Applications – Science and Engineering

A new sufficient condition for non-convex sparse recovery via weighted $\ell_r\!-\!\ell_1$ minimization

  • Jianwen Huang
  • Feng Zhang
    • Categories Data-Mining, Nonsmooth Optimization Tags , , ,

    In this letter, we discuss the reconstruction of sparse signals from undersampled data, which belongs to the core content of compressed sensing. A new sufficient condition in terms of the restricted isometry constant (RIC) and restricted orthogonality constant (ROC) is first established for the performance guarantee of recently proposed non-convex weighted $\ell_r-\ell_1$ minimization in recovering … Read more

    A level-set-based topology optimization strategy using radial basis functions and a Hilbertian velocity extension

  • Giovanna Castelloa Andrade
  • Sandra A. Santos
    • Categories Mechanical Engineering, Multidisciplinary Design Optimization, Optimization of Systems modeled by PDEs Tags , , , ,

    This work addresses the structural compliance minimization problem through a level-set-based strategy that rests upon radial basis functions with compact support combined with Hilbertian velocity extensions. A consistent augmented Lagrangian scheme is adopted to handle the volume constraint. The linear elasticity model and the variational problem associated with the computation of the velocity field are … Read more

    Benders-type Branch-and-Cut Algorithms for Capacitated Facility Location with Single-Sourcing

  • Dieter Weninger
  • Laurence A. Wolsey
    • Categories (Mixed) Integer Linear Programming, Branch and Cut Algorithms, Facility Planning and Design Tags , , , ,

    We consider the capacitated facility location problem with (partial) single-sourcing (CFLP-SS). A natural mixed integer formulation for the problem involves 0-1 variables x_j indicating whether faclility j is used or not and y_{ij} variables indicating the fraction of the demand of client i that is satisfied from facility j. When the x variables are fixed, … Read more

    The Null Space Property of the Weighted $\ell_r-\ell_1$ Minimization

  • Jianwen Huang
  • Xinling Liu
    • Categories Data-Mining, Nonsmooth Optimization, Robust Optimization Tags , , ,

    The null space property (NSP), which relies merely on the null space of the sensing matrix column space, has drawn numerous interests in sparse signal recovery. This article studies NSP of the weighted $\ell_r-\ell_1$ minimization. Several versions of NSP of the weighted $\ell_r-\ell_1$ minimization including the weighted $\ell_r-\ell_1$ NSP, the weighted $\ell_r-\ell_1$ stable NSP, the … Read more

    Riemannian Stochastic Proximal Gradient Methods for Nonsmooth Optimization over the Stiefel Manifold

  • Bokun Wang
  • Shiqian Ma
  • Lingzhou Xue
    • Categories Nonlinear Optimization, Statistics

    Riemannian optimization has drawn a lot of attention due to its wide applications in practice. Riemannian stochastic first-order algorithms have been studied in the literature to solve large-scale machine learning problems over Riemannian manifolds. However, most of the existing Riemannian stochastic algorithms require the objective function to be differentiable, and they do not apply to … Read more

    Continuous Covering on Networks: Strong Mixed Integer Programming Formulations

  • Mercedes Pelegrín
  • Liding Xu
    • Categories (Mixed) Integer Linear Programming, Facility Planning and Design, Network Optimization Tags , , ,

    Covering problems are well-studied in the domain of Operations Research, and, more specifically, in Location Science. When the location space is a network, the most frequent assumption is to consider the candidate facility locations, the points to be covered, or both, to be discrete sets. In this work, we study the set-covering location problem when … Read more

    The Combinatorial Brain Surgeon: Pruning Weights That Cancel One Another in Neural Networks

  • Xin Yu
  • Thiago Serra
  • Shandian Zhe
  • Srikumar Ramalingam
    • Categories Data-Mining, Meta Heuristics, Quadratic Programming Tags , ,

    Neural networks tend to achieve better accuracy with training if they are larger — even if the resulting models are overparameterized. Nevertheless, carefully removing such excess parameters before, during, or after training may also produce models with similar or even improved accuracy. In many cases, that can be curiously achieved by heuristics as simple as … Read more

    Mixed-Integer Programming Techniques for the Minimum Sum-of-Squares Clustering Problem

  • Jan Pablo Burgard
  • Carina Moreira Costa
  • Christopher Hojny
  • Kleinert Thomas
  • Martin Schmidt
    • Categories (Mixed) Integer Nonlinear Programming, Cutting Plane Approaches, Data-Mining Tags , , ,

    The minimum sum-of-squares clustering problem is a very important problem in data mining and machine learning with very many applications in, e.g., medicine or social sciences. However, it is known to be NP-hard in all relevant cases and to be notoriously hard to be solved to global optimality in practice. In this paper, we develop … Read more

    Global Convergence of Sub-gradient Method for Robust Matrix Recovery: Small Initialization, Noisy Measurements, and Over-parameterization

  • Jianhao Ma
  • Salar Fattahi
    • Categories Statistics, Unconstrained Optimization Tags ,

    In this work, we study the performance of sub-gradient method (SubGM) on a natural nonconvex and nonsmooth formulation of low-rank matrix recovery with $\ell_1$-loss, where the goal is to recover a low-rank matrix from a limited number of measurements, a subset of which may be grossly corrupted with noise. We study a scenario where the … Read more