Benedetta Morini

Inexact restoration with subsampled trust-region methods for finite-sum minimization

  • Stefania Bellavia
  • Nataša Krejić
  • Benedetta Morini
    • Categories Unconstrained Optimization Tags , , , ,

    Convex and nonconvex finite-sum minimization arises in many scientific computing and machine learning applications. Recently, first-order and second-order methods where objective functions, gradients and Hessians are approximated by randomly sampling components of the sum have received great attention. We propose a new trust-region method which employs suitable approximations of the objective function, gradient and Hessian … Read more

    Adaptive regularization algorithms with inexact evaluations for nonconvex optimization

  • Stefania Bellavia
  • Benedetta Morini
  • Philippe L. Toint
  • Gianmarco Gurioli
    • Categories Constrained Nonlinear Optimization, Data-Mining, Unconstrained Optimization Tags , , , ,

    A regularization algorithm using inexact function values and inexact derivatives is proposed and its evaluation complexity analyzed. This algorithm is applicable to unconstrained problems and to problems with inexpensive constraints (that is constraints whose evaluation and enforcement has negligible cost) under the assumption that the derivative of highest degree is beta-H\”{o}lder continuous. It features a … Read more

    Adaptive Cubic Regularization methods with dynamic inexact Hessian information and applications to finite-sum minimization

  • Stefania Bellavia
  • Benedetta Morini
  • Gianmarco Gurioli
    • Categories Unconstrained Optimization Tags

    We consider the Adaptive Regularization with Cubics approach for solving nonconvex optimization problems and propose a new variant based on inexact Hessian information chosen dynamically. The theoretical analysis of the proposed procedure is given. The key property of ARC framework, constituted by optimal worst-case function/derivative evaluation bounds for first- and second-order critical point, is guaranteed. … Read more

    Stability and accuracy of Inexact Interior Point methods for convex quadratic programming

  • Benedetta Morini
  • Valeria Simoncini
    • Categories Nonlinear Optimization Tags , ,

    We consider primal-dual IP methods where the linear system arising at each iteration is formulated in the reduced (augmented) form and solved approximately. Focusing on the iterates close to a solution, we analyze the accuracy of the so-called inexact step, i.e., the step that solves the unreduced system, when combining the effects of both different … Read more

    Quasi-Newton methods for constrained nonlinear systems: complexity analysis and application

  • Leopoldo Marini
  • Benedetta Morini
  • Margherita Porcelli
    • Categories Nonlinear Optimization, Nonlinear Systems and Least-Squares

    We address the solution of convex constrained nonlinear systems by new linesearch Quasi-Newton methods. These methods are based on a proper use of the projection map onto the constraint set and on a derivative-free and nonmonotone linesearch strategy. The convergence properties of the proposed methods are presented along with a worst-case iteration complexity bound. Several … Read more

    Approximate norm descent methods for constrained nonlinear systems

  • Benedetta Morini
  • Margherita Porcelli
  • Philippe L. Toint
    • Categories Bound-constrained Optimization, Nonlinear Systems and Least-Squares Tags , , ,

    We address the solution of convex-constrained nonlinear systems of equations where the Jacobian matrix is unavailable or its computation/storage is burdensome. In order to efficiently solve such problems, we propose a new class of algorithms which are “derivative-free” both in the computation of the search direction and in the selection of the steplength. Search directions … Read more

    On an adaptive regularization for ill-posed nonlinear systems and its trust-region implementation

  • Stefania Bellavia
  • Benedetta Morini
  • Elisa Riccietti
    • Categories Nonlinear Optimization Tags , , ,

    In this paper we address the stable numerical solution of nonlinear ill-posed systems by a trust-region method. We show that an appropriate choice of the trust-region radius gives rise to a procedure that has the potential to approach a solution of the unperturbed system. This regularizing property is shown theoretically and validated numerically. CitationDipartimento di … Read more

    A comparison of reduced and unreduced KKT systems arising from Interior Point methods

  • Benedetta Morini
  • Valeria Simoncini
  • Mattia Tani
    • Categories Quadratic Programming Tags , , ,

    We address the iterative solution of symmetric KKT systems arising in the solution of convex quadratic programming problems. Two strictly related and well established formulations for such systems are studied with particular emphasis on the effect of preconditioning strategies on their relation. Constraint and augmented preconditioners are considered, and the choice of the augmentation Matrix … Read more

    Spectral estimates for unreduced symmetric KKT systems arising from Interior Point methods

  • Benedetta Morini
  • Valeria Simoncini
  • Mattia Tani
    • Categories Quadratic Programming Tags , , , ,

    We consider symmetrized KKT systems arising in the solution of convex quadratic programming problems in standard form by Interior Point methods. Their coefficient matrices usually have 3×3 block structure and, under suitable conditions on both the quadratic programming problem and the solution, they are nonsingular in the limit. We present new spectral estimates for these … Read more

    On the update of constraint preconditioners for regularized KKT systems

  • Stefania Bellavia
  • Valentina De Simone
  • Daniela di Serafino
  • Benedetta Morini
    • Categories Nonlinear Optimization, Quadratic Programming Tags , , ,

    We address the problem of preconditioning sequences of regularized KKT systems, such as those arising in Interior Point methods for convex quadratic programming. In this case, Constraint Preconditioners (CPs) are very effective and widely used; however, when solving large-scale problems, the computational cost for their factorization may be high, and techniques for approximating them appear … Read more