Convex optimization in the theory and practice of statistical estimation, prediction, and inference

统计估计、预测和推理的理论和实践中的凸优化

• 批准号: RGPIN-2015-05062
• 负责人: Mizera, Ivan
• 金额: $ 1.46万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=590197
• 关键词: Convex; optimization; theory; practice; statistical

The proposed research elaborates on the experience the applicant gained in about past ten years of his research, concentrating on methods where convex, especially constrained and non-differentiable optimization plays a pivotal role. The impact of convex optimization is not limited only to the algorithmic aspects of the considered methods, but transcends also into their theory - the mathematics of convex optimization, in particular the study of the dual versions of the investigated formulations, is employed to obtain additional theoretical insights. These include viable strategies for the inference, the probabilistic evaluation of their results.

拟议的研究阐述了申请人在过去十年的研究中获得的经验，集中在凸优化，特别是约束和不可微优化发挥关键作用的方法上。凸优化的影响不仅限于所考虑的方法的算法方面，但也超越到他们的理论-凸优化的数学，特别是研究的双重版本的调查配方，用于获得额外的理论见解。这些包括可行的推理策略，其结果的概率评估。