Large scale nonsmooth, nonconvex optimisation

大规模非光滑、非凸优化

• 批准号: DP190100580
• 负责人: Prof Adil Bagirov
• 金额: $ 23.17万
• 依托单位: Federation University Australia
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2019
• 资助国家: 澳大利亚
• 起止时间: 2019-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP190100580
• 关键词: Large; scale; nonsmooth; nonconvex; optimisation

This project aims to develop, analyse, test and apply (sub) gradient-based methods for solving large scale nonsmooth, nonconvex optimisation problems. Large scale problems with complex nonconvex objective and/or constraint functions are among the most difficult in optimisation. This project will generate new knowledge in numerical optimisation and machine learning. The use of structures and sparsity of large scale problems will lead to the development of better models, and more accurate and robust methods. The expected outcomes of the project are ready-to-implement and apply numerical methods for solving large-scale, nonsmooth, nonconvex optimisation problems, as well as problems in machine learning and regression analysis.

该项目旨在开发，分析，测试和应用（子）基于梯度的方法来解决大规模非光滑，非凸优化问题。具有复杂非凸目标函数和/或约束函数的大规模优化问题是最困难的。该项目将产生数值优化和机器学习方面的新知识。大规模问题的结构和稀疏性的使用将导致更好的模型的发展，以及更准确和鲁棒的方法。该项目的预期成果是准备实施和应用数值方法来解决大规模，非光滑，非凸优化问题，以及在机器学习和回归分析的问题。