Efficient methods for large-scale structural optimization with application to machine learning

应用于机器学习的大规模结构优化的有效方法

• 批准号: RGPIN-2017-04169
• 负责人: Lu, Zhaosong
• 金额: $ 2.04万
• 依托单位: Simon Fraser University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=636762
• 关键词: Efficient; methods; large; scale; structural

Nowadays structural optimization problems frequently arise in engineering and sciences, especially in data analytics, machine learning and statistics. In big data era, these problems are often of high dimension and consist of large number of functions in their objective and/or constraints. This brings unprecedented challenges to traditional optimization methods. The objectives of this proposal are to propose efficient methods and develop software for solving them, and explore their applications in data analytics and machine learning.

如今，工程和科学中经常出现结构优化问题，尤其是在数据分析，机器学习和统计中。在大数据时代，这些问题通常具有很高的维度，并且由其目标和/或约束中的大量功能组成。这给传统优化方法带来了前所未有的挑战。该提案的目标是提出有效的方法并开发用于解决这些方法的软件，并探索其在数据分析和机器学习中的应用。