Nonlinear Optimization

Generalized Stochastic Frank-Wolfe Algorithm with Stochastic “Substitute” Gradient for Structured Convex Optimization

  • Robert M. Freund
  • Haihao Lu
    • Categories Convex Optimization, Nonlinear Optimization, Stochastic Programming Tags , , ,

    The stochastic Frank-Wolfe method has recently attracted much general interest in the context of optimization for statistical and machine learning due to its ability to work with a more general feasible region. However, there has been a complexity gap in the guaranteed convergence rate for stochastic Frank-Wolfe compared to its deterministic counterpart. In this work, … Read more

    Significant Generalization of the Convergence Proof for the Direct Transcription Method for Constrained Optimal Control Problems

  • Martin Neuenhofen
    • Categories Nonlinear Optimization

    In the arXiv paper [arXiv:1712.07761] from December 2017 we presented a convergent direct transcription method for optimal control problems. In the present paper we present a significantly generalized convergence theory in succinct form. Therein, we replace strong assumptions that we had formerly made on local uniqueness of the solution, and on differentiability of a particular … Read more

    Minimizing convex quadratics with variable precision Krylov methods

  • Serge Gratton
  • Philippe L. Toint
  • Ehouarn Simon
    • Categories Convex Optimization, Optimization Software and Modeling Systems, Quadratic Programming Tags , ,

    Iterative algorithms for the solution of convex quadratic optimization problems are investigated, which exploit inaccurate matrix-vector products. Theoretical bounds on the performance of a Conjugate Gradients and a Full-Orthormalization methods are derived, the necessary quantities occurring in the theoretical bounds estimated and new practical algorithms derived. Numerical experiments suggest that the new methods have significant … Read more

    A hybrid algorithm for the two-trust-region subproblem

  • Saeid Ansary Karbasy
  • Maziar Salahi
    • Categories Global Optimization, Quadratic Programming Tags , , ,

    Two-trust-region subproblem (TTRS), which is the minimization of a general quadratic function over the intersection of two full-dimensional ellipsoids, has been the subject of several recent research. In this paper, to solve TTRS, a hybrid of efficient algorithms for finding global and local-nonglobal minimizers of trust-region subproblem and the alternating direction method of multipliers (ADMM) … Read more

    Convex computation of extremal invariant measures of nonlinear dynamical systems and Markov processes

  • Didier Henrion
  • Milan Korda
  • Igor Mezic
    • Categories Semi-definite Programming, Systems governed by Differential Equations Optimization Tags , , ,

    We propose a convex-optimization-based framework for computation of invariant measures of polynomial dynamical systems and Markov processes, in discrete and con- tinuous time. The set of all invariant measures is characterized as the feasible set of an infinite-dimensional linear program (LP). The objective functional of this LP is then used to single-out a specific measure … Read more

    Semidenite Approximations of Invariant Measures for Polynomial Systems

  • Marcelo Forets
  • Didier Henrion
  • Victor Magron
    • Categories Semi-definite Programming, Systems governed by Differential Equations Optimization Tags , , , ,

    We consider the problem of approximating numerically the moments and the supports of measures which are invariant with respect to the dynamics of continuousand discrete-time polynomial systems, under semialgebraic set constraints. First, we address the problem of approximating the density and hence the support of an invariant measure which is absolutely continuous with respect to … Read more

    A conjugate gradient-based algorithm for large-scale quadratic programming problem with one quadratic constraint

  • Maziar Salahi
  • Akram Taati
    • Categories Global Optimization, Quadratic Programming Tags , ,

    In this paper, we consider the nonconvex quadratically constrained quadratic programming (QCQP) with one quadratic constraint. By employing the conjugate gradient method, an efficient algorithm is proposed to solve QCQP that exploits the sparsity of the involved matrices and solves the problem via solving a sequence of positive definite system of linear equations after identifying … Read more

    Design, Implementation and Simulation of an MPC algorithm for Switched Nonlinear Systems under Combinatorial Constraints

  • Angelika Altmann-Dieses
  • Moritz Diehl
  • Sebastian Sager
  • Adrian Bürger
  • Clemens Zeile
    • Categories (Mixed) Integer Nonlinear Programming, Systems governed by Differential Equations Optimization Tags , , , ,

    Within this work, we present a warm-started algorithm for Model Predictive Control (MPC) of switched nonlinear systems under combinatorial constraints based on Combinatorial Integral Approximation (CIA). To facilitate high-speed solutions, we introduce a preprocessing step for complexity reduction of CIA problems, and include this approach within a new toolbox for solution of CIA problems with … Read more

    Finite convergence and weak sharpness for solutions of nonsmooth variational inequalities in Hilbert spaces

  • Qamrul Hasan Ansari
  • Luong Nguyen
  • Xiaolong Qin
    • Categories Convex and Nonsmooth Optimization, Nonlinear Optimization Tags , , , , ,

    This paper deals with the study of weak sharp solutions for nonsmooth variational inequalities and finite convergence property of the proximal point method. We present several characterizations for weak sharpness of the solutions set of nonsmooth variational inequalities without using the gap functions. We show that under weak sharpness of the solutions set, the sequence … Read more

    A stochastic Levenberg-Marquardt method using random models with complexity results and application to data assimilation

  • Elhoucine Bergou
  • Youssef Diouane
  • Vyacheslav Kungurtsev
  • Clément W. Royer
    • Categories Nonlinear Systems and Least-Squares, Unconstrained Optimization Tags , , , , ,

    Globally convergent variants of the Gauss-Newton algorithm are often the methods of choice to tackle nonlinear least-squares problems. Among such frameworks, Levenberg-Marquardt and trust-region methods are two well-established, similar paradigms. Both schemes have been studied when the Gauss-Newton model is replaced by a random model that is only accurate with a given probability. Trust-region schemes … Read more