Paper ID sheet UCL-INMA-2008.127


Low-rank optimization on the cone of positive semidefinite matrices

M. Journée, F. Bach, P.-A. Absil, R. Sepulchre
We propose an algorithm for solving optimization problems defined on a subset of the cone of symmetric positive semidefinite matrices. This algorithm relies on the factorization $X=YY^T$, where the number of columns of $Y$ fixes an upper bound on the rank of the positive semidefinite matrix $X$. It is thus very effective for solving problems that have a low-rank solution. The factorization $X=YY^T$ leads to a reformulation of the original problem as an optimization on a particular quotient manifold. The present paper discusses the geometry of that manifold and derives a second-order optimization method with guaranteed quadratic convergence. It furthermore provides some conditions on the rank of the factorization to ensure equivalence with the original problem. In contrast to existing methods, the proposed algorithm converges monotonically to the sought solution. Its numerical efficiency is evaluated on two applications: the maximal cut of a graph and the problem of sparse principal component analysis.
Key words
low-rank constraints; cone of symmetric positive definite matrices; Riemannian quotient manifold; sparse principal component analysis; maximum-cut algorithms; large-scale algorithms
BibTeX entry

  author = "M. Journée and F. Bach and P.-A. Absil and R. Sepulchre",
  title= "Low-Rank Optimization on the Cone of Positive Semidefinite Matrices",
  journal = "SIAM J. Optim",
  fjournal = "SIAM Journal on Optimization",
  year = 2010,
  volume = 20,
  number = 5,
  pages = "2327--2351",
On page 2337 (published version), in the last equation for D alpha_i [Z], insert a "Trace( ... )" operation around (Z^TA_i^2 Y + Y^T A_i^2 Z); likewise for (D zeta [Z] A_i Y + zeta^T A_i Z).
Further information