## What is/are Numerical Comparison?

Numerical Comparison - Numerical comparisons with other techniques show that this methodology can be useful in practical statistical modeling and analysis helping to researchers and managers in making meaningful decisions.^{[1]}Numerical comparisons are made among the classical, i.

^{[2]}Robustness of this estimation scheme is also demonstrated through a numerical comparison against some state-of-the-art nonlinear attitude estimation schemes.

^{[3]}Numerical comparisons demonstrate that the exponential integrators can obtain high accuracy and efficiency for solving large-scale systems of stiff Riccati differential equations.

^{[4]}A numerical comparison of the blood flow in a bioprosthesis connected to the main vascular bed using the «end-to-end» and «end-to-side» methods (bypass) taking into account the heart rate and blood pressure was performed.

^{[5]}The study found that through the numerical comparison of the experiment combined with the simulation analysis of Cog Tool and the target interaction experiment, the intrinsic cognitive law of the user in the interaction process was extracted.

^{[6]}The relations between the left and right states of the shock wave, rarefaction wave, and contact discontinuity are also discussed, so that the exact solution of the 1D Riemann problem could be derived and used for the numerical comparisons.

^{[7]}The numerical comparison is first carried out using real-world face-to-face dynamical networks collected in a high school, following by sequential multi-layer networks, generated relying on the Barabasi-Albert model emulating the department of Computing at Imperial College London in the UK as an example.

^{[8]}Numerical comparisons allow us to conclude that the proposed CA-system, which is based on simple arithmetic operations, achieves the same results that a reference model based on the well known d'Alembert one-dimensional discrete wave solution, with a Mean Square Error (MSE) in the order of 10−13.

^{[9]}Finally, to assess vibration isolation effectiveness and local stability behavior of trapezoidal CPVAs, a numerical comparison with the tautochrone parallel pendulum and results of multibody dynamics simulation are discussed.

^{[10]}Numerical comparisons are also provided.

^{[11]}Furthermore, differences between original and reconstructed animation are minimal, as evidenced by visual and numerical comparisons.

^{[12]}Numerical comparisons have made to show the performance of the proposed methods, as shown in the illustrative examples.

^{[13]}The effectiveness of the proposed L3Fnet is supported by both visual and numerical comparisons on this dataset.

^{[14]}Numerical comparisons with the two best-known methods demonstrate the efficiency of our method.

^{[15]}Finally, a numerical comparison and a practical examples are provided to demonstrate the validity of the developed finite-time control algorithm.

^{[16]}Through analytical and numerical comparisons, much more bifurcations and dynamical behaviors can be obtained and preserved by using the NSFD scheme, in which the integral step size can be chosen larger relatively due to its better stability and convergence than those in the forward Euler scheme.

^{[17]}Arguably the first outflow of probabilistic thinking in medicine was to evaluate the effects of smallpox inoculation in 18th-century England: numerical comparisons of death rates of inoculated and uninoculated groups were made by mathematically inclined clinicians such as James Jurin, Secretary of the Royal Society.

^{[18]}Through a numerical comparison and multi-criteria decision-making problems, we show that the proposed distance measure is reasonable and effective.

^{[19]}The effectiveness of our method is evaluated with numerical comparisons to existing techniques and through examples of ensemble and multivariate datasets.

^{[20]}An Eemian age is supported by the numerical comparison with the Beerenmosli (MIS 5e) and Meikirch 3 (MIS 7a) reference records.

^{[21]}Finally, a numerical comparison is given to verify validity and effectiveness of the proposed approach.

^{[22]}A numerical comparison is carried out with a total of 546 data sets.

^{[23]}: A numerical comparison of approximations to the Stokes equations used in ice sheet and glacier modeling, Journal of Geophysical Research: Earth Surface, 109, https://doi.

^{[24]}Numerical comparisons show that the logarithmic-barrier path-following method significantly outperforms the convex-quadratic-penalty path-following method.

^{[25]}We also perform an analytical and numerical comparison of LP NNLL + NLP LL resummation in softcollinear effective theory and direct QCD, where we achieve excellent analytical and numerical agreement once the NLP LL terms are included in both formalisms.

