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Journal articles on the topic 'Gradient Variable'

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1

Ye, Luotang, Yanmao Chen, and Qixian Liu. "Development of an Efficient Variable Step-Size Gradient Method Utilizing Variable Fractional Derivatives." Fractal and Fractional 7, no. 11 (2023): 789. http://dx.doi.org/10.3390/fractalfract7110789.

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The fractional gradient method has garnered significant attention from researchers. The common view regarding fractional-order gradient methods is that they have a faster convergence rate compared to classical gradient methods. However, through conducting theoretical convergence analysis, we have revealed that the maximum convergence rate of the fractional-order gradient method is the same as that of the classical gradient method. This discovery implies that the superiority of fractional gradients may not reside in achieving fast convergence rates compared to the classical gradient method. Bui
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2

Oth, A. "Viscosimétrie à gradient variable." Bulletin des Sociétés Chimiques Belges 64, no. 9-10 (2010): 593–99. http://dx.doi.org/10.1002/bscb.19550640909.

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Minervini, Pasquale, Luca Franceschi, and Mathias Niepert. "Adaptive Perturbation-Based Gradient Estimation for Discrete Latent Variable Models." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 8 (2023): 9200–9208. http://dx.doi.org/10.1609/aaai.v37i8.26103.

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The integration of discrete algorithmic components in deep learning architectures has numerous applications. Recently, Implicit Maximum Likelihood Estimation, a class of gradient estimators for discrete exponential family distributions, was proposed by combining implicit differentiation through perturbation with the path-wise gradient estimator. However, due to the finite difference approximation of the gradients, it is especially sensitive to the choice of the finite difference step size, which needs to be specified by the user. In this work, we present Adaptive IMLE (AIMLE), the first adapti
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4

Pritzker, Z., and A. Feuer. "Variable length stochastic gradient algorithm." IEEE Transactions on Signal Processing 39, no. 4 (1991): 997–1001. http://dx.doi.org/10.1109/78.80936.

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5

Lima, Valter C., Evan Zahn, Christine Houde, Jeffrey Smallhorn, Robert M. Freedom, and Lee N. Benson. "Non-invasive determination of the systolic peak-to-peak gradient in children with aortic stenosis: validation of a mathematical model." Cardiology in the Young 10, no. 2 (2000): 115–19. http://dx.doi.org/10.1017/s1047951100006569.

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AbstractDoppler derived systolic pressure gradients have become widely applied as noninvasively obtained estimates of the severity of aortic valvar stenosis. There is little correlation, however, between the Doppler derived peak instantaneous gradient and the peak-to-peak gradient obtained at catheterisation, the latter being the most applied variable to determine severity in children. The purpose of this study was to validate a mathematical model based on data from catheterisation which estimates the peak-to-peak gradient from variables which can be obtained by noninvasive means (Doppler deri
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Wright, Kristopher K., and Judith L. Li. "From continua to patches: examining stream community structure over large environmental gradients." Canadian Journal of Fisheries and Aquatic Sciences 59, no. 8 (2002): 1404–17. http://dx.doi.org/10.1139/f02-113.

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We present an approach that integrates a conceptual framework with multivariate ordination techniques and traditional parametric analyses to examine biotic and abiotic gradients in stream ecosystems. Ordinations were used to examine multivariate patterns along an environmental gradient, with individual variables used to interpret those patterns across spatial scales. The conceptual framework provides a consistent context to compare community distributions and consequently allows for hypothesis testing using ordinations. To illustrate the approach, we examined the physical template, fish and be
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7

Scheiner, Samuel M. "Variable Selection Along a Successional Gradient." Evolution 43, no. 3 (1989): 548. http://dx.doi.org/10.2307/2409058.

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8

Li, Gang, and Yuan Yao. "Manufacturable Gradient-Variable Cellular Structures Design." IOP Conference Series: Materials Science and Engineering 692 (November 27, 2019): 012010. http://dx.doi.org/10.1088/1757-899x/692/1/012010.

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9

Scheiner, Samuel M. "VARIABLE SELECTION ALONG A SUCCESSIONAL GRADIENT." Evolution 43, no. 3 (1989): 548–62. http://dx.doi.org/10.1111/j.1558-5646.1989.tb04251.x.

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10

HANKE, HAUKE. "HOMOGENIZATION IN GRADIENT PLASTICITY." Mathematical Models and Methods in Applied Sciences 21, no. 08 (2011): 1651–84. http://dx.doi.org/10.1142/s0218202511005520.

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This paper yields a two-scale homogenization result for a rate-independent elasto-plastic system. The presented model is a generalization of the classical model of linearized elasto-plasticity with hardening, which is extended by a gradient term of the plastic variables. The associated stored elastic energy density has periodically oscillating coefficients, where the period is scaled by ε > 0. The additional gradient term of the plastic variables z is contained in the elastic energy with a prefactor εγ(γ ≥ 0). We derive different limiting models for ε → 0 in dependence of γ. For γ > 1, t
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11

Lee. "Novel Variable Step-Size Gradient Adaptive Lattice Algorithm for Active Noise Control." Journal Of The Acoustical Society Of Korea 33, no. 5 (2014): 309. http://dx.doi.org/10.7776/ask.2014.33.5.309.

