Applications – Science and Engineering

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

    Adaptive Nonlinear Optimization of District Heating Networks Based on Model and Discretization Catalogs

  • Hannes Dänschel
  • Volker Mehrmann
  • Marius Roland
  • Martin Schmidt
    • Categories Applications - Science and Engineering, Nonlinear Optimization, Systems governed by Differential Equations Optimization Tags , ,

    We propose an adaptive optimization algorithm for operating district heating networks in a stationary regime. The behavior of hot water flow in the pipe network is modeled using the incompressible Euler equations and a suitably chosen energy equation. By applying different simplifications to these equations, we derive a catalog of models. Our algorithm is based … Read more

    A semidefinite programming approach for the projection onto the cone of negative semidefinite symmetric tensors with applications to solid mechanics

  • Cristina Padovani
  • Margherita Porcelli
    • Categories Civil and Environmental Engineering, Linear, Cone and Semidefinite Programming Tags , , ,

    We propose an algorithm for computing the projection of a symmetric second-order tensor onto the cone of negative semidefinite symmetric tensors with respect to the inner product defined by an assigned positive definite symmetric fourth-order tensor C. The projection problem is written as a semidefinite programming problem and an algorithm based on a primal-dual path-following … Read more

    Stable Recovery of Sparse Signals With Non-convex Weighted $r$-Norm Minus $1$-Norm

  • Jianwen Huang
  • Feng Zhang
  • Xinling Liu
  • Jianjun Wang
    • Categories Constrained Nonlinear Optimization, Data-Mining, Nonsmooth Optimization Tags , , ,

    Given the measurement matrix $A$ and the observation signal $y$, the central purpose of compressed sensing is to find the most sparse solution of the underdetermined linear system $y=Ax+z$, where $x$ is the $s$-sparse signal to be recovered and $z$ is the noise vector. Zhou and Yu \cite{Zhou and Yu 2019} recently proposed a novel … Read more

    Distributionally Robust Modeling of Optimal Control

  • Alexander Shapiro
    • Categories Control Applications, Dynamic Programming, Stochastic Programming Tags , , , , , , ,

    The aim of this paper is to formulate several questions related to distributionally robust Stochastic Optimal Control modeling. As an example, the distributionally robust counterpart of the classical inventory model is discussed in details. Finite and infinite horizon stationary settings are considered. ArticleDownload View PDF