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-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

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

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

. 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

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

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

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

, 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

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

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

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

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

**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

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**

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

**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

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

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

**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

) **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