As with Petri nets (PNs), the state space explosion has limited further studies of fuzzy Petri net (FPN), and with the rising scale of FPN, the algorithm complexity for related applications using FPN has also rapidly increased. To overcome this challenge, we propose a decomposition algorithm that includes a backwards search stage and forward strategy for further decomposition, one that divides ...

A new decomposition algorithm for training regression Support Vector Machines (SVM) is presented. The algorithm builds on the basic principles of decomposition proposed by Osuna et. al ., and addresses the issue of optimal working set selection. The new criteria for testing optimality of a working set are derived. Based on these criteria, the principle of "maximal inconsistency" is proposed to ...

Branch decomposition-based algorithms have been used in practical settings to solve some NP-hard problems like the travelling salesman problem (TSP) and general minor containment. The notions of branch decompositions and branchwidth were introduced by Robertson and Seymour to assist in proving the Graph Minors Theorem. Given a connected graph G and a branch decomposition of G of width k where k...

The sparse decomposition based on matching pursuit is an adaptive sparse expression method for signals. This paper proposes an idea concerning a composite dictionary multi-atom matching decomposition and reconstruction algorithm, and the introduction of threshold de-noising in the reconstruction algorithm. Based on the structural characteristics of gear fault signals, a composite dictionary com...

We revisit the problem of designing a linear time algorithm for undirected graph split decomposition. Although that this problem has already been claimed to be solved in [E. Dahlhaus, FSTTCS, 1994] and [E. Dahlhaus, Journal of Algorithms 36(2):205-240, 2000], we present a new well founded theoretical background for split decomposition that allow us to clearly design and proove the rst simple li...

We describe a decomposition algorithm that combines Benders and scenariobased Lagrangean decomposition for two-stage stochastic programming investment planning problems with complete recourse, where the first-stage variables are mixedinteger and the second-stage variables are continuous. The algorithm is based on the cross-decomposition scheme and fully integrates primal and dual information in...

Functional decomposition is an important technique in logic synthesis, especially for the design of lookup table based fpga architectures. We present a method for functional decomposition with a novel concept for the exploitation of don't cares thereby combining two essential goals: the minimization of the numbers of decomposition functions in the current decomposition step and the extraction o...

We show that a Schur form of a real orthogonal matrix can be obtained from a full CS decomposition. Based on this fact a CS decomposition-based orthogonal eigenvalue method is developed. We also describe an algorithm for orthogonal similarity transformation of an orthogonal matrix to a condensed product form, and an algorithm for full CS decomposition. The latter uses mixed shifted and zero-shi...

Rook polynomials are a powerful tool in the theory of restricted permutations. It is known that the rook polynomial of any board can be computed recursively, using a cell decomposition technique of Riordan. [13] In this paper, we give a new decomposition theorem, which yields a more efficient algorithm for computing the rook polynomial. We show that, in the worst case, this block decomposition ...

We extend a dynamic-programming algorithm of Keil and Snoeyink for finding a minimum convex decomposition of a simple polygon to the case when both convex polygons and pseudo-triangles are allowed. Our algorithm determines a minimum pseudo-convex decomposition of a simple polygon in O(n) time where n is the number of the vertices of the polygon. In this way we obtain a well-structured decomposi...