TL;DR: In this article, a sequential search method for finding the global maximum of an objective function is proposed, which is applicable to a single variable defined on a closed interval and such that some bound on its rate of change is available.
Abstract: In this paper a sequential search method for finding the global maximum of an objective function is proposed. The method is applicable to an objective function of a single variable defined on a closed interval and such that some bound on its rate of change is available. The method is shown to be minimax. Computational aspects of the method are also discussed.
TL;DR: The numerical results of the method are compared with some existing methods and it is found that the proposed numerical method produces more accurate results than existing methods.
TL;DR: The new eighth-order Newton-type method is shown to converge of the order eight and has the efficiency index of 8 4, which is better than the well known Newton- type methods of lower order.
TL;DR: The necessary and sufficient condition for the convergence of the GSOR-like method is derived and it is shown that when α is negative, the convergence domain for the parameter ω for the MSOR-Like method is larger than that for the SOR- like method.
Abstract: The SOR-like method with two real parameters ω and α is considered for solving the augmented system. The new method is called the modified SOR-like method (MSOR-like method). The MSOR-like method becomes the SOR-like method when α = 0. The functional equation relating the parameters and eigenvalues of the iteration matrix of the MSOR-like method is obtained. Hence the necessary and sufficient condition for the convergence of the GSOR-like method is derived. It is shown that when α is negative, the convergence domain for the parameter ω for the MSOR-like method is larger than that for the SOR-like method. Finally, a numerical computation based on a particular linear system is given which clearly shows that the MSOR-like method outperforms the SOR-like method.
TL;DR: The results show that it is possible to combine these two methods to compute exponential divided differences accurately, and a hybrid algorithm is presented for which the error bound grows quite slowly with the order of the divided difference.
Abstract: : The traditional recurrence for the computation of exponential divided differences, along with a new method based on the properties of the exponential function, are studied in detail in this paper. Our results show that it is possible to combine these two methods to compute exponential divided differences accurately. A hybrid algorithm is presented for which our error bound grows quite slowly with the order of the divided difference. (Author)