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12

Harkins, Kevin D., and Mark D. Does. "Efficient gradient waveform measurements with variable-prephasing." Journal of Magnetic Resonance 327 (June 2021): 106945. http://dx.doi.org/10.1016/j.jmr.2021.106945.

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13

Marteau, F., A. Ghaith, P. N'Gotta, et al. "Variable high gradient permanent magnet quadrupole (QUAPEVA)." Applied Physics Letters 111, no. 25 (2017): 253503. http://dx.doi.org/10.1063/1.4986856.

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14

So, C. F., S. C. Ng, and S. H. Leung. "Gradient based variable forgetting factor RLS algorithm." Signal Processing 83, no. 6 (2003): 1163–75. http://dx.doi.org/10.1016/s0165-1684(03)00037-9.

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15

Guo, Longyuan, Guoyun Zhang, and Jianhu Wu. "Variable Search Region Base on Disparity Gradient." Procedia Engineering 29 (2012): 2151–55. http://dx.doi.org/10.1016/j.proeng.2012.01.278.

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16

Yucel, Gozde, and Stephen Small. "Morphogens: Precise Outputs from a Variable Gradient." Current Biology 16, no. 1 (2006): R29—R31. http://dx.doi.org/10.1016/j.cub.2005.12.005.

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17

Kimmlingen, Ralph, Matthias Gebhardt, Johann Schuster, Martin Brand, Franz Schmitt, and Axel Haase. "Gradient system providing continuously variable field characteristics." Magnetic Resonance in Medicine 47, no. 4 (2002): 800–808. http://dx.doi.org/10.1002/mrm.10129.

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18

Kuo, Chung-Feng, and Chen-Yuan Kuo. "Improved Gradient-Type Algorithms for Zero Terminal Gradient Optimal Control Problems." Journal of Dynamic Systems, Measurement, and Control 109, no. 4 (1987): 355–62. http://dx.doi.org/10.1115/1.3143867.

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Difficulties often arise when we apply the gradient type algorithms employing penalty functions to optimal control problems with variable final time. There is another class of optimal control problems for which the necessary conditions for optimality require a zero gradient at the final time. This causes the gradient-type algorithms, in their standard forms, to become incapable of changing the terminal value of the control variable at each iteration and the rate of convergence is adversely affected. In this paper, we first apply a new transformation method developed by Polak [19] which transfo
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19

Hartono, Adimas Tristan Nagara, and Hindriyanto Dwi Purnomo. "Pengembangan Stochastic Gradient Descent dengan Penambahan Variabel Tetap." Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) 7, no. 3 (2023): 359–67. http://dx.doi.org/10.35870/jtik.v7i3.840.

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Stochastic Gradient Descent (SGD) is one of the commonly used optimizers in deep learning. Therefore, in this work, we modify stochastic gradient descent (SGD) by adding a fixed variable. We will then look at the differences between standard stochastic gradient descent (SGD) and stochastic gradient descent (SGD) with additional variables. The phases performed in this study were: (1) optimization analysis, (2) fix design, (3) fix implementation, (4) fix test, (5) reporting. The results of this study aim to show the additional impact of fixed variables on the performance of stochastic gradient d
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20

Kalantari, Robab, Khashayar Rahimi, and Saman Naderi Mezajin. "Multi-Fractional Gradient Descent: A Novel Approach to Gradient Descent for Robust Linear Regression." Engineering World 6 (October 14, 2024): 118–27. http://dx.doi.org/10.37394/232025.2024.6.12.

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Authors: This work introduces a novel gradient descent method by generalizing the fractional gradient descent (FGD) such that instead of the same fractional order for all variables, we assign different fractional orders to each variable depending on its characteristics and its relation to other variables. We name this method Multi-Fractional Gradient Descent (MFGD) and by using it in linear regression for minimizing loss function (residual sum of square) and apply it on four financial time series data and also tuning their hyperparameters, we can observe that unlike GD and FGD, MFGD is robust
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21

Kunes, Russell Z., Mingzhang Yin, Max Land, Doron Haviv, Dana Pe'er, and Simon Tavaré. "Gradient Estimation for Binary Latent Variables via Gradient Variance Clipping." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 7 (2023): 8405–12. http://dx.doi.org/10.1609/aaai.v37i7.26013.

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Gradient estimation is often necessary for fitting generative models with discrete latent variables, in contexts such as reinforcement learning and variational autoencoder (VAE) training. The DisARM estimator achieves state of the art gradient variance for Bernoulli latent variable models in many contexts. However, DisARM and other estimators have potentially exploding variance near the boundary of the parameter space, where solutions tend to lie. To ameliorate this issue, we propose a new gradient estimator bitflip-1 that is lower variance at the boundaries of the parameter space. As bitflip-
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22

Bellavia, Stefania, Nataša Krklec Jerinkić, and Greta Malaspina. "Subsampled Nonmonotone Spectral Gradient Methods." Communications in Applied and Industrial Mathematics 11, no. 1 (2020): 19–34. http://dx.doi.org/10.2478/caim-2020-0002.

