Bound-constrained Optimization

Metal Artefact Reduction by Least-Squares Penalized-Likelihood Reconstruction with a Fast Polychromatic Projection Model

  • Jean-Pierre Dussault
  • Yves Goussard
  • Benoit Hamelin
    • Categories Biomedical Applications, Bound-constrained Optimization Tags , , , , ,

    We consider penalized-likelihood reconstruction for X-ray computed tomography of objects that contain small metal structures. To reduce the beam hardening artefacts induced by these structures, we derive the reconstruction algorithm from a projection model that takes into account the photon emission spectrum and nonlinear variation of attenuation to photon energy. This algorithm requires excessively long … Read more

    Stopping rules and backward error analysis for bound-constrained optimization

  • Serge Gratton
  • Philippe L. Toint
  • Mélodie Mouffe
    • Categories Bound-constrained Optimization, Optimization Software Design Principles Tags , , , ,

    Termination criteria for the iterative solution of bound-constrained optimization problems are examined in the light of backward error analysis. It is shown that the problem of determining a suitable perturbation on the problem’s data corresponding to the definition of the backward error is analytically solvable under mild assumptions. Moreover, a link between existing termination criteria … Read more

    TRESNEI, a Matlab trust-region solver for systems of nonlinear equalities and inequalities

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

    The Matlab implementation of a trust-region Gauss-Newton method for bound-constrained nonlinear least-squares problems is presented. The solver, called TRESNEI, is adequate for zero and small-residual problems and handles the solution of nonlinear systems of equalities and inequalities. The structure and the usage of the solver are described and an extensive numerical comparison with functions from … Read more

    Solving the Sensor Network Localization Problem using an Heuristic Multistage Approach

  • Andrea Cassioli
    • Categories Bound-constrained Optimization, Telecommunications

    The Sensor Network Localization Problem (SNLP), arising from many applied fields related with environmental monitoring, has attracted much research during the last years. Solving the SNLP deals with the reconstruction of a geometrical structure from incomplete pairwise distances between sensors. In this paper we present an heuristic multistage approach in which the solving strategy is … Read more

    An adaptive cubic regularisation algorithm for nonconvex optimization with convex constraints and its function-evaluation complexity

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

    The adaptive cubic overestimation algorithm described in Cartis, Gould and Toint (2007) is adapted to the problem of minimizing a nonlinear, possibly nonconvex, smooth objective function over a convex domain. Convergence to first-order critical points is shown under standard assumptions, but without any Lipschitz continuity requirement on the objective’s Hessian. A worst-case complexity analysis in … Read more

    Nonlinear Stepsize Control, Trust Regions and Regularizations for Unconstrained Optimization

  • Philippe L. Toint
    • Categories Bound-constrained Optimization, Nonlinear Systems and Least-Squares, Unconstrained Optimization Tags , ,

    A general class of algorithms for unconstrained optimization is introduced, which subsumes the classical trust-region algorithm and two of its newer variants, as well as the cubic and quadratic regularization methods. A unified theory of global convergence to first-order critical points is then described for this class. An extension to projection-based trust-region algorithms for nonlinear … Read more

    A Subspace Limited Memory BFGS Algorithm For Box Constrained Optimization

  • Yuan Gonglin
  • Wu Yanlin
  • Wei Zengxin
    • Categories Bound-constrained Optimization

    In this paper, a subspace limited BFGS algorithm is proposed for bound constrained optimization. The global convergence will be established under some suitable conditions. Numerical results show that this method is more competitive than the normal method does. Article Download View A Subspace Limited Memory BFGS Algorithm For Box Constrained Optimization

    Numerical Experience with a Recursive Trust-Region Method for Multilevel Nonlinear Optimization

  • Serge Gratton
  • Annick Sartenaer
  • Philippe L. Toint
  • Mélodie Mouffe
  • Dimitri Tomanos
    • Categories Bound-constrained Optimization, Distributed Control, Optimization of Systems modeled by PDEs Tags , , , , ,

    We consider an implementation of the recursive multilevel trust-region algorithm proposed by Gratton, Mouffe, Toint, Weber (2008) for bound-constrained nonlinear problems, and provide numerical experience on multilevel test problems. A suitable choice of the algorithm’s parameters is identified on these problems, yielding a satisfactory compromise between reliability and efficiency. The resulting default algorithm is then … Read more

    Constraint propagation on quadratic constraints

  • Ferenc Domes
  • Arnold Neumaier
    • Categories Bound-constrained Optimization, Quadratic Programming Tags , , , , , , , ,

    This paper considers constraint propagation methods for continuous constraint satisfaction problems consisting of linear and quadratic constraints. All methods can be applied after suitable preprocessing to arbitrary algebraic constraints. The basic new techniques consist in eliminating bilinear entries from a quadratic constraint, and solving the resulting separable quadratic constraints by means of a sequence of … Read more

    Pricing with uncertain customer valuations

  • Aurelie Thiele
    • Categories Bound-constrained Optimization, Robust Optimization, Yield Management Tags , ,

    Uncertain demand in pricing problems is often modeled using the sum of a linear price-response function and a zero-mean random variable. In this paper, we argue that the presence of uncertainty motivates the introduction of nonlinearities in the demand as a function of price, both in the risk-neutral and risk-sensitive models. We motivate our analysis … Read more