Adaptive optimization: parameter-free self-tuning algorithms beyond smoothness and convexity

Research Project

Project/Area Number	24K20737
Research Category	Grant-in-Aid for Early-Career Scientists
Allocation Type	Multi-year Fund
Review Section	Basic Section 60020:Mathematical informatics-related
Research Institution	Kyushu University
Principal Investigator	Themelis Andreas 九州大学, システム情報科学研究院, 准教授 (50898749)
Project Period (FY)	2024-04-01 – 2028-03-31
Project Status	Granted (Fiscal Year 2024)
Budget Amount *help	¥4,550,000 (Direct Cost: ¥3,500,000、Indirect Cost: ¥1,050,000) Fiscal Year 2027: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000) Fiscal Year 2026: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000) Fiscal Year 2025: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000) Fiscal Year 2024: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Keywords	Optimization algorithms / Open-source software
Outline of Research at the Start	This project aims at producing out-of-the-box parameter-free optimization algorithms for engineering applications. The focus is on "self-adaptive" methods that automatically tune parameters at execution time without subroutines. Main challenges involve nonconvexity and embracing Newton-type methods.