We introduce a family of highly efficient (non-iterative) numerical methods for a wide class of hybrid control systems. The application of Dijkstra’s classical method to a discrete optimal trajectory problem on a network obtains the solution in O(M logM) operations. The key idea behind the method is a careful use of the direction of information propagation, stemming from the optimality principl...

The hybrid stress boundary element method (HSBEM) was introduced in 1987 on the basis of the Hellinger-Reissner potential, as a generalization of Pian’s hybrid finite element method. This new two-field formulation makes use of fundamental solutions to interpolate the stress field in the domain of an elastic body, which ends up discretized as a superelement with arbitrary shape and arbitrary num...

We discuss hybrid atomistic-continuum methods for multiscale hydrodynamic applications. Both dense fluid and dilute gas formulations are considered. The choice of coupling method and its relation to the fluid physics is discussed. The differences in hybrid methods resulting from underlying compressible and incompressible continuum formulations as well as the importance of timescale decoupling a...

This paper concerns developing two hybrid proximal point methods (PPMs) for finding a common solution of some optimization-related problems. First we construct an algorithm to solve simultaneously an equilibrium problem and a variational inequality problem, combing the extragradient method for variational inequalities with an approximate PPM for equilibrium problems. Next we develop another alg...

LAKe (Linear Algebra Kernel) is a linear algebra class library developed using an object oriented approach, in order to enable very good code reuse for sequential and parallel linear algebra applications. An application implemented using LAKe can be run either in sequential or parallel mode using the same code. In this paper we propose an extension to the LAKe library, which allows making use o...

This paper considers the problem of constructing confidence intervals for a single parameter θ in a multiparameter or nonparametric family. Hybrid resampling methods, which “hybridize” the essential features of bootstrap and exact methods, are proposed and developed for both parametric and nonparametric situations. In particular, we apply such methods to construct confidence regions, whose cove...

The use of intelligent systems for stock market prediction has been widely established. In this paper, we investigate how the seemingly chaotic behavior of stock markets could be well represented using several connectionist paradigms and soft computing techniques. To demonstrate the different techniques, we consider the Nasdaq-100 index of Nasdaq Stock Market and the S&P CNX NIFTY stock index. ...

Hybrid methods, which combine and integrate several biochemical and biophysical techniques, have rapidly caught up in the last twenty years to provide a way to obtain a fuller description of proteins and molecular complexes with sizes and complexity otherwise not easily affordable. Here, we review the use of a robust hybrid methodology based on a mixture of NMR, SAXS, site directed mutagenesis ...

Many biological and physical systems exhibit behaviour at multiple spatial, temporal or population scales. Multiscale processes provide challenges when they are to be simulated using numerical techniques. While coarser methods such as partial differential equations are typically fast to simulate, they lack the individual-level detail that may be required in regions of low concentration or small...

OF DOCTORAL DISSERTATION HELSINKI UNIVERSITY OF TECHNLOGY P.O. BOX 1000, FI-02015 TKK http://www.tkk.fi Author Xiaolei Wang Name of the dissertation Hybrid Nature-Inspired Computation Methods for Optimization Manuscript submitted 23/2/2009 Manuscript revised 28/4/2009 Date of the defence 29/5/2009 Monograph Article dissertation (summary + original articles) Faculty Faculty of Electronics, Commu...