Hao Wang

Efficient Low-rank Identification via Accelerated Iteratively Reweighted Nuclear Norm Minimization

  • Xiangyu Yang
  • Hao Wang
    • Categories Nonsmooth Optimization, Optimization in Data Science Tags , , , ,

    \(\) This paper considers the problem of minimizing the sum of a smooth function and the Schatten-\(p\) norm of the matrix. Our contribution involves proposing accelerated iteratively reweighted nuclear norm methods designed for solving the nonconvex low-rank minimization problem. Two major novelties characterize our approach. Firstly, the proposed method possesses a rank identification property, enabling … Read more

    Iterative Reweighted Linear Least Squares for Exact Penalty Subproblems on Product Sets

  • James V. Burke
  • Frank E. Curtis
  • Hao Wang
  • Jiashan Wang
    • Categories Constrained Nonlinear Optimization, Convex Optimization, Nonlinear Optimization Tags , , , , ,

    We present two matrix-free methods for solving exact penalty subproblems on product sets that arise when solving large-scale optimization problems. The first approach is a novel iterative reweighting algorithm (IRWA), which iteratively minimizes quadratic models of relaxed subproblems while automatically updating a relaxation vector. The second approach is based on alternating direction augmented Lagrangian (ADAL) … Read more

    A Sequential Quadratic Optimization Algorithm with Rapid Infeasibility Detection

  • James V. Burke
  • Frank E. Curtis
  • Hao Wang
    • Categories Constrained Nonlinear Optimization, Nonlinear Optimization Tags , , , , ,

    We present a sequential quadratic optimization (SQO) algorithm for nonlinear constrained optimization. The method attains all of the strong global and fast local convergence guarantees of classical SQO methods, but has the important additional feature that fast local convergence is guaranteed when the algorithm is employed to solve infeasible instances. A two-phase strategy, carefully constructed … Read more