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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

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

Input/Output Pad Placement Problem

pad placement is the problem of placing I/O pads around a VLSI chip boundary such that it leads to a reduction in the cost of cells placement. Pad placement is typically done as the last stage in a VLSI ... layout design. However, some cell placement packages are sensitive to the pad placement and require such placement to be done first [ 1 ]. To solve this problem Tsay et al. [ 6 ] proposed a similar

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

Terminal holographic complexity

Abstract We introduce a quasilocal version of holographic complexity adapted to ‘terminal states’ such as spacelike singularities. We use a modification of the action-complexity ansatz, restricted to ... Conclusions and outlook Introduction analysis of certain cosmological singularities with controlled AdS/CFT embedding. Here we seek to provide quasilocal notions of complexity which may be abstracted from

A Fast Algorithm for Performance-Driven Module Implementation Selection

We develop an O(p log n) time algorithm to obtain optimal solutions to the p-pin n-net single channel performance-driven implementation selection problem in which each module has at most two possible ... implementations (2-PDMIS). Although Her, Wang and Wong [1] have also developed an O(p log n) algorithm for this problem, experiments indicate that our algorithm is twice as fast on small circuits and up to eleven

Quantum-Inspired Wolf Pack Algorithm to Solve the 0-1 Knapsack Problem

This paper proposes a Quantum-Inspired wolf pack algorithm (QWPA) based on quantum encoding to enhance the performance of the wolf pack algorithm (WPA) to solve the 0-1 knapsack problems. There are ... Grover algorithm can solve the search problem of the scale of N in the case of the time complexity . In this section, we propose the QWPA through several quantum concepts related to the Grover algorithm

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

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

Unraveling the Complexity of Wildland Urban Interface Fires

Recent wildland urban interface fires have demonstrated the unrelenting destructive nature of these events and have called for an urgent need to address the problem. The Wildfire paradox reinforces ... Oakland fire in an attempt to unravel the complexity of community fires. We use traditional centrality measures to identify critical behavior patterns and to evaluate the effect of fire mitigation

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

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

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

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

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

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

Enhanced intelligent water drops algorithm for multi-depot vehicle routing problem

The intelligent water drop algorithm is a swarm-based metaheuristic algorithm, inspired by the characteristics of water drops in the river and the environmental changes resulting from the action of ... routing problem: models and solutions . Journal of Quality Measurement and Analysis . 2008 ; 4 ( 1 ): 205 ± 18 . 8. CË atay B. A new saving-based ant algorithm for the vehicle routing problem with

Inverse dynamics of mechanical multibody systems: An improved algorithm that ensures consistency between kinematics and external forces

forces are usually not consistent due to incorrect modelling assumptions and measurement errors. This is commonly resolved by introducing ‘residual forces and torques’ which compensate for this problem ... ' which compensate for this problem, but do not exist in reality. In this study a constrained optimization algorithm is proposed that finds the kinematics that are mechanically consistent with measured