Read the abstract, see who wrote it and who cites it, and get the citation in APA, MLA, Chicago or Harvard.
About 10,000 papers for “complexity measures”
Marc Moreno Maza, Cilk For Loops · 2013
The fork-join parallelism model int fib (int n) { if (n<2) return (n); else { int x,y; x = cilk_spawn fib(n-1); y = fib(n-2); 2); cilk_sync; return (x+y);
Cited by 0
Stephen R. Tate · Elsevier eBooks · 2003
Cited by 3
Frank Witte · 2024
Cited by 0
Benoît Sagot · HAL (Le Centre pour la Communication Scientifique Directe) · 2013
International audience
Cited by 11Open access
Alejandro Ramírez-Rojas, Leonardo Di G. Sigalotti, Elsa Leticia Flores-Márquez et al. · Elsevier eBooks · 2019
Cited by 0
Ivan M. Havel · ACM SIGACT News · 1971
The well-known abstract approach to the theory of computational complexity is based on two axioms (Ax. 1 and 2 below) for complexity measures introduced by Blum [I]. Blum's axioms are generally considered to be the widest formalization of the intuitive concept of computational complexity that still yields interesting results. However, it appears that Blum's axioms can be further weakened without loss of their mathematical power, and, moreover, in some respect with a benefit in their relevance to corresponding intuitive concepts.
Cited by 5Open access
Sanjay Misra · IGI Global eBooks · 2012
Cognitive informatics (CI), a multidisciplinary area of research tries to solve the common problems in the field of informatics, computer science, software engineering, mathematics, cognitive science, neurobiology, psychology, and physiology. Measurement in software engineering is also a core issue which is still striving for its standardization process. In recent years, several cognitive complexity measures based on CI have been proposed. However, each of them has their own advantages and disadvantages. This chapter presents a critical review on existing cognitive complexity measures. Furthermore, a comparative study based on some selected attributes has been presented.
Cited by 0
Linzhuo Li (14803415), Nannan Zhao (4331221) · Figshare · 2023
<p>Comparison of the R&D measure of complexity with Alternatives. (A). Knowledge complexity and scaling of patent numbers in China, which is similar to previous studies. (B). Knowledge complexity and scaling of revenue for manufacturing sectors. The terms show that sectors that have a higher R&D expenditure ratio correspond to those with higher complexity (greater average years of subclass introduction) in patent systems.</p> <p>(TIF)</p>
Cited by 0Open access
Marc Moreno Maza, Cilk For Loops · 2015
The fork-join parallelism model int fib (int n) { if (n&lt;2) return (n); Example: fib(4)else { int x,y; x = cilk_spawn fib(n-1); y fib(n 2); 4 cilk_sync; return (x+y);
Cited by 0
Ilya Shmulevich, Dirk-jan Povel · 2008
Cited by 0
V. S. Anil Kumar, Virginia Bio-informatics, Srinivasan Parthasarathy et al. · 2008
Given a wireless network and a collection of source-destination pairs {(si, ti)}, what is the maximum end-to-end rate (throughput) at which the network can transfer data from the sources to their corresponding destinations? The problem is nontrivial to solve in the case of wireless networks due to interference. It is additionally complicated when taking into account TCP like transport protocols. Here, we present near-optimal provably good polynomialtime routing and scheduling algorithms for solving these and other throughput maximization problems in wireless ad hoc networks. We also present distributed algorithms for simultaneously optimizing a large class of throughput related objectives with fixed routes and schedules. We consider a wide variety of conflict-graph based models with both primary and secondary wireless interference constraints. Our techniques can accommodate a variety of routing constraints such as low energy, low hop-count, etc. as well as incorporate wireless technologies such as multiple channels and directional antennas. Categories and Subject Descriptors
Cited by 0
Josep Gregori, Celia Perales, Francisco Rodríguez‐Frias et al. · Virology · 2016
Cited by 110
April Pease, Korosh Mahmoodi, Bruce J. West · Chaos Solitons & Fractals · 2018
We present a technique to search for the presence of crucial events in music, based on the analysis of the music volume. Earlier work on this issue was based on the assumption that crucial events correspond to the change of music notes, with the interesting result that the complexity index of the crucial events is μ ≈ 2, which is the same inverse power-law index of the dynamics of the brain. The search technique analyzes music volume and confirms the results of the earlier work, thereby contributing to the explanation as to why the brain is sensitive to music, through the phenomenon of complexity matching. Complexity matching has recently been interpreted as the transfer of multifractality from one complex network to another. For this reason we also examine the mulifractality of music, with the observation that the multifractal spectrum of a computer performance is significantly narrower than the multifractal spectrum of a human performance of the same musical score. We conjecture that although crucial events are demonstrably important for information transmission, they alone are not sufficient to define musicality, which is more adequately measured by the multifractality spectrum.
Cited by 37Open access
2000
This document provides formulae (from [3]) on which the calculations by icomp are based. icomp calculates Akaike information criteria (AIC), Schwartz Bayesian information criteria (SBC, SBIC), and Bozdogan’s index of informational complexity (ICOMP). These criteria are used to select the “best ” model compromising an adequate goodness of fit and a small number of parameters by adding a penalty for overparametrization to the lack of fit measure (estimate of the maximum likelihood or the residual sum of squares). The best model minimizes the criterion. The three informational measures differ in the penalty term, SBIC penalizing more severely for larger samples, and ICOMP accounting for covariance structure of a model (and, thus, for collinearity between the factors and dependence among the parameter estimates). AIC = −2 lnL(θ̂k) + 2k; (1) SBIC = −2 lnL(θ̂k) + k lnn; (2) ICOMP = −2 lnL(θ̂k) + s ln tr(I−1(θ̂k)/s) − ln |I−1(θ̂k)|, (3) where k is the number of parameters, n, sample size, θ̂k, the parameter estimates, I−1(θ̂k) = V ̂ ar(θ̂k), inverse of Fisher information matrix, and s = rk I−1(θk). Empirical versions of these criteria also use n ln
Cited by 0
V. Afraimovich, L. Glebsky · 2008
Abstract. We study some measures which are related to the notion of the ɛ-complexity. We prove that measure of ɛ-complexity defined on the base of the notion of ɛ-separability is equivalent to the dual measure that is defined through ɛ-nets. 1.
Cited by 0
Stein, D.L., Bohr, H. · Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU) · 1990
Cited by 0
Peter Wills (8442741), François G. Meyer (8442744) · Figshare · 2020
<p>The size (of the larger) graph is <i>n</i>; the number of edges is <i>m</i>. For the spectral decomposition, <i>k</i> denotes the number of principal eigenvalues we wish to find.</p>
Cited by 0Open access
P. Hrubeš, Stasys Jukna, Alexander S. Kulikov et al. · Theoretical Computer Science · 2010
Cited by 9
Michael Rice · Workshops in computing · 1992
Cited by 0
Stanislav Kolenikov · 2000
This document provides formulae (from [3]) on which the calculations by icomp are based. icomp calculates Akaike information criteria (AIC), Schwartz Bayesian information criteria (SBC, SBIC), and Bozdogan’s index of informational complexity (ICOMP). These criteria are used to select the “best” model compromising an adequate goodness of fit and a small number of parameters by adding a penalty for overparametrization to the lack of fit measure (estimate of the maximum likelihood or the residual sum of squares). The best model minimizes the criterion. The three informational measures differ in the penalty term, SBIC penalizing more severely for larger samples, and ICOMP accounting for covariance structure of a model (and, thus, for collinearity between the factors and dependence among the parameter estimates).
Cited by 0