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Measuring static complexity

-Solomonoff complexity measure is considered, along with Bennett's ‘logical depth’, Koppel's ‘sophistication'’, and Chaitin's analysis of the complexity of geometric objects. The pattern-theoretic point of view ... complexity may be formulated, analyzed and compared. This approach yields significant modifications of these measures, as well as several novel, general concepts for the analysis of complexity. Furthermore, it

A Hybrid Orthogonal Forward-Backward Pursuit Algorithm for Partial Fourier Multiple Measurement Vectors Problem

In solving the partial Fourier Multiple Measurement Vectors (FMMV) problem, existing greedy pursuit algorithms such as Simultaneous Orthogonal Matching Pursuit (SOMP), Simultaneous Subspace Pursuit ... Section 3, we introduce related GP algorithms, including SOMP, SSP, HMP, and FBP. In Section 4, we propose the HOFBP algorithm. In Section 5, the computational complexity of HOFBP is analyzed. In Section 6

Low complexity maximum-likelihood detector for DSTTD architecture based on the QRD-M algorithm

is capable of achieving near maximum likelihood performance. We also show that the proposed algorithm exhibits lower computational complexity than other existing maximum likelihood detectors. ... other hand, the DSTTD code is a linear dispersion code and, as such, can be decoded using the same low-complexity, ordered decision feedback algorithm developed for V-BLAST [ 10,14 ]; however, its

Self-embedding and complexity in oral registers

. non-public speech) has a significant impact on complexity in terms of self-embedding: speakers use more self-embedding in public speech production in different syntactic projections. In addition, we ... differences between right, left, and center embedding in C projections. The results confirm a preference against center embedding in non-public texts, which reflects the complexity of center embedding. Finally

An Algorithm for Higher Order Hopf Normal Forms

order using the algorithm mentioned above recursively, such that, { { a3 = 1~8 (35a~d + 5a~~ + 3a~~ + 5am + 5b~) + 3b¥~ + 5b~{ + 35b~J), b3 = ;;~ (5a~) + 3a~~ + 5a~~ + 35a~i - 35b~~ - 5bf{] - 3b~~ - 5b ... - 21M1l); here the subscripts A, B refer to the indices 10 and 11, respectively. The complexity of higher order normal forms rapidly becomes apparent as pointed out by Leung and Zhang (1994) . Thus, the

A low computational complexity normalized subband adaptive filter algorithm employing signed regressor of input signal

square performance analysis of SR-NSAF. The theoretical stability bounds relations are given in Section V. In the following, the computational complexity of the proposed algorithm will be discussed ... steady-state error similar to the conventional NSAF. In addition, the proposed algorithm has lower computational complexity than NSAF due to the signed regressor of the input signal at each subband. The

Self-Adaptive -Means Based on a Covering Algorithm

carried out on the Spark platform, and the results verify the good scalability of the C--means algorithm. This algorithm can effectively solve the problem of large-scale data clustering. Extensive ... the clustering operations. 3.4. Computational Complexity Analysis This section discusses the computational complexity of the C--means algorithm with two phases. First, we analyze the computational

Identification of influential spreaders in complex networks using HybridRank algorithm

, and global measures like closeness and betweenness centrality could better identify influential spreaders but they have some limitations. In this paper, we propose the HybridRank algorithm using a new ... algorithm will be presented to deal with the problem of influence maximization. In HybridRank algorithm, the main idea is to define a set of influential spreaders based on hybrid centrality; by interacting

An Artificial Bee Colony Algorithm with Random Location Updating

As a novel swarm intelligence algorithm, artificial bee colony (ABC) algorithm inspired by individual division of labor and information exchange during the process of honey collection has advantage ... problem increases, the computational complexity also increases dramatically, and thus the algorithm performance degenerates. But in the case of dimension D = 100, the convergence accuracy and stability of

An Artificial Bee Colony Algorithm with Random Location Updating

As a novel swarm intelligence algorithm, artificial bee colony (ABC) algorithm inspired by individual division of labor and information exchange during the process of honey collection has advantage ... problem increases, the computational complexity also increases dramatically, and thus the algorithm performance degenerates. But in the case of dimension D = 100, the convergence accuracy and stability of