^{[26]}Numerical comparison between the models is generally good.

^{[27]}Thus, this paper presents an analysis on numerical comparison between common method and the other methods.

^{[28]}Under other situations, numerical comparisons with CPLEX 12.

^{[29]}A numerical comparison with the published works is conducted to verify the accuracy of the present study.

^{[30]}We frame linear factor models for asset pricing in a machine learning context and consider a numerical comparison of their performance against ordinary least squares linear regression over a dataset of anomaly portfolios.

^{[31]}Finally, numerical comparisons are presented to validate the routines.

^{[32]}Numerical comparisons are provided to demonstrate the effectiveness of the proposed strategies.

^{[33]}On test examples simulating the spatial inhomogeneity of the radiation field, a numerical comparison of the proposed method with some diffusion-type methods on the initial gas-dynamic and adapted computational meshes is carried out.

^{[34]}Numerical comparisons are performed to verify the convergence of the modified RPIM and the accuracy of iteration method in postbuckling path analysis.

^{[35]}Visual and numerical comparison of the concrete strength values obtained from isotherms gave an understanding of the degree of influence of sample heating on strength development.

^{[36]}Finally, a comprehensive analytical and numerical comparison of three illustrative examples is conducted to show that the proposed results are less conservative than the existing work.

^{[37]}We provide some numerical comparisons to illustrate our results.

^{[38]}Finally, numerical comparisons with the results in literature validate the effectiveness and accuracy of the proposed method.

^{[39]}The numerical comparison is first carried out using real-world face-to-face dynamic networks collected in a high school, followed by sequential multi-layer networks generated relying on the Barabasi-Albert model emulating large-scale social networks with several communities.

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## Extensive Numerical Comparison

By extensive numerical comparisons, we show that our algorithms outperform existing DAG learning methods in identifying causal graphical structures.^{[1]}Extensive numerical comparisons on several test networks illustrate that the proposed method outperforms other Riemannian optimization methods (Gradient Descent, Newton's), and achieves comparable performance with the traditional Newton-Raphson method, albeit besting it by a guarantee to convergence.

^{[2]}Through extensive numerical comparisons, we show that ARCS outperformed existing methods by a substantial margin, demonstrating its great advantage in structure learning of Bayesian networks from both observational and experimental data.

^{[3]}Extensive numerical comparisons of one- and two-dimensional (2D) cases are reported to verify the effectiveness of the proposed algorithm and the correctness of the theoretical analysis.

^{[4]}We validate the performance of all our schemes via extensive numerical comparisons.

^{[5]}Extensive numerical comparisons are made with 3D models built by structured mesh.

^{[6]}Through extensive numerical comparisons, we demonstrate that our method outperforms other competitors with big margins for finite samples, including oracle methods built upon the true sparsity of the underlying model.

^{[7]}Extensive numerical comparisons using real and synthetic data sets demonstrate that the proposed algorithm provides state-of-the-art performance in terms of computational accuracy and cpu time.

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## Detailed Numerical Comparison

The detailed numerical comparison for spectral line shapes in the frame of both scalar and vector perturbation additions with and without jumping frequency field dependence for the linear and quadratic Stark effects is presented.^{[1]}Here we carry out a detailed numerical comparison of several FP approaches with the exact scattering kernel solution for a range of test problems assuming isotropic media and thermal electrons at various temperatures.

^{[2]}In the paper, detailed numerical comparisons are considered for the mixed-mode failure characteristics of different composite adhesively bonded joints with brittle/quasi-brittle adhesive using following failure models: cohesive zone model (CZM), virtual crack closure technology (VCCT), mixed-mode continuum damage model (MCDM) and finite fracture mechanics (FFM).

^{[3]}Detailed numerical comparison with existing methods based on projection to the constrained space indicates that the modified SAV schemes are more efficient, particularly for the multi-component BECs.

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## Comprehensive Numerical Comparison

In this work, we provide a comprehensive numerical comparison of microring/disk modulators between the conventional, lateral pn junction and our vertical pn junction designs.^{[1]}A comprehensive numerical comparison and statistical analysis are carried out to investigate the performance of the proposed approaches.

