skip to main content

Title: Adaptive sparse tiling for sparse matrix multiplication
; ; ; ;
Award ID(s):
1816793 1645599
Publication Date:
Journal Name:
Proceedings of the 24th ACM Symposium on Principles and Practice of Parallel Programming
Page Range or eLocation-ID:
300 to 314
Sponsoring Org:
National Science Foundation
More Like this
  1. Sparse principal component analysis and sparse canonical correlation analysis are two essential techniques from high-dimensional statistics and machine learning for analyzing large-scale data. Both problems can be formulated as an optimization problem with nonsmooth objective and nonconvex constraints. Because nonsmoothness and nonconvexity bring numerical difficulties, most algorithms suggested in the literature either solve some relaxations of them or are heuristic and lack convergence guarantees. In this paper, we propose a new alternating manifold proximal gradient method to solve these two high-dimensional problems and provide a unified convergence analysis. Numerical experimental results are reported to demonstrate the advantages of our algorithm.