Convex Optimization

Optimal subgradient algorithms with application to large-scale linear inverse problems

  • Masoud Ahookhosh
    • Categories Convex and Nonsmooth Optimization, Convex Optimization, Nonsmooth Optimization Tags , , , , , , , , ,

    This study addresses some algorithms for solving structured unconstrained convex optimization problems using first-order information where the underlying function includes high-dimensional data. The primary aim is to develop an implementable algorithmic framework for solving problems with multi-term composite objective functions involving linear mappings using the optimal subgradient algorithm, OSGA, proposed by {\sc Neumaier} in \cite{NeuO}. … Read more

    String-Averaging Expectation-Maximization for Maximum Likelihood Estimation in Emission Tomography

  • Yair Censor
  • Álvaro Rodolfo De Pierro
  • Elias Salomão Helou
  • Tai-Been Chen
  • I-Liang Chern
  • Ming Jiang
  • Henry Horng-Shing Lu
    • Categories Biomedical Applications, Convex Optimization Tags , , , , , ,

    We study the maximum likelihood model in emission tomography and propose a new family of algorithms for its solution, called String-Averaging Expectation-Maximization (SAEM). In the String-Averaging algorithmic regime, the index set of all underlying equations is split into subsets, called “strings,” and the algorithm separately proceeds along each string, possibly in parallel. Then, the end-points … Read more

    OSGA: A fast subgradient algorithm with optimal complexity

  • Arnold Neumaier
    • Categories Convex Optimization, Nonsmooth Optimization Tags , , , , , , , ,

    This paper presents an algorithm for approximately minimizing a convex function in simple, not necessarily bounded convex domains, assuming only that function values and subgradients are available. No global information about the objective function is needed apart from a strong convexity parameter (which can be put to zero if only convexity is known). The worst … Read more

    Feasibility-Seeking and Superiorization Algorithms Applied to Inverse Treatment Planning in Radiation Therapy

  • Yair Censor
  • Ran Davidi
  • Sarah Geneser
  • Reinhard W. Schulte
  • Lei Xing
    • Categories Biomedical Applications, Convex Optimization Tags , , , ,

    We apply the recently proposed superiorization methodology (SM) to the inverse planning problem in radiation therapy. The inverse planning problem is represented here as a constrained minimization problem of the total variation (TV) of the intensity vector over a large system of linear two-sided inequalities. The SM can be viewed conceptually as lying between feasibility-seeking … Read more

    A Block Successive Upper Bound Minimization Method of Multipliers for Linearly Constrained Convex Optimization

  • Tsung-Hui Chang
  • Zhi-Quan Luo
  • Shiqian Ma
  • Mingyi Hong
  • Meisam Razaviyayn
  • Xiangfeng Wang
    • Categories Convex Optimization, Nonsmooth Optimization Tags , ,

    Consider the problem of minimizing the sum of a smooth convex function and a separable nonsmooth convex function subject to linear coupling constraints. Problems of this form arise in many contemporary applications including signal processing, wireless networking and smart grid provisioning. Motivated by the huge size of these applications, we propose a new class of … Read more

    Extreme point inequalities and geometry of the rank sparsity ball

  • Dmitriy Drusvyatskiy
  • Stephen A. Vavasis
  • Henry Wolkowicz
    • Categories Convex Optimization Tags , , , , ,

    We investigate geometric features of the unit ball corresponding to the sum of the nuclear norm of a matrix and the l_1 norm of its entries — a common penalty function encouraging joint low rank and high sparsity. As a byproduct of this effort, we develop a calculus (or algebra) of faces for general convex … Read more

    Spectral Operators of Matrices

  • Chao Ding
  • Defeng Sun
  • Kim-Chuan Toh
  • Jie Sun
    • Categories Convex Optimization, Nonsmooth Optimization

    The class of matrix optimization problems (MOPs) has been recognized in recent years to be a powerful tool by researchers far beyond the optimization community to model many important applications involving structured low rank matrices. This trend can be credited to some extent to the exciting developments in the emerging field of compressed sensing. The … Read more

    Lagrangian-Conic Relaxations, Part II: Applications to Polynomial Optimization Problems

  • Naohiko Arima
  • Sunyoung Kim
  • Masakazu Kojima
  • Kim-Chuan Toh
    • Categories Convex Optimization, Semi-definite Programming Tags , , , ,

    We present the moment cone (MC) relaxation and a hierarchy of sparse Lagrangian-SDP relaxations of polynomial optimization problems (POPs) using the unified framework established in Part I. The MC relaxation is derived for a POP of minimizing a polynomial subject to a nonconvex cone constraint and polynomial equality constraints. It is an extension of the … Read more

    Lagrangian-Conic Relaxations, Part I: A Unified Framework and Its Applications to Quadratic Optimization Problems

  • Naohiko Arima
  • Sunyoung Kim
  • Masakazu Kojima
  • Kim-Chuan Toh
    • Categories Convex Optimization, Linear, Cone and Semidefinite Programming Tags , , , , ,

    In Part I of a series of study on Lagrangian-conic relaxations, we introduce a unified framework for conic and Lagrangian-conic relaxations of quadratic optimization problems (QOPs) and polynomial optimization problems (POPs). The framework is constructed with a linear conic optimization problem (COP) in a finite dimensional vector space endowed with an inner product, where the … Read more

    Generalized Gauss Inequalities via Semidefinite Programming

  • Paul J. Goulart
  • Daniel Kuhn
  • Bart Paul Gerard van Parys
    • Categories Convex Optimization, Statistics, Stochastic Programming Tags , ,

    A sharp upper bound on the probability of a random vector falling outside a polytope, based solely on the first and second moments of its distribution, can be computed efficiently using semidefinite programming. However, this Chebyshev-type bound tends to be overly conservative since it is determined by a discrete worst-case distribution. In this paper we … Read more