Search

This paper introduces a hybrid algorithm of Nelder–Mead and scatter search algorithms called SSNM. Numerical results for some known test problems with varying dimensions from 2 to 100 variables prove its great performance for a bounded or constrained problem. This paper also has a considerable contribution to the joint economic lot sizing (JELS) problem literature. This problem is considered for a two-stage supply chain with price-sensitive demand for geometric, geometric-then-equal size and optimal shipment policies. Solution procedures are also developed which use the SSNM algorithm in their steps. These models and solution procedures are novel in the JELS literature. [ABSTRACT FROM AUTHOR]

This paper presents an accurate implicit re-initialization approach in the framework of a level set method. The improved method includes two schemes for keeping the zero level set unperturbed. The first scheme is to derive and use a new formula for the smoothing parameter in the conventional re-initialization equation based on the principle of the interface not moving. The second scheme is to reduce the local time step to avoid the interface moving across grid points when the sign of the level set function near the interface is changed. The example presented suggests that the new algorithm has a better approximation to the signed distance function and obtains a more accurate interface than the algorithm presented by Sussman et al. (1994). For shape and topology optimization of fluid, the effectiveness of the developed method in this paper was demonstrated by the internal and external flow examples. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents two different versions of an optimal control method for enforcing mass conservation in level set algorithms. The proposed PDE-constrained optimization procedure corrects a numerical solution to the level set transport equation so as to satisfy a conservation law for the corresponding Heaviside function. In the original version of this method, conservation errors are corrected by adding the gradient of a scalar control variable to the convective flux in the state equation. In the present paper, we investigate the use of vector controls. The alternative formulation offers additional flexibility and requires less regularity than the original method. The nonlinear system of first-order optimality conditions is solved using a standard fixed-point iteration. The new methodology is evaluated numerically and compared to the scalar control approach. [Copyright &y& Elsevier]

Optical tomography is an imaging modality that explores the distribution of optical parameters in tissues. In this paper, the regularization jointing both T V and L 1 norm is studied for absorption parameter identification based on radiative transport equation. The T V + L 1 framework is introduced containing L 2 data fidelity, T V regularizer and L 1 regularizer. We demonstrate the existence, stability and convergence of the minima with respect to this T V + L 1 regularization. A novel algorithm for solving related optimization problem is proposed based on reweighted method and technique of split-Bregman. Simulations are performed to show that the proposed reweighted T V + L 1 regularization is more capable of preserving geometric structure of inclusions, quantifying values of absorption parameter and promoting fast convergence compared with T V or L 1 regularization, and is potential for breast cancer imaging. Moreover, it is robust to noise. [ABSTRACT FROM AUTHOR]

In this paper, an extended nonmonotone line search is proposed to improve the efficiency of the existing line searches. This line search is first proved to be an extension of the classical line search rules. On the one hand, under mild assumptions, global convergence and R-linear convergence are established for the new line search rule. On the other hand, by numerical experiments, it is shown that the line search can integrate the advantages of the existing methods in searching for a suitable step-size. Combined with the spectral step-size, a class of spectral gradient algorithms are developed and employed to solve a large number of benchmark test problems from CUTEst. Numerical results show that the new line search is promising in solving large-scale optimization problems, and outperforms the other similar ones as it is combined with a spectral gradient method. [ABSTRACT FROM AUTHOR]

In this paper, we introduce an affine scaling cubic regularization algorithm for solving optimization problem without available derivatives subject to bound constraints employing a polynomial interpolation approach to handle the unavailable derivatives of the original objective function. We first define an affine scaling cubic model of the approximate objective function which is obtained by the polynomial interpolation approach with an affine scaling method. At each iteration a candidate search direction is determined by solving the affine scaling cubic regularization subproblem and the new iteration is strictly feasible by way of an interior backtracking technique. The global convergence and local superlinear convergence of the proposed algorithm are established under some mild conditions. Preliminary numerical results are reported to show the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper we propose a tensor based description of the Bézier Shape Deformation (BSD) algorithm, denoted as T-BSD. The BSD algorithm is a well-known technique, based on the deformation of a Bézier curve through a field of vectors. A critical point in the use of real-time applications is the cost in computational time. Recently, the use of tensors in numerical methods has been increasing because they drastically reduce computational costs. Our formulation based in tensors T-BSD provides an efficient reformulation of the BSD algorithm. More precisely, the evolution of the execution time with respect to the number of curves of the BSD algorithm is an exponentially increasing curve. As the numerical experiments show, the T-BSD algorithm transforms this evolution into a linear one. This fact allows to compute the deformation of a Bézier with a much lower computational cost. [ABSTRACT FROM AUTHOR]

This paper discusses a kind of mathematical programs with equilibrium constraints (MPEC for short). By using a complementarity function and a kind of disturbed technique, the original (MPEC) problem is transformed into a nonlinear equality and inequality constrained optimization problem. Then, we combine a generalized gradient projection matrix with penalty function technique to given a generalized project metric algorithm with arbitrary initial point for the (MPEC) problems. In order to avoid Mataros effect, a high-order revised direction is obtained by an explicit formula. Under some relative weaker conditions, the proposed method is proved to possess global convergence and superlinear convergence. [ABSTRACT FROM AUTHOR]

In this paper, a class of optimization problems with nonlinear inequality constraints is discussed. Based on the ideas of sequential quadratic programming algorithm and the method of strongly sub-feasible directions, a new superlinearly convergent algorithm is proposed. The initial iteration point can be chosen arbitrarily for the algorithm. At each iteration, the new algorithm solves one quadratic programming subproblem which is always feasible, and one or two systems of linear equations with a common coefficient matrix. Moreover, the coefficient matrix is uniformly nonsingular. After finite iterations, the iteration points can always enter the feasible set of the problem, and the search direction is obtained by solving one quadratic programming subproblem and only one system of linear equations. The new algorithm possesses global and superlinear convergence under some suitable assumptions without the strict complementarity. Finally, some numerical results are reported to show that the algorithm is promising. [ABSTRACT FROM AUTHOR]

In this paper, we propose a modified two-step trust-region algorithm for solving nonlinear systems. Two-step trust-region algorithms, at every iteration, use a trust-region step and an approximate step by saving the Jacobian matrix to have fewer computations. We introduce a convex combination to modify the trust-region subproblems along with an additional criterion to verify whether the first step is accepted or not. We establish global and quadratic convergence of the algorithm under some mild assumptions. Numerical results show that the modified algorithm is efficient and promising. [ABSTRACT FROM AUTHOR]