Efficient Interior-Point Methods for Mixed-Integer Nonlinear and Conic Programming

混合整数非线性和圆锥规划的高效内点方法

• 批准号: 0725692
• 负责人: Hande Benson
• 金额: $ 6万
• 依托单位: Drexel University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-01 至 2009-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0725692&HistoricalAwards=false
• 关键词: Efficient; Interior; Point; Methods; Mixed

Optimization problems arise in engineering, business, and medicine.Computational studies show that interior-point methods outperform others for solving large-scale problems. This research involves the development of techniques to improve interior-point methods for nonlinear and related optimization problems. Its intellectual merit is the solution of complex problems and their re-solution under changing conditions, two current deficiencies that must be addressed to keep these methods at the forefront of optimization technology. Test problems benefit the optimization community and researchers in applied fields by encouraging the use of appropriate modeling and solution techniques and the development of new ones as necessary. The broadest impact of this research is the social and financial gains achieved by solving problems that better reflect the real-world and by responding more effectively to a dynamic environment.In order to improve and extend the use of interior-point methods, the investigators study (1) primal-dual penalty methods to improve warm-starting capabilities for solving closely related problems with changing data and problem size, (2) bilevel frameworks for solving mixed-integer nonlinear problems, with improved warm-starting and infeasibility identification schemes for nonlinear subproblems, and (3) incorporation of equilibrium and cone constraints into the nonlinear optimization framework using the primal-dual penalty approach. Another component of this research is the development and dissemination of a repository of applied problems with a variety of components, such as nonlinear functions, discrete variables, equilibrium constraints, and cone constraints, along with alternate data sets and parameter settings suitable for testing warm-start approaches. All work is incorporated into graduate courses and research. Course notes and test models are distributed on the WWW, and software is made available for free use on the NEOS Server.

优化问题出现在工程、商业和医学中。计算研究表明，内点方法在解决大规模问题方面优于其他方法。本研究涉及改进非线性及相关优化问题的内点法的技术发展。它的智力优势在于复杂问题的解决和在变化条件下的再解决，这是当前必须解决的两个缺陷，以保持这些方法在优化技术的前沿。测试问题通过鼓励使用适当的建模和解决技术以及开发必要的新技术，使优化社区和应用领域的研究人员受益。这项研究最广泛的影响是通过解决更好地反映现实世界和更有效地应对动态环境的问题而获得的社会和经济收益。为了改进和扩展内点方法的应用，研究者研究了(1)原始对偶惩罚方法，以提高解决数据和问题规模变化密切相关问题的热启动能力；(2)求解混合整数非线性问题的双层框架，改进了非线性子问题的热启动和不可行性识别方案；(3)采用原始-对偶惩罚方法将平衡约束和锥约束纳入非线性优化框架。本研究的另一个组成部分是开发和传播具有各种组成部分的应用问题库，例如非线性函数，离散变量，平衡约束和锥约束，以及适用于测试热启动方法的替代数据集和参数设置。所有工作都纳入研究生课程和研究。课程笔记和测试模型在WWW上发布，软件在NEOS服务器上免费提供。