2018/04/16 by Weijie Zhong, Zhong, Weijie
Computer Science · Mathematics · #Advanced Multi-Objective Optimization Algorithms #Blind Source Separation Techniques #FOS: Mathematics #Machine Learning and Algorithms #Optimization and Control (math.OC) #math.OC
paper · pdf · doi:10.48550/arxiv.1804.05752
openalex publication_date 2018/04/16 · arxiv created 2018/09/04 · arxiv updated 2018/09/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Let V be the set of all combinations of expected value of finite objective functions from designing information. I showed that V is a compact and convex set implemented by signal structures with finite support when unknown states are finite. Moreover, V(μ) as a correspondence of prior is continuous. This result can be applied to develop a concavification method of Lagrange multipliers that works with general constrained optimization. It also provides tractability to a wide range of information design problems.