Applying algorithm selection to abductive diagnostic reasoning

reasoning relying on the notion of logical entailment. Nevertheless, abductive reasoning is an intractable problem and computing solutions for instances of reasonable size and complexity persists to pose a ... efficient abduction technique given a new diagnosis problem. To assess the predictor's selection capabilities and the suitability of the metaapproach in general, we conducted an empirical analysis featuring

L* Algorithm—A Linear Computational Complexity Graph Searching Algorithm for Path Planning

The state-of-the-art graph searching algorithm applied to the optimal global path planning problem for mobile robots is the A* algorithm with the heap structured open list. In this paper, we present ... the use of bidirectional sublists (buckets) ensures the linear computational complexity of the L* algorithm because the nodes in the current bucket can be processed in any sequence and it is not

Ensemble Prediction Algorithm of Anomaly Monitoring Based on Big Data Analysis Platform of Open-Pit Mine Slope

analysis expectation at the scale of big data. In order to make up this disadvantage, this research will provide an ensemble prediction algorithm of anomalous system data based on time series and an ... the accuracy of prediction. In addition, the evaluation system greatly supports the algorithm, which enhances the stability of log analysis platform. 1. Introduction Landslide is one of the most

Instrumental Variable-Based OMP Identification Algorithm for Hammerstein Systems

system and to promote the computational efficiency of the identification algorithm, a sparsity-seeking orthogonal matching pursuit (OMP) optimization method of compressive sensing is extended to identify ... best fitting column of the measurement matrix and the corresponding sparse signal in each selected step. Due to the selection being orthogonal, the OMP algorithm has a lower computational complexity

Optimal deterministic algorithm generation

computational steps of which the selected algorithms consist. The objective function of the optimization problem encodes the merit function of the algorithm, e.g., the computational cost (possibly also including ... convergence of the algorithm, i.e., solution of the problem at hand. The formulation is described prototypically for algorithms used in solving nonlinear equations and in performing unconstrained optimization

A Combined Localization Algorithm for Wireless Sensor Networks

Analysis algorithm is used to obtain more accurate coordinate transformation. Through extensive simulations and the repeatable experiments under diverse representative networks, it can be confirmed that the ... , the time complexity of the MA-MDS algorithm is lower than that of the heuristic algorithm. 4.2. Simulation Results Analysis In this section we conduct the simulation studies on the MA-MDS algorithm. The

A chaotic simulated annealing and particle swarm improved artificial immune algorithm for flexible job shop scheduling problem

is taken as the objective function. Secondly, artificial immune algorithm is used to solve the problem, and particle swarm optimization algorithm is taken as the operator to embed into manual immune ... immune algorithm and the classical algorithm searched for flexibility. The number of optimal solutions for the job shop scheduling problem and the number of iterations are shown in Table 1. Analysis of the

A Binary Cuckoo Search Big Data Algorithm Applied to Large-Scale Crew Scheduling Problems

the results and convergence times of the algorithm under different conditions in the number of solutions and the processing capacity. Under what conditions can we obtain acceptable results in an ... algorithm using the MapReduce programming paradigm implemented in the Apache Spark tool. The algorithm is applied to different instances of the crew scheduling problem. The experiments show that the

Resource Scheduling Based on Improved Spectral Clustering Algorithm in Edge Computing

algorithm (ISCM) in this paper. Based on the improved k-means algorithm, the ISCM algorithm solves the problem that the clustering result is sensitive to the initial value and realizes the reclustering, which ... algorithm proposed in this paper is based on clustering method. Cluster analysis is a multivariate statistical analysis and a part of unsupervised pattern recognition [30–32]. Compared with k-means clustering

The Single Row Routing Problem Revisited: A Solution Based on Genetic Algorithms

) complexity technique based on GAs heuristic is obtained to solve the general SRR problem containing n nodes. Experimental results show that the algorithm is faster and can often generate better results than ... the routing problem. A faster algorithm has been developed by Raghavan and Sahni [ 3 ]. It has a complexity of O(k!*k*n*log k ) where k is the maximum street width. The fastest heuristic found in the