1. Introduction. The main objective of DDM is, when a domain Ω and one of its partitions are given, to obtain the solution of a boundary value problem defined on it (the 'global problem'), by solving problems formulated on the subdomains of the partition (the 'local problems'), exclusively. This objective can be achieved if sufficient information about the global solution is known, on the inter...

ÐGraphs are a powerful and universal data structure useful in various subfields of science and engineering. In this paper, we propose a new algorithm for subgraph isomorphism detection from a set of a priori known model graphs to an input graph that is given online. The new approach is based on a compact representation of the model graphs that is computed offline. Subgraphs that appear multiple...

Ever-growing data generate a need for new solutions to the problem of attribute reduction. Such solutions are required to dealwith limitedmemory capacity andwithmany computations needed for large data processing. This paper proposes newdefinitions of attribute reductionusinghorizontal data decomposition. Algorithms for computing reducts of an information system and decision table are developed ...

The problem of decomposing networks into modules (or clusters) has gained much attention in recent years, as it can account for a coarsegrained description of complex systems, often revealing functional subunits of these systems. A variety of module detection algorithms have been proposed, mostly oriented towards finding hard partitionings of undirected networks. Despite the increasing number o...

Hexahedral meshing needs performing volume decomposition that decomposes a complex shape into simple hex meshable sub-domains, which is a very time-consuming and experience needed work. It is necessary to research and develop intelligent swept volume decomposition approaches. The fundamental work of the volume decomposition approaches based on reconstruction of swept features is to recognize th...

We develop a theoretical and computational framework to perform guaranteed tensor decomposition, which also has the potential to accomplish other tensor tasks such as tensor completion and denoising. We formulate tensor decomposition as a problem of measure estimation from moments. By constructing a dual polynomial, we demonstrate that measure optimization returns the correct CP decomposition u...

Domain decompositionmethods are highly parallelmethods for solving elliptic partial di erential equations. In many domain decomposition variants, the domain is partitioned into a number of (possibly overlapping) subdomains before computation begins. In contrast, the domain reduction method folds the domain into a number of smaller domains covering only a small portion of the entire domain. The ...

This paper presents a constrained decomposition methodology for discrete-event models. Partial models are obtained and used for model-based fault detection and isolation. The constraints of the decomposition ensures that the resulting partial model is decoupled from a given subset of inputs. The constrained decomposition method is formulated using pair algebra concepts as an iterative algorithm...

Successful manufacturing system designs must be capable of satisfying the strategic objectives of a company. There exist numerous tools to design manufacturing systems. Most frameworks, however, do not separate objectives from means. As a result, it is difficult to understand the interactions among different design objectives and solutions and to communicate these interactions. The research des...

It is observed that the separated design of location for depots and routing for servicing customers often reach a suboptimal solution. So, solving location and routing problem simultaneously could achieve better results. In this paper, waste collection problem is considered with regard to economic and societal objective functions. A non-dominated sorting genetic algorithm (NSGA-II) is used to l...