The use of matchings is a powerful technique for scaling and ordering sparse matrices prior to the solution of a linear system Ax D b. Traditional methods such as implemented by the HSL software package MC64 use the Hungarian algorithm to solve the maximum weight maximum cardinality matching problem. However, with advances in the algorithms and hardware used by direct methods for the paralleliz...

Neural architecture is a purely numeric framework, which fits the data as a continuous function. However, lacking of logic flow (e.g. if, for, while), traditional algorithms (e.g. Hungarian algorithm, A∗ searching, decision tress algorithm) could not be embedded into this paradigm, which limits the theories and applications. In this paper, we reform the calculus graph as a dynamic process, whic...

This paper presents an improved version of a harmony meta-heuristic algorithm, (IHSCH), for solving the linear assignment problem. The proposed algorithm uses chaotic behavior to generation a candidate solution in a behavior similar to acoustic monophony. Nume rical results show that the IHSCH is able to obtain the optimal results in comparison with tradit ional methods (the Hungarian method). ...

The author p r e s e n t s a geometrical modelwhich illuminates variants of the Hungarian method for the solution of the assignment problem.

We present an e cient hybrid method for aligning sentences with their translations in a parallel bilingual corpus. The new algorithm is composed of a length-based and anchor matching method that uses Named Entity recognition. This algorithm combines the speed of length-based models with the accuracy of anchor nding methods. The accuracy of nding cognates for Hungarian-English language pair is e...

In this paper, we present the dynamic Hungarian algorithm, applicable to optimally solving the assignment problem in situations with changing edge costs or weights. This problem is relevant, for example, in a transportation domain where the unexpected closing of a road translates to changed transportation costs. When such cost changes occur after an initial assignment has been made, the new pro...

In this paper, we propose a new multi-objective evolutionary algorithm (MOEA), which transforms a multi-objective optimization problem into a linear assignment problem using a set of weight vectors uniformly scattered. Our approach adopts uniform design to obtain the set of weights and Kuhn-Munkres’ (Hungarian) algorithm to solve the assignment problem. Differential evolution is used as our sea...

There are a number of issues which are involved with organizing a conference. Among these issues, assigning conference-papers to reviewers is one of the most difficult tasks. Assigning conference-papers to reviewers is automatically the most crucial part. In this paper, we address this issue of paper-to-reviewer assignment, and we propose a method to model the reviewers, based on the matching d...

Consider a multi-class classification problem. Given is a set of objects, for which it is known that there is at most one object from each class. The problem is to identify the missing classes. We propose to apply the Hungarian assignment algorithm to the logarithms of the estimated posterior probabilities for the given objects. Each object is thereby assigned to a class. The unassigned classes...