Next-Generation Servers for Optimization as an Internet Resource

今天，更广泛地使用优化模型和方法的主要障碍是沟通。 越来越多的求解器实现得越来越好，但潜在用户不知道它们，或者看不到安装它们的好处。 建模系统在支持新的求解器方面也往往很慢，特别是那些解决新问题类型的求解器。 通过Internet将优化问题发送到某个远程站点的求解器，是解决大规模优化中的通信问题的一种越来越实用的方法。 远程优化服务器可以适应多种问题类型，并可以为每种类型的问题提供不同的求解器，为建模人员提供比本地更多的选择。 然而，当前的优化服务器仅开始解决大规模优化的通信挑战，涉及求解器选择、调度、基准测试和建模系统交互，并且受到早期设计决策的限制。 计划中的研究的动机是下一代服务器的愿景，该服务器解决了大规模优化中通信的突出挑战。 这项工作将解决设计以及标准化的问题表示，自动化的问题分析和求解器的选择，与新的Web服务标准，调度计算资源，基准求解器，并验证结果-所有的大规模优化的特殊要求的背景下所提出的实施问题。 NEOS项目旨在使优化成为支持科学、工程和商业的全球软件基础设施的一部分。 优化分析复杂的成本最小化和利润最大化问题，涉及大量不同但相互关联的决策。 分析是通过将问题表示为数学模型来完成的，这些数学模型与各种计算求解器的数据进行通信。 近地天体服务器提供对大约50个解算器的远程访问，因此是计划中研究的重点。 优化工具的现成可用性具有广泛的好处，既直接给从业者带来好处，也间接提高了优化技术的研究和教育质量。 此外，通过鼓励在解算器之间进行比较，近地天体服务器的各种解算器和接口也促进了优化软件的进一步改进。

The primary impediment to broader use of optimization models and methods today is one of communication. Increasing numbers of solvers are implemented increasingly well, but prospective users are unaware of them or do not see the benefit that would justify installing them. Modeling systems also tend to be slow to support new solvers, especially ones that address new problem types. Sending optimization problems over the Internet, to a solver at some remote site, is an increasingly practical way of addressing communication problems in large-scale optimization. A remote optimization server can accommodate numerous problem types and can provide varied solvers for problems of each type, giving modelers much more of a choice than they have locally. Yet current optimization servers only begin to address the communication challenges of large-scale optimization with respect to solver choice, scheduling, benchmarking, and modeling system interaction, and are limited by early design decisions. The planned research is motivated by a vision of a next-generation server that addresses outstanding challenges of communication in large-scale optimization. This work will address design as well as implementation issues posed by standardizing problem representations, automating problem analysis and solver choice, working with new web-service standards, scheduling computational resources, benchmarking solvers, and verification of results - all in the context of the special requirements of large-scale optimization. The Network Enabled Optimization System (NEOS) project seeks to make optimization a part of the worldwide software infrastructure that supports science, engineering, and commerce. Optimization analyzes complex cost-minimization and profit-maximization problems that involve large numbers of distinct but interrelated decisions. The analysis is accomplished by representing problems as mathematical models that are communicated with data to a variety of computational solvers. The NEOS Server provides remote access to about 50 solvers and hence is the focus of the planned research. The ready availability of optimization tools has widespread benefits, both directly to practitioners, and indirectly by improving the quality of research and education in optimization techniques. By encouraging comparison between solvers, moreover, the NEOS Server's variety of solvers and interfaces also serves as a spur to further improvement of optimization software.