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Abstract: A counterexample is provided in this short note to show that some of local convergence theorems established in [D.K.R. Babajee, M.Z. Dauhoo, An analysis of the properties of the variants of Newton’s method with third order convergence, Appl. Math. Comput. 183 (2006) 659–684] are not always true. Some mistakes in the proofs of these theorems are pointed out. [Copyright &y& Elsevier]

Abstract: In this paper, we present an enhanced parallel cat swarm optimization (EPCSO) method for solving numerical optimization problems. The parallel cat swarm optimization (PCSO) method is an optimization algorithm designed to solve numerical optimization problems under the conditions of a small population size and a few iteration numbers. The Taguchi method is widely used in the industry for optimizing the product and the process conditions. By adopting the Taguchi method into the tracing mode process of the PCSO method, we propose the EPCSO method with better accuracy and less computational time. In this paper, five test functions are used to evaluate the accuracy of the proposed EPCSO method. The experimental results show that the proposed EPCSO method gets higher accuracies than the existing PSO-based methods and requires less computational time than the PCSO method. We also apply the proposed method to solve the aircraft schedule recovery problem. The experimental results show that the proposed EPCSO method can provide the optimum recovered aircraft schedule in a very short time. The proposed EPCSO method gets the same recovery schedule having the same total delay time, the same delayed flight numbers and the same number of long delay flights as the . The optimal solutions can be found by the proposed EPCSO method in a very short time. [Copyright &y& Elsevier]

Abstract: In this paper, the honey bee mating optimization (HBMO) algorithm is used to improve the detection of the concave region connected with the control points of active contour. In the traditional active contour model (ACM) method, the updating of control point is based on its local energy within a small searching window. As a result, it always results in the failure of precisely searching the boundary concavities. In order to vanquish these drawbacks, the HBMO-based snake algorithm is applied in this paper to search for the optimal position in a lager searching window around each control point. In this proposed algorithm, to each active contour there is a chromosome that includes several genes as well as the control points of active contour. These control points are moved iteratively by minimizing the total energy of the active contour. Experimental results reveal that the proposed HBMO-based snake algorithm can locate the object boundary of concavity more precisely without requiring large number of computational time. [Copyright &y& Elsevier]

Abstract: Multistep quasi-Newton methods were introduced by Ford and Moghrabi [1]. They address the problem of the unconstrained minimisation of a function whose gradient and Hessian are denoted by g and G, respectively. These methods generalised the standard construction of quasi-Newton methods and were based on employing interpolatory polynomials to utilise information from more than one previous step. In a series of papers, Ford and Moghrabi [2–5] have developed various techniques for determining the parametrisation of the interpolating curves. In [2], they introduced two-step metric-based methods which determine the set of parameter values required through measuring distances between various pairs of the iterates employed in the current interpolation. One of the most successful methods in [2] was found to be in the “fixed-point” class, in which the parametrisation of the interpolating curve is determined, at each iteration, by reference to distances measured from a fixed iterate. As suggested in [1], multistep quasi-Newton methods can be constructed for any number of steps.In this paper, we therefore extend the previous work by describing the development of some three-step methods which use the “fixed-point” approach and data derived from the latest four iterates. The experimental results provide evidence that the new methods offer a significant improvement in performance when compared with the standard BFGS method and the unit-spaced three-step method, particularly as the dimension of the test problems grows. [Copyright &y& Elsevier]

Abstract: Kernel methods map, usually nonlinearly, the data from input space into a higher-dimensional feature space, in which linear algorithms are performed. In many applications, the inverse mapping is also useful, and the pre-images of some feature vectors need to be found in input space. However, finding pre-images is often a difficult optimization problem. This paper attempts to use evolution strategies (ES) to seek pre-images. This method firstly selects some of the nearest training patterns of an unknown pre-image as the initial group of the ES, then the ES carries out an iterative process to find the pre-images or approximate pre-images. Experimental results based on kernel principal component analysis (KPCA) for pattern denoising show that our proposed method outperforms some conventional techniques, including gradient descent technique, kernel ridge regression, and distance constraint method. Compared to these conventional techniques, the ES-based method is also straightforward to understand, and is easy to implement. [Copyright &y& Elsevier]

Abstract: In this paper, we investigate new lower and upper bounds for the multiple-center hybrid flow shop scheduling problem. We propose a family of center-based lower bounds as well as a destructive lower bound that is based on the concept of revised energetic reasoning. Also, we describe an optimization-based heuristic that requires iteratively solving a sequence of parallel machine problems with heads and tails. We present the results of extensive computational experiments that provide evidence that the proposed bounding procedures consistently improve the best existing ones. [Copyright &y& Elsevier]

Abstract: In this paper we propose a nonmonotone approach to recurrent neural networks training for temporal sequence processing applications. This approach allows learning performance to deteriorate in some iterations, nevertheless the network’s performance is improved over time. A self-scaling BFGS is equipped with an adaptive nonmonotone technique that employs approximations of the Lipschitz constant and is tested on a set of sequence processing problems. Simulation results show that the proposed algorithm outperforms the BFGS as well as other methods previously applied to these sequences, providing an effective modification that is capable of training recurrent networks of various architectures. [Copyright &y& Elsevier]

