Nonlinear Optimization

Constrained Derivative-Free Optimization on Thin Domains

  • José Mario Martínez
  • Francisco N. C. Sobral
    • Categories Constrained Nonlinear Optimization, Global Optimization Applications, Nonlinear Optimization Tags , , , ,

    Many derivative-free methods for constrained problems are not efficient for minimizing functions on “thin” domains. Other algorithms, like those based on Augmented Lagrangians, deal with thin constraints using penalty-like strategies. When the constraints are computationally inexpensive but highly nonlinear, these methods spend many potentially expensive objective function evaluations motivated by the difficulties of improving feasibility. … Read more

    On smooth relaxations of obstacle sets

  • Oliver Stein
  • Paul Steuermann
    • Categories Constrained Nonlinear Optimization, Convex Optimization Tags , , , , ,

    We present and discuss a method to relax sets described by finitely many smooth convex inequality constraints by the level set of a single smooth convex inequality constraint. Based on error bounds and Lipschitz continuity, special attention is paid to the maximal approximation error and a guaranteed safety margin. Our results allow to safely avoid … Read more

    Proximal point method on Finslerian manifolds and the “Effort Accuracy Trade off”

  • J. X. da Cruz Neto
  • Paulo Roberto Oliveira
  • Antoine Soubeyran
  • Pedro A. Soares Jr.
    • Categories Applications - Science and Engineering, Global Optimization Applications, Nonlinear Optimization

    In this paper we consider minimization problems with constraints. We will show that if the set of constraints is a Finslerian manifold of non positive flag curvature, and the objective function is di fferentiable and satisfi es the property Kurdyka-Lojasiewicz, then the proximal point method is naturally extended to solve that class of problems. We will prove … Read more

    A conjugate directions approach to improve the limited-memory BFGS method

  • Ladislav Lukšan
  • Jan Vlček
    • Categories Nonlinear Optimization, Unconstrained Optimization Tags , , , , ,

    Simple modifiations of the limited-memory BFGS method (L-BFGS) for large scale unconstrained optimization are considered, which consist in corrections (derived from the idea of conjugate directions) of the used difference vectors, utilizing information from the preceding iteration. In case of quadratic objective functions, the improvement of convergence is the best one in some sense and … 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

    A quadratically convergent Newton method for vector optimization

  • L. M. Graña Drummond
  • Benar Fux Svaiter
  • Fernanda M. P. Raupp
    • Categories Convex Optimization, Multi-Criteria Optimization, Nonlinear Optimization Tags , , ,

    We propose a Newton method for solving smooth unconstrained vector optimization problems under partial orders induced by general closed convex pointed cones. The method extends the one proposed by Fliege, Grana Drummond and Svaiter for multicriteria, which in turn is an extension of the classical Newton method for scalar optimization. The steplength is chosen by … Read more

    Convergence of the restricted Nelder-Mead algorithm in two dimensions

  • Jeffrey Lagarias
  • Margaret H. Wright
  • Bjorn Poonen
    • Categories Unconstrained Optimization

    The Nelder-Mead algorithm, a longstanding direct search method for unconstrained optimization published in 1965, is designed to minimize a scalar-valued function $f$ of $n$ real variables using only function values, without any derivative information. Each Nelder–Mead iteration is associated with a nondegenerate simplex defined by $n + 1$ vertices and their function values; a typical … Read more

    Two new weak constraint qualifications and applications

  • Roberto Andreani
  • Gabriel Haeser
  • Paulo J. S. Silva
  • María Laura Schuverdt
    • Categories Constrained Nonlinear Optimization Tags , , ,

    We present two new constraint qualifications (CQ) that are weaker than the recently introduced Relaxed Constant Positive Linear Depen- dence (RCPLD) constraint qualification. RCPLD is based on the assump- tion that many subsets of the gradients of the active constraints preserve positive linear dependence locally. A major open question was to identify the exact set … Read more

    A Note About The Complexity Of Minimizing Nesterov’s Smooth Chebyshev-Rosenbrock Function

  • Coralia Cartis
  • Nicholas I. M. Gould
  • Philippe L. Toint
    • Categories Unconstrained Optimization Tags , ,

    This short note considers and resolves the apparent contradiction between known worst-case complexity results for first and second-order methods for solving unconstrained smooth nonconvex optimization problems and a recent note by Jarre (2011) implying a very large lower bound on the number of iterations required to reach the solution’s neighbourhood for a specific problem with … Read more

    Representing quadratically constrained quadratic programs as generalized copositive programs

  • Samuel Burer
  • Hongbo Dong
    • Categories Global Optimization Theory, Linear, Cone and Semidefinite Programming, Quadratic Programming Tags , ,

    We show that any nonconvex quadratically constrained quadratic program(QCQP) can be represented as a generalized copositive program. In fact,we provide two representations. The first is based on the concept of completely positive (CP) matrices over second order cones, while the second is based on CP matrices over the positive semidefinte cone. Our analysis assumes that … Read more