Convex Optimization

A generalized block-iterative projection method for the common fixed point problem induced by cutters

  • Yair Censor
  • Daniel Reem
  • Maroun Zaknoon
    • Categories Convex Optimization, Nonlinear Optimization Tags , , , ,

    The block-iterative projections (BIP) method of Aharoni and Censor [Block-iterative projection methods for parallel computation of solutions to convex feasibility problems, Linear Algebra and its Applications 120, (1989), 165-175] is an iterative process for finding asymptotically a point in the nonempty intersection of a family of closed convex subsets. It employs orthogonal projections onto the … Read more

    Convexity and continuity of specific set-valued maps and their extremal value functions

  • Gabriele Eichfelder
  • Tobias Gerlach
  • Stefan Rocktäschel
    • Categories Convex Optimization, Generalized Convexity/Monoticity, Nonlinear Optimization Tags , , ,

    In this paper, we study several classes of set-valued maps, which can be used in set-valued optimization and its applications, and their respective maximum and minimum value functions. The definitions of these maps are based on scalar-valued, vector-valued, and cone-valued maps. Moreover, we consider those extremal value functions which are obtained when optimizing linear functionals … Read more

    Convergence Results for Primal-Dual Algorithms in the Presence of Adjoint Mismatch

  • Andres Contreras
  • Émilie Chouzenoux
  • Jean-Christophe Pesquet
  • Marion Savanier
    • Categories Convex Optimization, Nonsmooth Optimization Tags , , , , , , ,

    Most optimization problems arising in imaging science involve high-dimensional linear operators and their adjoints. In the implementations of these operators, approximations may be introduced for various practical considerations (e.g., memory limitation, computational cost, convergence speed), leading to an adjoint mismatch. This occurs for the X-ray tomographic inverse problems found in Computed Tomography (CT), where the … Read more

    Convergence analysis of an inexact relaxed augmented Lagrangian method

  • Jianchao Bai
  • Yuxue Ma
    • Categories Convex Optimization Tags , , , , ,

    In this paper, we develop an Inexact Relaxed Augmented Lagrangian Method (IR-ALM) for solving a class of convex optimization problems. Flexible relative error criteria are designed for approximately solving the resulting subproblem, and a relaxation step is exploited to accelerate its convergence numerically. By a unified variational analysis, we establish the global convergence of this … Read more

    Spectral Projected Subgradient Method for Nonsmooth Convex Optimization Problems

  • Nataša Krejić
  • Nataša Krklec Jerinkić
  • Tijana Ostojić
    • Categories Constrained Nonlinear Optimization, Convex and Nonsmooth Optimization, Convex Optimization Tags , , , , ,

    We consider constrained optimization problems with a nonsmooth objective function in the form of mathematical expectation. The Sample Average Approximation (SAA) is used to estimate the objective function and variable sample size strategy is employed. The proposed algorithm combines an SAA subgradient with the spectral coefficient in order to provide a suitable direction which improves … Read more

    Limits of eventual families of sets with application to algorithms for the common fixed point problem

  • Yair Censor
  • Eliahu Levy
    • Categories Convex Optimization, Nonlinear Optimization Tags , , , , , , ,

    We present an abstract framework for asymptotic analysis of convergence based on the notions of eventual families of sets that we define. A family of subsets of a given set is called here an “eventual family” if it is upper hereditary with respect to inclusion. We define accumulation points of eventual families in a Hausdorff … Read more

    A line search based proximal stochastic gradient algorithm with dynamical variance reduction

  • Giorgia Franchini
  • Federica Porta
  • Valeria Ruggiero
  • Ilaria Trombini
    • Categories Convex Optimization Tags , , ,

    Many optimization problems arising from machine learning applications can be cast as the minimization of the sum of two functions: the first one typically represents the expected risk, and in practice it is replaced by the empirical risk, and the other one imposes a priori information on the solution. Since in general the first term … Read more

    Adaptive Third-Order Methods for Composite Convex Optimization

  • Geovani Nunes Grapiglia
  • Yurii Nesterov
    • Categories Convex Optimization Tags , ,

    In this paper we propose third-order methods for composite convex optimization problems in which the smooth part is a three-times continuously differentiable function with Lipschitz continuous third-order derivatives. The methods are adaptive in the sense that they do not require the knowledge of the Lipschitz constant. Trial points are computed by the inexact minimization of … Read more

    New Penalized Stochastic Gradient Methods for Linearly Constrained Strongly Convex Optimization

  • Meng Li
  • Paul Grigas
  • Alper Atamturk
    • Categories Constrained Nonlinear Optimization, Convex Optimization Tags , , , ,

    For minimizing a strongly convex objective function subject to linear inequality constraints, we consider a penalty approach that allows one to utilize stochastic methods for problems with a large number of constraints and/or objective function terms. We provide upper bounds on the distance between the solutions to the original constrained problem and the penalty reformulations, … Read more

    An inexact ADMM with proximal-indefinite term and larger stepsize

  • Yuxue Ma
  • Jianchao Bai
    • Categories Convex Optimization Tags , , , ,

    In this paper, an inexact Alternating Direction Method of Multipliers (ADMM) has been proposed for solving the two-block separable convex optimization problem subject to linear equality constraints. The first resulting subproblem is solved inexactly under relative error criterion, while another subproblem called regularization problem is solved inexactly by introducing an indefinite proximal term. Meanwhile, the … Read more