hBcgoodness

Sparse Recovery on Euclidean Jordan Algebras

  • Lingchen Kong
  • Jie Sun
  • Jiyuan Tao
  • Naihua Xiu
    • Categories Convex Optimization, Linear, Cone and Semidefinite Programming Tags , , , , ,

    We consider the sparse recovery problem on Euclidean Jordan algebra (SREJA), which includes sparse signal recovery and low-rank symmetric matrix recovery as special cases. We introduce the restricted isometry property, null space property (NSP), and $s$-goodness for linear transformations in $s$-sparse element recovery on Euclidean Jordan algebra (SREJA), all of which provide sufficient conditions for … Read more

    Sufficient Conditions for Low-rank Matrix Recovery,Translated from Sparse Signal Recovery

  • Lingchen Kong
  • Levent Tunçel
  • Naihua Xiu
    • Categories Convex and Nonsmooth Optimization, Nonlinear Optimization Tags , , ,

    The low-rank matrix recovery (LMR) is a rank minimization problem subject to linear equality constraints, and it arises in many fields such as signal and image processing, statistics, computer vision, system identification and control. This class of optimization problems is $NP$-hard and a popular approach replaces the rank function with the nuclear norm of the … Read more