A framework of constraint preserving update schemes for optimization on Stiefel manifold

• 成果类型: Article
• 署名作者: Jiang, Bo; Dai, Yu-Hong
• 署名单位: Chinese Academy of Sciences; Academy of Mathematics & System Sciences, CAS
• 刊物名称: MATHEMATICAL PROGRAMMING
• ISSN/ISSBN: 0025-5610
• DOI: 10.1007/s10107-014-0816-7
• 发表日期: 2015
• 页码: 535-575
• 关键词: line search technique; gradient-method; rank reduction; barzilai; algorithms; minimization; SUBJECT

摘要

This paper considers optimization problems on the Stiefel manifold (XX)-X-T = I-p, where X is an element of R-nxp is the variable and I-p is the p-by-p identity matrix. A framework of constraint preserving update schemes is proposed by decomposing each feasible point into the range space of X and the null space of X-T. While this general framework can unify many existing schemes, a new update scheme with low complexity cost is also discovered. Then we study a feasible Barzilai-Borwein-like method under the new update scheme. The global convergence of the method is established with an adaptive nonmonotone line search. The numerical tests on the nearest low-rank correlation matrix problem, the Kohn-Sham total energy minimization and a specific problem from statistics demonstrate the efficiency of the new method. In particular, the new method performs remarkably well for the nearest low-rank correlation matrix problem in terms of speed and solution quality and is considerably competitive with the widely used SCF iteration for the Kohn-Sham total energy minimization.