动体系结构的平行模型，以启用综合模型，以启用替代流程的架构，以启用第一份机器（启用原始的原则）（启用原始精确的元素）和代数方程。提出的工作的重要性在于解锁DGO的图形处理单元（GPU）的大量平行计算性能，并开发了新的分支和结合的确定性搜索算法。结果将是对当前最新状态的大幅度加速，这将使能够解决较大规模的更高复杂性问题，这些问题在食品能 - 水（少数）应用中等等。第一个主要创新是一种在优化公式中自动生成非凸功能函数凸的源代码表示的方法，并在优化公式和其亚级别中，在任意感兴趣的域上生成了源代码表示。第二个主要创新是可扩展的与GPU兼容的并行DGO算法和开源软件实现，以确保非Convex程序的解决方案。该项目将使拟议的研究与旨在将各种学生转变为熟练的计算思想家的教育活动保持一致。该项目将支持对学生的培训，以了解从优化环境中的系统模型的复杂性，以更好地了解基于优化的方法的实用性。该项目将提供方法，工具和培训模块，以满足工程领域的直接和未来的技术劳动力培训需求，这些培训将越来越依赖于对创新的优化。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的审查标准来通过评估来获得支持的。