Logo

Exact Penalty Function for L21 Norm Minimization with Orthogonality Constraints 64

F597d8ae8d151979e1b8887f44cc82936979d17d

Speaker

Xin Liu, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, China

Time

2020.06.03 14:00-15:00

Venue

Online—ZOOM APP

ZOOM Info

Conference ID: 946-793-78842
PIN Code: 990733

Abstract

L21 norm minimization with orthogonality constraints, feasible region of which is called Stiefel manifold, has wide applications in statistics and data science. The state-of-the-art approaches adopt proximal gradient technique on either Stiefel manifold or its tangent spaces. The consequent subproblem does not have closed-form solution and hence requires an iterative procedure to solve, which is usually time consuming. In this paper, we discover that the Lagrangian multipliers of the orthogonality constraints in this class of problems are of closed-form expressions. By using this closed-form expression, we introduce a penalty function for this type of problems. We theoretically demonstrate the equivalence between the penalty function and the original L21 norm minimization under mild assumptions. Based on the exact penalty function, we propose an inexact proximal gradient method in which the subproblem is of closed-form solution. The global convergence and the worst case complexity are established. Numerical experiments illustrate the efficiency of our new proposed algorithm, when comparing with the existing methods.

Sponsors