Abstract: In this paper, the optimum design of a factorable Nyquist filter with the intersymbol interference (ISI) being exactly zero is formulated as a nonlinear optimization problem with continuous inequality constraints. An iterative scheme is developed for solving this semi-infinite optimization problem, where an improved dual parametrization method is utilized in each iteration of the iterative scheme. Trade-off between robustness against timing jitter and small stopband attenuation is achieved via an adjustment of a parameter. Some examples are solved using the proposed iterative method. [Copyright &y& Elsevier]

Abstract: In this paper, we propose a capacity scaling heuristic using a column generation and row generation technique to address the multicommodity capacitated network design problem. The capacity scaling heuristic is an approximate iterative solution method for capacitated network problems based on changing arc capacities, which depend on flow volumes on the arcs. By combining a column and row generation technique and a strong formulation including forcing constraints, this heuristic derives high quality results, and computational effort can be reduced considerably. The capacity scaling heuristic offers one of the best current results among approximate solution algorithms designed to address the multicommodity capacitated network design problem. [Copyright &y& Elsevier]

Abstract: In this paper we propose a primal-dual path-following interior-point algorithm for second-order cone optimization. The algorithm is based on a new technique for finding the search directions and the strategy of the central path. At each iteration, we use only full Nesterov–Todd step. Moreover, we derive the currently best known iteration bound for the algorithm with small-update method, namely, , where N denotes the number of second-order cones in the problem formulation and the desired accuracy. [Copyright &y& Elsevier]

Abstract: In this paper, a kind of optimization problems with nonlinear inequality constraints is discussed. Combined the ideas of norm-relaxed SQP method and strongly sub-feasible direction method as well as a pivoting operation, a new fast algorithm with arbitrary initial point for the discussed problem is presented. At each iteration of the algorithm, an improved direction is obtained by solving only one direction finding subproblem which possesses small scale and always has an optimal solution, and to avoid the Maratos effect, another correction direction is yielded by a simple explicit formula. Since the line search technique can automatically combine the initialization and optimization processes, after finite iterations, the iteration points always get into the feasible set. The proposed algorithm is proved to be globally convergent and superlinearly convergent under mild conditions without the strict complementarity. Finally, some numerical tests are reported. [Copyright &y& Elsevier]

Abstract: The power method is an algorithm for computing the largest eigenvalue of matrix A in absolute value. To find the other eigenvalues one can apply the power method to the matrix for some shift σ. This scheme is called the inverse iteration method. Both of these two methods produce a convergence sequence and the limit is approximated by one of the iterates. In the chosen iterate, it may be difficult to estimate the global error, consisting of the truncation error and the round-off error. In this paper, by using the CESTAC method and the CADNA library, we propose a method for computing the optimal iterate, the iterate for which the global error is minimal. In the proposed method the accuracy of the computed eigenvalue may also be estimated. Some numerical examples are given to show the efficiency of the method. [Copyright &y& Elsevier]

Abstract: In this paper, we present a nonmonotone conic trust region method based on line search technique for unconstrained optimization. The new algorithm can be regarded as a combination of nonmonotone technique, line search technique and conic trust region method. When a trial step is not accepted, the method does not resolve the trust region subproblem but generates an iterative point whose steplength satisfies some line search condition. The function value can only be allowed to increase when trial steps are not accepted in close succession of iterations. The local and global convergence properties are proved under reasonable assumptions. Numerical experiments are conducted to compare this method with the existing methods. [Copyright &y& Elsevier]

Abstract: In this paper we develop a new class of conjugate gradient methods for unconstrained optimization problems. A new nonmonotone line search technique is proposed to guarantee the global convergence of these conjugate gradient methods under some mild conditions. In particular, Polak–Ribiére–Polyak and Liu–Storey conjugate gradient methods are special cases of the new class of conjugate gradient methods. By estimating the local Lipschitz constant of the derivative of objective functions, we can find an adequate step size and substantially decrease the function evaluations at each iteration. Numerical results show that these new conjugate gradient methods are effective in minimizing large-scale non-convex non-quadratic functions. [Copyright &y& Elsevier]

Abstract: This paper investigates the energy function technique for load margin calculation when an algebraic set of equations is considered, so only the system potential energy is calculated. The unstable solutions, demanded by energy-based methods, are qualitatively analyzed and an approach for finding such solutions is proposed. The whole iterative process is obtained with the help of the Newton’s method, enabling a fast, reliable and accurate sub-optimal solution. The accuracy and speed up of the method are assessed by comparing it with the continuation method. This assessment is carried out with the help of some sample power systems, since they contain several important operating restrictions. These systems are analyzed with their limits considered, in order to make the results more realistic. [Copyright &y& Elsevier]

Abstract: In this paper, we propose a new trust region method for unconstrained optimization problems. The new trust region method can automatically adjust the trust region radius of related subproblems at each iteration and has strong global convergence under some mild conditions. We also analyze the global linear convergence, local superlinear and quadratic convergence rate of the new method. Numerical results show that the new trust region method is available and efficient in practical computation. [Copyright &y& Elsevier]

Abstract: The paper concerns the Cholesky factorization of symmetric positive definite matrices arising in interior point methods. Our investigation is based on a property of the Cholesky factorization which interprets “small” diagonal values during factorization as degeneracy in the scaled optimization problem. A practical, scaling independent technique, based on the above property, is developed for the modified Cholesky factorization of interior point methods. This technique increases the robustness of Cholesky factorizations performed during interior point iterations when the optimization problem is degenerate. Our investigations show also the limitations of interior point methods with the recent implementation technology and floating point arithmetic standard. We present numerical results on degenerate linear programming problems of NETLIB. [Copyright &y& Elsevier]