A decomposition of a molecular conformational space into sets or functions (states) allows for a reduced description of the dynamical behavior in terms of transition probabilities between these states. Spectral clustering of the corresponding transition probability matrix can then reveal metastabilities. The more states are used for the decomposition, the smaller the risk to cover multiple conf...

This paper investigates a two-dimensional angle of arrival (2D AOA) estimation algorithm for the electromagnetic vector sensor (EMVS) array based on Type-2 block component decomposition (BCD) tensor modeling. Such a tensor decomposition method can take full advantage of the multidimensional structural information of electromagnetic signals to accomplish blind estimation for array parameters wit...

The LU decomposition is a widely used method to solve the dense linear algebra in many scientific computation applications. In recent years, the single instruction multiple data (SIMD) technology has been a popular method to accelerate the LU decomposition. However, the pipeline parallelism and memory bandwidth utilization are low when the LU decomposition mapped onto SIMD processors. This pape...

QR decomposition of matrix is one of the important problems in the field of matrix theory. Besides, there are also so many extensive applications that using QR decomposition. Because of that, there are many researchers have been studying about algorithm for this decomposition. Two of those researchers are Feng Tianxiang and Liu Hongxia. In their paper, they proposed new algorithm to make QR dec...

we consider the queueing maximal covering location-allocation problem (qm-clap) with an m/m/1 queueing system. we propose a new solution procedure based on decomposition of the problem into smaller sub-problems. we solve the resulting sub-problems both with a branch and bound algorithm and with the meta-heuristic grasp. we also solve the entire model with grasp. computational results for these ...

No general algorithm is known for the functional decomposition of wild polynomials over a finite field. However partial solutions exist. In particular, a fast functional decomposition algorithm for linearised polynomials has been developed using factoring methods in skew-polynomial rings. This algorithm is extended to a related class of wild polynomials, which are sub-linearised polynomials.

We present a new algorithm that can output the rank-decomposition of width at most k of a graph if such exists. For that we use an algorithm that, for an input matroid represented over a fixed finite field, outputs its branch-decomposition of width at most k if such exists. This algorithm works also for partitioned matroids. Both these algorithms are fixed-parameter tractable, that is, they run...

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 f...

The sparse decomposition of vibration signal is the important part of the fault diagnosis of Double Cavity Jaw Crusher. But the calculation count of sparse decomposition is very large, and it is difficult to fulfill signal processing. After analyzing characteristics of Double Cavity Jaw Crusher, this paper proposes applying the hybrid algorithm, DEPSO which mixed the characteristics of particle...