Resumo em Inglês:

A trust-region method with two subproblems and backtracking line search for solving unconstrained optimization is proposed. At every iteration, we use the truncated conjugate gradient method or its variation to solve one of the two subproblems approximately. Backtracking line search is carried out when the trust-region trail step fails. We show that this method have the same convergence properties as the traditional trust-region method based on the truncated conjugate gradient method. Numerical results show that this method is as reliable as the traditional one and more efficient in respect of iterations, CPU time and evaluations. Mathematical subject classification: Primary: 65K05; Secondary: 90C30.

Resumo em Inglês:

This paper addresses the single machine scheduling problem with a common due date aiming to minimize earliness and tardiness penalties. Due to its complexity, most of the previous studies in the literature deal with this problem using heuristics and metaheuristics approaches. With the intention of contributing to the study of this problem, a branch-and-bound algorithm is proposed. Lower bounds and pruning rules that exploit properties of the problem are introduced. The proposed approach is examined through a computational comparative study with 280 problems involving different due date scenarios. In addition, the values of optimal solutions for small problems from a known benchmark are provided. Mathematical subject classification: 90C11, 62P30, 90B35.

Resumo em Inglês:

Carathéodory's lemma states that if we have a linear combination of vectors in <img border=0 src="../../../../img/revistas/cam/v29n2/r_bastao.gif" align=absmiddle>n, we can rewrite this combination using a linearly independent subset. This lemma has been successfully applied in nonlinear optimization in many contexts. In this work we present a new version of this celebrated result, in which we obtained new bounds for the size of the coefficients in the linear combination and we provide examples where these bounds are useful. We show how these new bounds can be used to prove that the internal penalty method converges to KKT points, and we prove that the hypothesis to obtain this result cannot be weakened.The new bounds also provides us some new results of convergence for the quasi feasible interior point ℓ2-penalty method of Chen and Goldfarb [7]. Mathematical subject classification: 90C30, 49K99, 65K05.

Resumo em Inglês:

Let C be a n×n symmetric matrix. For each integer 1 < k < n we consider the minimization problem m(ε): = minX{ Tr{CX} + εƒ(X)}. Here the variable X is an n×n symmetric matrix, whose eigenvalues satisfy <img border=0 src="../../../../img/revistas/cam/v29n2/a04img01.gif"> the number ε is a positive (perturbation) parameter and ƒ is a Lipchitz-continuous function (in general nonlinear). It is well known that when ε = 0 the minimum value, m(0), is the sum of the smallest k eigenvalues of C. Assuming that the eigenvalues of C satisfy λ1(C) < ... < λk(C) < λk+1(C) < ∙∙∙ < λn(C), we establish the following upper and lower bounds for the minimum value m(ε): <img border=0 src="../../../../img/revistas/cam/v29n2/a04img02.gif"> where <img border=0 src="../../../../img/revistas/cam/v29n2/f4_barra.gif" align=absmiddle>is the minimum value of ƒ over the solution set of unperturbed problem and L is the Lipschitz-constant of ƒ. The above inequality shows that the error by replacing the upper bound (or the lower bound) by the exact value is at least quadratic in the perturbation parameter. We also treat the case that λk+1(C) = λk(C). We compare the exact solution with the upper and lower bounds for some examples. Mathematical subject classification: 15A42, 15A18, 90C22.

Resumo em Inglês:

Some theories and a method are discussed on updating a generalized centrosymmetric model. It gives a generalized centrosymmetric modified solution with partial prescribed least square spectra constraints. The emphasis is given on exploiting structure-preserving algorithm based on matrix approximation theory. A perturbation theory for the modified solution is established. The convergence of an iterative solution is investigated. Illustrative examples are provided. Mathematical subject classification: 15A29, 15A90, 41A29, 65F15, 65L70.

Resumo em Inglês:

In this paper, we propose a regularized factorized quasi-Newton method with a new Armijo-type line search and prove its global convergence for nonlinear least squares problems. This convergence result is extended to the regularized BFGS and DFP methods for solving strictly convex minimization problems. Some numerical results are presented to show efficiency of the proposed method. Mathematical subject classification: 90C53, 65K05.

Resumo em Inglês:

This work concerns the practical computation of the volumetric modulus, also called normalized volume, of a convex cone in a Euclidean space of dimension beyond three. Deterministic and stochastic techniques are considered. Mathematical subject classification: Primary: 28A75; Secondary: 52A20.

Resumo em Inglês:

We describe finite sets of points, called sentinels, which allow us to decide if isometric copies of polygons, convex or not, intersect. As an example of the applicability of the concept of sentinel, we explain how they can be used to formulate an algorithm based on the optimization of differentiable models to pack polygons in convex sets. Mathematical subject classification: 90C53, 65K05.

Resumo em Inglês:

In this paper, we study the distribution on the eigenvalues of the preconditioned matrices that arise in solving two-by-two block non-Hermitian positive semidefinite linear systems by use of the accelerated Hermitian and skew-Hermitian splitting iteration methods. According to theoretical analysis, we prove that all eigenvalues of the preconditioned matrices are very clustered with any positive iteration parameters α and β; especially, when the iteration parameters α and β approximate to 1, all eigenvalues approach 1. We also prove that the real parts of all eigenvalues of the preconditioned matrices are positive, i.e., the preconditioned matrix is positive stable. Numerical experiments show the correctness and feasibility of the theoretical analysis. Mathematical subject classification: 65F10, 65N22, 65F50.

Resumo em Inglês:

This paper proposes a filter method for solving nonlinear semidefinite programming problems. Our method extends to this setting the filter SQP (sequential quadratic programming) algorithm, recently introduced for solving nonlinear programming problems, obtaining the respective global convergence results. Mathematical subject classification: 90C30, 90C55.