Magnetohydrodynamic steady flow computations in three dimensions

A complete three-dimensional mathematical model has been developed governing the steady, laminar flow of an incompressible fluid subjected to a magnetic field and including internal heating due to the Joule effect, heat transfer due to conduction, and thermally induced buoyancy forces. The thermally induced buoyancy was accounted for via the Boussinesq approximation. The entire system of eight partial differential equations was solved by integrating intermittently a system of five fluid flow equations and a system of three magnetic field equations and transferring the information through source-like terms. An explicit Runge-Kutta time-stepping algorithm and a finite difference scheme with artificial compressibility were used in the general non-orthogonal curvilinear boundary-conforming co-ordinate system. Comparison of computational results and known analytical solutions in two and three dimensions demonstrates high accuracy and smooth monotone convergence of the iterative algorithm. Results of test cases with thermally induced buoyancy demonstrate the stabilizing effect of the magnetic field on the recirculating flows.     

High order starting iterates for implicit Runge-Kutta methods: an improvement for variable-step symplectic integrators

When dealing with implicit Runge–Kutta methods, the equationsdefining the stages are usually solved by iterative methods.The closer the first iterate is to the solution, the fewer iterationsare required. In this paper the author presents and analysesnew high order algorithms to compute such initial iterates.Numerical experiments are given to illustrate the performanceof the new procedures when combined with a variable-step symplecticintegrator.     

平面上的min-max型点-线选址问题

本文研究两类平面选址问题：(1)求一直线到n个给定点的最大加权距离为最小；(2)求一点到n条给定直线的最大加权距离为最小．对这两个非线性优化问题，我们给出最优解的刻划及迭代次数为多项式的算法．     

Analytic solutions for time-dependent Schrödinger equations with linear of nonlinear Hamiltonians

The decomposition method is applied to the time-dependent Schrödinger equation for linear or nonlinear Hamiltonian operators, without linearization, perturbation, or numerical methods, to obtain a rapidly converging analytic solution     

Reduced conservatism in stability analysis of interval matrices

This paper presents new sufficient conditions for the stability of interval matrices. An iterative algorithm, based on the Lyapunov stability approach, is developed. In this way, the stability bounds are increased substantially over the ones recently reported in the literature. Three numerical examples are given to demonstrate the merits of the proposed approach.This research was supported in part by the US-Yugoslav Joint Fund for Scientific and Technological Cooperation in cooperation with DOE under Grant No. PP-727.     

Parallel Asynchronous Schwarz and Multisplitting Methods for a Nonlinear Diffusion Problem

Parallel asynchronous subdomain algorithms with flexible communication for the numerical solution of nonlinear diffusion problems are presented. The discrete maximum principle is considered and the Schwarz alternating method and multisplitting methods are studied. A connection is made with M-functions for a classical nonlinear diffusion problem. Finally, computational experiments carried out on a shared memory multiprocessor are presented and analyzed.     

Polynomial algorithms that prove an NP-Hard hypothesis implies an NP-hard conclusion

A number of results in hamiltonian graph theory are of the form “ implies ”, where is a property of graphs that is NP-hard and is a cycle structure property of graphs that is also NP-hard. An example of such a theorem is the well-known Chvátal–Erd s Theorem, which states that every graph G with κ is hamiltonian. Here κ is the vertex connectivity of G and is the cardinality of a largest set of independent vertices of G. In another paper Chvátal points out that the proof of this result is in fact a polynomial time construction that either produces a Hamilton cycle or a set of more than κ independent vertices. In this note we point out that other theorems in hamiltonian graph theory have a similar character. In particular, we present a constructive proof of a well-known theorem of Jung (Ann. Discrete Math. 3 (1978) 129) for graphs on 16 or more vertices.     

A partition-based approach towards constructing Galois (concept) lattices

Galois lattices and formal concept analysis of binary relations have proved useful in the resolution of many problems of theoretical or practical interest. Recent studies of practical applications in data mining and software engineering have put the emphasis on the need for both efficient and flexible algorithms to construct the lattice. Our paper presents a novel approach for lattice construction based on the apposition of binary relation fragments. We extend the existing theory to a complete characterization of the global Galois (concept) lattice as a substructure of the direct product of the lattices related to fragments. The structural properties underlie a procedure for extracting the global lattice from the direct product, which is the basis for a full-scale lattice construction algorithm implementing a divide-and-conquer strategy. The paper provides a complexity analysis of the algorithm together with some results about its practical performance and describes a class of binary relations for which the algorithm outperforms the most efficient lattice-constructing methods.     

Starting algorithms for low stage order RKN methods

When second order differential equations are solved with Runge-Kutta-Nyström methods, the computational effort is dominated by the cost of solving the nonlinear system. That is why it is important to have good starting values to begin the iterations. In this paper we consider a type of starting algorithms without additional computational cost. We study the general order conditions and the maximum order achieved when the Runge-Kutta-Nyström method satisfies some simplifying assumptions.     

A combined genetic algorithm-fuzzy logic controller (GA–FLC) in nonlinear programming

This paper presents a combined genetic algorithm-fuzzy logic controller (GA–FLC) technique for constrained nonlinear programming problems. In the standard Genetic algorithms, the upper and lower limits of the search regions should be given by the decision maker in advance to the optimization process. In general a needlessly large search region is used in fear of missing the global optimum outside the search region. Therefore, if the search region is able to adapt toward a promising area during the optimization process, the performance of GA will be enhanced greatly. Thus in this work we tried to investigate the influence of the bounding intervals on the final result. The proposed algorithm is made of classical GA coupled with FLC. This controller monitors the variation of the decision variables during process of the algorithm and modifies the boundary intervals to restart the next round of the algorithm. These characteristics make this approach well suited for finding optimal solutions to the highly NLP problems. Compared to previous works on NLP, our method proved to be more efficient in computation time and accuracy of the final solution.