A Double Tracking Method for Optimization with Decentralized Generalized Orthogonality Constraints

信息管理与工程学院308室
上海财经大学（第三教学楼西侧）

上海市杨浦区武东路100号

个人简介：刘歆，中国科学院数学与系统科学研究院“冯康首席研究员”，博士生导师，计算数学与科学工程计算研究所副所长。

刘歆2004年本科毕业于北京大学数学科学学院；并于2009年在中国科学院数学与系统科学研究院获得博士学位。主要研究方向包括流形优化、分布式优化及其在材料计算、大数据分析和机器学习等领域的应用。刘歆分别于2016年与2021年获得国家自然科学基金委优秀青年科学基金项目和杰出青年科学基金项目的资助。2024年获得中国工业与应用数学学会萧树铁应用数学奖。现担任MPC, JCM, JIMO, APJOR等国内外期刊编委，《中国科学·数学》（中英文）青年编委，《计算数学》副主编；中国科学院青年创新促进会理事长；中国运筹学会常务理事；中国工业与应用数学会副秘书长，中国数学会计算数学分会常务理事。

We consider the decentralized optimization problems with generalized orthogonality constraints, where both the objective function and the constraint exhibit a distributed structure. Such optimization problems, albeit ubiquitous in practical applications, remain unsolvable by existing algorithms in the presence of distributed constraints. To address this issue, we convert the original problem into an unconstrained penalty model by resorting to the recently proposed constraint-dissolving operator. However, this transformation compromises the essential property of separability in the resulting penalty function, rendering it impossible to employ existing algorithms to solve. We overcome this di culty by introducing a novel algorithm that tracks the gradient of the objective function and the Jacobian of the constraint mapping simultaneously. The global convergence guarantee is rigorously established with an iteration complexity. To substantiate the effectiveness and efficiency of our proposed algorithm, we present numerical results on both synthetic and real-world datasets.