^{[2]}42 045205) made by Ferreira D R, we carry out a comprehensive numerical comparison of the three known analytic expressions for the electrostatic potential of a uniformly charged triangle sheet.

^{[3]}We also conduct a comprehensive numerical comparison of the effects of different modulating factors, including the price and the proportion and variation of surviving quantity, in these two settings.

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## Provide Numerical Comparison

We also provide numerical comparisons in synthetic simulations and the real PROSAIL model, a popular RTM that combines land vegetation leaf and canopy modeling.^{[1]}Moreover, it provides numerical comparisons for unconstrained minimization using benchmark functions of the unified algorithm with certain heuristic intelligent optimization algorithms.

^{[2]}We provide numerical comparisons with a new benchmark formulation, the so-called converted dc (CDC) power flow model, using Monte Carlo simulations for two different IEEE case studies.

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## Present Numerical Comparison

The aim of this contribution is to present numerical comparisons of model-order reduction methods for geometrically nonlinear structures in the general framework of finite element (FE) procedures.^{[1]}In this talk we present the methodology, apply it to the estimation of canonical ensembles and present numerical comparisons of the standard particle filter estimates with those of the homotopy data assimilation.

^{[2]}Finally, we present numerical comparisons of our data-driven minimax regret model with data-driven models based on the Hurwicz criterion and with a minimax regret model based on partial statistical information on moments.

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## Computerized Numerical Comparison

We analyzed data from 397 adults who performed a computerized numerical comparison task, a computerized numerical order verification task (i.^{[1]}We analyzed data from 397 adults who performed a computerized numerical comparison task, a computerized numerical order verification task (i.

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## Several Numerical Comparison

To illustrate the performance of this family, several numerical comparisons are made with other third and higher order methods.^{[1]}Several numerical comparisons are carried out to verify a higher degree of accuracy based on the obtained scheme.

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## numerical comparison result

Several confirmations were performed for analytical and numerical comparison results.^{[1]}Theoretical analysis and numerical comparison results further show that the AFT-ZNN model has better performance than the TT-ZNN model.

^{[2]}Furthermore, Jacobi neural network method has higher accuracy compared with the approximate analytical methods, the numerical comparison results further show the feasibility and effectiveness of the proposed method for solving the DAEs.

^{[3]}Visual and numerical comparison results demonstrate the effectiveness of our RALSR for data representation and classification.

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## numerical comparison task

We analyzed data from 397 adults who performed a computerized numerical comparison task, a computerized numerical order verification task (i.^{[1]}We aimed to compare the influence of sex in functional brain connectivity and behavioral measures in a numerical comparison task.

^{[2]}We analyzed data from 397 adults who performed a computerized numerical comparison task, a computerized numerical order verification task (i.

^{[3]}Adapting a numerical comparison task to a negative priming paradigm, we aimed to provide new evidence that inhibitory control processes are involved in numerical comparison.

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## numerical comparison show

Numerical comparison shows that the new method is remarkably effective for solving systems of nonlinear equations.^{[1]}Numerical comparison shows the effectiveness of the proposed methods.

^{[2]}The numerical comparison shows its applicability and accuracy in simulating the turbulent atmospheric boundary layer (ABL) flows, which satisfy the requirements of the fluctuating wind characteristics, such as the wind speed spectrum and the spatial correlation, and successfully achieve the equilibrium status for the numerical simulation of the ABL flow.

^{[3]}The numerical comparison shows that the proposed chart prevails over the existing standard MCV chart for detecting small and moderate upward and downward MCV shifts.

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## numerical comparison experiment

In parts of numerical comparison experiments, it is shown that the PC-CVZNN model possesses faster convergence rate than fixed-parameter CVZNN models and other analogy neural networks with parameter-changing function, when applied to finding the solution of CV-LME-TVC.^{[1]}Numerical comparison experiments are illustrated to prove the validity of the conjunction of possibility measures under intuitionistic evidence sets.

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