Bound-constrained Optimization

A matrix-free approach to build band preconditioners for large-scale bound-constrained optimization

  • Valentina De Simone
  • Daniela di Serafino
    • Categories Bound-constrained Optimization, Nonlinear Optimization

    We propose a procedure for building symmetric positive definite band preconditioners for large-scale symmetric, possibly indefinite, linear systems, when the coefficient matrix is not explicitly available, but matrix-vector products involving it can be computed. We focus on linear systems arising in Newton-type iterations within matrix-free versions of projected methods for bound-constrained nonlinear optimization. In this … Read more

    On the evaluation complexity of constrained nonlinear least-squares and general constrained nonlinear optimization using second-order methods

  • Coralia Cartis
  • Nicholas I. M. Gould
  • Philippe L. Toint
    • Categories Bound-constrained Optimization, Constrained Nonlinear Optimization, Nonlinear Systems and Least-Squares Tags , ,

    When solving the general smooth nonlinear optimization problem involving equality and/or inequality constraints, an approximate first-order critical point of accuracy $\epsilon$ can be obtained by a second-order method using cubic regularization in at most $O(\epsilon^{-3/2})$ problem-functions evaluations, the same order bound as in the unconstrained case. This result is obtained by first showing that the … Read more

    Subspace accelerated matrix splitting algorithms for bound-constrained quadratic programming and linear complementarity problems

  • Liming Feng
  • Jorge Nocedal
  • Daniel P. Robinson
  • Jong-Shi Pang
    • Categories Bound-constrained Optimization, Finance and Economics Tags , , , , , , ,

    This paper studies the solution of two problems—bound-constrained quadratic programs and linear complementarity problems—by two-phase methods that consist of an active set prediction phase and a subspace phase. The algorithms enjoy favorable convergence properties under weaker assumptions than those assumed for other methods in the literature. The active set prediction phase employs matrix splitting iterations … Read more

    An algorithm for the choice of the regularization parameter in inverse problems in imaging

  • Elena Loli Piccolomini
  • Fabiana Zama
    • Categories Applications - Science and Engineering, Bound-constrained Optimization

    In this paper we present an iterative algorithm for the solution of regularization problems arising in inverse image processing. The regularization function to be minimized is constituted by two terms, a data fit function and a regularization function, weighted by a regularization parameter. The proposed algorithm solves the minimization problem and estimates the regularization parameter … Read more

    On the convergence of an inexact Gauss-Newton trust-region method for nonlinear least-squares problems with simple bounds

  • Margherita Porcelli
    • Categories Bound-constrained Optimization, Nonlinear Optimization, Nonlinear Systems and Least-Squares Tags , , , ,

    We introduce an inexact Gauss-Newton trust-region method for solving bound-constrained nonlinear least-squares problems where, at each iteration, a trust-region subproblem is approximately solved by the Conjugate Gradient method. Provided a suitable control on the accuracy to which we attempt to solve the subproblems, we prove that the method has global and asymptotic fast convergence properties. … Read more

    An Iterative algorithm for large size Least-Squares constrained regularization problems.

  • Elena Loli Piccolomini
  • Fabiana Zama
    • Categories Applications - Science and Engineering, Bound-constrained Optimization Tags , ,

    In this paper we propose an iterative algorithm to solve large size linear inverse ill posed problems. The regularization problem is formulated as a constrained optimization problem. The dual lagrangian problem is iteratively solved to compute an approximate solution. Before starting the iterations, the algorithm computes the necessary smoothing parameters and the error tolerances from … Read more

    A quasi-Newton projection method for nonnegatively constrained image deblurring

  • Germana Landi
  • Elena Loli Piccolomini
    • Categories Basic Sciences Applications, Bound-constrained Optimization Tags , , , ,

    In this paper we present a quasi-Newton projection method for image deblurring. The mathematical problem is a constrained minimization problem, where the objective function is a regularization function and the constraint is the positivity of the solution. The regularization function is a sum of the Kullback-Leibler divergence, used to minimize the error in the presence … Read more

    DERIVATIVE-FREE METHODS FOR BOUND CONSTRAINED MIXED-INTEGER OPTIMIZATION

  • Giampaolo Liuzzi
  • Stefano Lucidi
  • Francesco Rinaldi
    • Categories (Mixed) Integer Nonlinear Programming, Bound-constrained Optimization Tags , ,

    We consider the problem of minimizing a continuously differentiable function of several variables subject to simple bound constraints where some of the variables are restricted to take integer values. We assume that the first order derivatives of the objective function can be neither calculated nor approximated explicitly. This class of mixed integer nonlinear optimization problems … Read more

    A Non-monotonic Method for Large-scale Nonnegative Least Squares

  • Inderjit S. Dhillon
  • Dongmin Kim
  • Suvrit Sra
    • Categories Basic Sciences Applications, Bound-constrained Optimization, Nonlinear Systems and Least-Squares Tags , , , , , ,

    We present a new algorithm for nonnegative least-squares (NNLS). Our algorithm extends the unconstrained quadratic optimization algorithm of Barzilai and Borwein (BB) (J. Barzilai and J. M. Borwein; Two-Point Step Size Gradient Methods. IMA J. Numerical Analysis; 1988.) to handle nonnegativity constraints. Our extension diff ers in several basic aspects from other constrained BB variants. The … Read more

    The BOBYQA algorithm for bound constrained optimization without derivatives

  • M.J.D. Powell
    • Categories Bound-constrained Optimization

    BOBYQA is an iterative algorithm for finding the minimum of a function F(x) subject to lower and upper bounds on the variables, F(x) being specified by a “black box” that returns the value F(x) for any feasible x. Each iteration employs a quadratic approximation Q to F that satisfies Q(y_j) = F(y_j), j=1,2,…,m, the interpolation … Read more