Nonlinear Optimization

On the use of Jordan Algebras for improving global convergence of an Augmented Lagrangian method in nonlinear semidefinite programming

  • Roberto Andreani
  • Ellen H. Fukuda
  • Gabriel Haeser
  • Daiana O. Santos
  • Leonardo D. Secchin
    • Categories Nonlinear Optimization, Semi-definite Programming Tags , , , ,

    Jordan Algebras are an important tool for dealing with semidefinite programming and optimization over symmetric cones in general. In this paper, a judicious use of Jordan Algebras in the context of sequential optimality conditions is done in order to generalize the global convergence theory of an Augmented Lagrangian method for nonlinear semidefinite programming. An approximate … Read more

    A Primal–Dual Penalty Method via Rounded Weighted-\boldmath{$\ell_1$} Lagrangian Duality

  • Regina S. Burachik
  • Christopher J. Price
  • C. Yalcin Kaya
    • Categories Constrained Nonlinear Optimization, Nonlinear Optimization

    We propose a new duality scheme based on a sequence of smooth minorants of the weighted-$\ell_1$ penalty function, interpreted as a parametrized sequence of augmented Lagrangians, to solve nonconvex and nonsmooth constrained optimization problems. For the induced sequence of dual problems, we establish strong asymptotic duality properties. Namely, we show that (i) the sequence of … Read more

    On Inexact Accelerated Proximal Gradient Methods with Relative Error Rules

  • Yunier Bello-Cruz
  • Max L. N. Gonçalves
  • Nathan Krislock
    • Categories Convex and Nonsmooth Optimization, Nonlinear Optimization Tags , , , , ,

    One of the most popular and important first-order iterations that provides optimal complexity of the classical proximal gradient method (PGM) is the “Fast Iterative Shrinkage/Thresholding Algorithm” (FISTA). In this paper, two inexact versions of FISTA for minimizing the sum of two convex functions are studied. The proposed schemes inexactly solve their subproblems by using relative … Read more

    Iteration-complexity of an inexact proximal accelerated augmented Lagrangian method for solving linearly constrained smooth nonconvex composite optimization problems

  • Jefferson Gonçalves Melo
  • Renato D.C. Monteiro
  • Hairong Wang
    • Categories Constrained Nonlinear Optimization Tags , , ,

    This paper proposes and establishes the iteration-complexity of an inexact proximal accelerated augmented Lagrangian (IPAAL) method for solving linearly constrained smooth nonconvex composite optimization problems. Each IPAAL iteration consists of inexactly solving a proximal augmented Lagrangian subproblem by an accelerated composite gradient (ACG) method followed by a suitable Lagrange multiplier update. It is shown that … Read more

    Data Approximation by L1 Spline Fits with Free Knots

  • Ziteng Wang
  • Manfei Xie
    • Categories Applications - OR and Management Sciences, Applications - Science and Engineering, Nonlinear Optimization

    L1 spline fits are a class of spline models that have shown advantages in approximating irregular and multiscale data. This paper investigates the knot placement problem of L1 spline fits under two scenarios. If the number of knots is given, we propose an augmented Lagrangian method to solve the bilevel L1 spline fit problem and … Read more

    Optimality Conditions for Constrained Minimax Optimization

  • Yu-Hong Dai
  • Liwei Zhang
    • Categories Constrained Nonlinear Optimization Tags , , , , ,

    Minimax optimization problems arises from both modern machine learning including generative adversarial networks, adversarial training and multi-agent reinforcement learning, as well as from tradition research areas such as saddle point problems, numerical partial differential equations and optimality conditions of equality constrained optimization. For the unconstrained continuous nonconvex-nonconcave situation, Jin, Netrapalli and Jordan (2019) carefully considered … Read more

    DMulti-MADS: Mesh adaptive direct multisearch for blackbox multiobjective optimization

  • Jean Bigeon
  • Sébastien Le Digabel
  • Ludovic Salomon
    • Categories Multi-Criteria Optimization, Nonlinear Optimization Tags , , , ,

    The context of this research is multiobjective optimization where conflicting objectives are present. In this work, these objectives are only available as the outputs of a blackbox for which no derivative information is available. This work proposes a new extension of the mesh adaptive direct search (MADS) algorithm to constrained multiobjective derivative-free optimization. This method … Read more

    A primal-dual interior-point relaxation method with adaptively updating barrier for nonlinear programs

  • Yu-Hong Dai
  • Yakui Huang
  • Xin-Wei Liu
    • Categories Nonlinear Optimization Tags , , , ,

    Based on solving an equivalent parametric equality constrained mini-max problem of the classic logarithmic-barrier subproblem, we present a novel primal-dual interior-point relaxation method for nonlinear programs. In the proposed method, the barrier parameter is updated in every step as done in interior-point methods for linear programs, which is prominently different from the existing interior-point methods … Read more

    Approximate solution of system of equations arising in interior-point methods for bound-constrained optimization

  • David Ek
  • Anders Forsgren
    • Categories Bound-constrained Optimization Tags , , ,

    The focus in this paper is interior-point methods for bound-constrained nonlinear optimization where the system of nonlinear equations that arise are solved with Newton’s method. There is a trade-off between solving Newton systems directly, which give high quality solutions, and solving many approximate Newton systems which are computationally less expensive but give lower quality solutions. … Read more

    Survey of Sequential Convex Programming and Generalized Gauss-Newton Methods

  • Katrin Baumgärtner
  • Moritz Diehl
  • Florian Messerer
    • Categories Constrained Nonlinear Optimization Tags , , , , ,

    We provide an overview of a class of iterative convex approximation methods for nonlinear optimization problems with convex-over-nonlinear substructure. These problems are characterized by outer convexities on the one hand, and nonlinear, generally nonconvex, but differentiable functions on the other hand. All methods from this class use only first order derivatives of the nonlinear functions … Read more