Nonlinear Systems and Least-Squares

A homotopy for the reliable estimation of model parameters in chromatography processes

  • Dominik H. Cebulla
  • Christian Kirches
  • Andreas Potschka
    • Categories Chemical Engineering, Nonlinear Systems and Least-Squares, Optimization of Systems modeled by PDEs Tags , , ,

    Mathematical modeling, simulation, and optimization can significantly support the development and characterization of chromatography steps in the biopharmaceutical industry. Particularly mechanistic models become preferably used, as these models, once carefully calibrated, can be employed for a reliable optimization. However, model calibration is a difficult task in this context due to high correlations between parameters, highly … Read more

    Revisiting the fitting of the Nelson-Siegel and Svensson models

  • Dirk Banholzer
  • Jörg Fliege
  • Ralf Werner
    • Categories Finance and Economics, Global Optimization Applications, Nonlinear Systems and Least-Squares Tags , , , , ,

    The Nelson-Siegel and the Svensson models are two of the most widely used models for the term structure of interest rates. Even though the models are quite simple and intuitive, fitting them to market data is numerically challenging and various difficulties have been reported. In this paper, a novel mathematical analysis of the fitting problem … Read more

    Inexact Newton methods with matrix approximation by sampling for nonlinear least-squares and systems

  • Stefania Bellavia
  • Greta Malaspina
  • Benedetta Morini
    • Categories Nonlinear Optimization, Nonlinear Systems and Least-Squares, Stochastic Programming

    We develop and analyze stochastic inexact Gauss-Newton methods for nonlinear least-squares problems and inexact Newton methods for nonlinear systems of equations. Random models are formed using suitable sampling strategies for the matrices involved in the deterministic models. The analysis of the expected number of iterations needed in the worst case to achieve a desired level … Read more

    A subspace inertial method for derivative-free nonlinear monotone equations

  • Morteza Kimiaei
  • Abdulkarim Hassan Ibrahim
  • Susan Ghaderi
    • Categories Nonlinear Systems and Least-Squares Tags

    We introduce SILSA, a subspace inertial line search algorithm, for finding solutions of nonlinear monotone equations (NME). At each iteration, a new point is generated in a subspace generated by the previous points. Of all finite points forming the subspace, a point with the largest residual norm is replaced by the new point to update … Read more

    Multilevel Objective-Function-Free Optimization with an Application to Neural Networks Training

  • Serge Gratton
  • Alena Kopanicakova
  • Philippe L. Toint
    • Categories Data Science Algorithms, Nonlinear Optimization, Nonlinear Systems and Least-Squares Tags , , ,

    A class of multi-level algorithms for unconstrained nonlinear optimization is presented which does not require the evaluation of the objective function. The class contains the momentum-less AdaGrad method as a particular (single-level) instance. The choice of avoiding the evaluation of the objective function is intended to make the algorithms of the class less sensitive to … Read more

    Splitted Levenberg-Marquardt Method for Large-Scale Sparse Problems

  • Greta Malaspina
  • Nataša Krejić
    • Categories Nonlinear Optimization, Nonlinear Systems and Least-Squares Tags , , ,

    We consider large-scale nonlinear least squares problems with sparse residuals, each of them depending on a small number of variables. A decoupling procedure which results in a splitting of the original problems into a sequence of independent problems of smaller sizes is proposed and analysed. The smaller size problems are modified in a way that … Read more

    A Levenberg-Marquardt Method for Nonsmooth Regularized Least Squares

  • Dominique Orban
  • Aleksandr Y. Aravkin
  • Robert Baraldi
    • Categories Data Science Algorithms, Nonlinear Systems and Least-Squares, Nonsmooth Optimization Tags , , , , ,

    We develop a Levenberg-Marquardt method for minimizing the sum of a smooth nonlinear least-squares term \(f(x) = \frac{1}{2} \|F(x)\|_2^2\) and a nonsmooth term \(h\). Both \(f\) and \(h\) may be nonconvex. Steps are computed by minimizing the sum of a regularized linear least-squares model and a model of \(h\) using a first-order method such as … Read more

    A Voronoi-Based Mixed-Integer Gauss-Newton Algorithm for MINLP Arising in Optimal Control

  • Andrea Ghezzi
  • LeoS
  • Adrian Bürger
  • Clemens Zeile
  • Sebastian Sager
  • Moritz Diehl
    • Categories (Mixed) Integer Nonlinear Programming, Control Applications, Nonlinear Systems and Least-Squares Tags ,

    We present a new algorithm for addressing nonconvex Mixed-Integer Nonlinear Programs (MINLPs) where the cost function is of nonlinear least squares form. We exploit this structure by leveraging a Gauss-Newton quadratic approximation of the original MINLP, leading to the formulation of a Mixed-Integer Quadratic Program (MIQP), which can be solved efficiently. The integer solution of the … Read more

    On the convergence of iterative schemes for solving a piecewise linear system of equations

  • Nicolas F. Armijo
  • Yunier Bello-Cruz
  • Gabriel Haeser
    • Categories Nonlinear Systems and Least-Squares Tags , ,

    This paper is devoted to studying the global and finite convergence of the semi-smooth Newton method for solving a piecewise linear system that arises in cone-constrained quadratic programming problems and absolute value equations. We first provide a negative answer via a counterexample to a conjecture on the global and finite convergence of the Newton iteration … Read more

    Convergence properties of an Objective-Function-Free Optimization regularization algorithm, including an $\mathcal{O}(\epsilon^{-3/2})$ complexity bound

  • Serge Gratton
  • Sadok Jerad
  • Philippe L. Toint
    • Categories Nonlinear Systems and Least-Squares, Unconstrained Optimization Tags , , ,

    An adaptive regularization algorithm for unconstrained nonconvex optimization is presented in which the objective function is never evaluated, but only derivatives are used. This algorithm belongs to the class of adaptive regularization methods, for which optimal worst-case complexity results are known for the standard framework where the objective function is evaluated. It is shown in … Read more