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AbstractThis paper deals with subsampled spectral gradient methods for minimizing finite sums. Subsample function and gradient approximations are employed in order to reduce the overall computational cost of the classical spectral gradient methods. The global convergence is enforced by a nonmonotone line search procedure. Global convergence is proved provided that functions and gradients are approximated with increasing accuracy. R-linear convergence and worst-case iteration complexity is investigated in case of strongly convex objective function. Numerical results on well known binary classif
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23

Ván, Peter. "Thermodynamically consistent gradient elasticity with an internal variable." Theoretical and Applied Mechanics 47, no. 1 (2020): 1–17. http://dx.doi.org/10.2298/tam200204006v.

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24

Nagarajoo, Karthigesu. "Variable methods to estimate the ionospheric horizontal gradient." IOP Conference Series: Earth and Environmental Science 37 (June 2016): 012012. http://dx.doi.org/10.1088/1755-1315/37/1/012012.

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25

Vedernikov, R. A., A. V. Rogozin, and A. V. Gasnikov. "DECENTRALIZED CONDITIONAL GRADIENT METHOD ON TIME-VARIABLE GRAPHS." Программирование, no. 6 (November 1, 2023): 27–35. http://dx.doi.org/10.31857/s0132347423060080.

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In this paper, we consider a generalization of the decentralized Frank-Wulff algorithm for network time variables, study the convergence properties of the algorithm, and carry out the corresponding numerical experiments. The changing network is modeled as a deterministic or stochastic sequence of graphs.
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26

Cappiello, Achille, Giorgio Famiglini, Chiara Fiorucci, Filippo Mangani, Pierangela Palma, and Antonella Siviero. "Variable-Gradient Generator for Micro- and Nano-HPLC." Analytical Chemistry 75, no. 5 (2003): 1173–79. http://dx.doi.org/10.1021/ac026125z.

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27

Zhang, Yonggang, Ning Li, Jonathon A. Chambers, and Yanling Hao. "New Gradient-Based Variable Step Size LMS Algorithms." EURASIP Journal on Advances in Signal Processing 2008, no. 1 (2008): 529480. http://dx.doi.org/10.1155/2008/529480.

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28

Evans, Clare-Louise, Gareth A. Morris, and Adrian L. Davis. "A New Method for Variable Temperature Gradient Shimming." Journal of Magnetic Resonance 154, no. 2 (2002): 325–28. http://dx.doi.org/10.1006/jmre.2001.2497.

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29

Zhou, Xinlei, Jing Ge, Xiaoyu Yang, Qingbin Yang, Yiyu Feng, and Wei Feng. "Phase-change gradient composites for variable thermal management." Composites Communications 48 (June 2024): 101948. http://dx.doi.org/10.1016/j.coco.2024.101948.

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30

LIVESCU, D., and J. R. RISTORCELLI. "Buoyancy-driven variable-density turbulence." Journal of Fluid Mechanics 591 (October 30, 2007): 43–71. http://dx.doi.org/10.1017/s0022112007008270.

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Buoyancy-generated motions in an unstably stratified medium composed of two incompressible miscible fluids with different densities, as occurs in the variable-density Rayleigh–Taylor instability, are examined using direct numerical simulations. The non-equilibrium homogeneous buoyantly driven problem is proposed as a unit problem for variable density turbulence to study: (i) the nature of variable density turbulence, (ii) the transition to turbulence and the generation of turbulence by the conversion of potential to kinetic energy; (iii) the role of non-Boussinesq effects; and (iv) a parameter
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31

Gao, Yanbo, Chuankun Li, Shuai Li, Xun Cai, Mao Ye, and Hui Yuan. "Variable Rate Independently Recurrent Neural Network (IndRNN) for Action Recognition." Applied Sciences 12, no. 7 (2022): 3281. http://dx.doi.org/10.3390/app12073281.

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Recurrent neural networks (RNNs) have been widely used to solve sequence problems due to their capability of modeling temporal dependency. Despite the rich varieties of RNN models proposed in the literature, the problem of different sampling rates or performing speeds in sequence tasks has not been explicitly considered in the network and the corresponding training and testing processes. This paper addresses the problem of different sampling rates or performing speeds in the skeleton-based action recognition with RNNs. Specifically, the recently proposed independently recurrent neural network
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32

Ye, Xichen, Yifan Wu, Weizhong Zhang, Xiaoqiang Li, Yifan Chen, and Cheng Jin. "Optimized Gradient Clipping for Noisy Label Learning." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 9 (2025): 9463–71. https://doi.org/10.1609/aaai.v39i9.33025.

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Previous research has shown that constraining the gradient of loss function w.r.t. model-predicted probabilities can enhance the model robustness against noisy labels. These methods typically specify a fixed optimal threshold for gradient clipping through validation data to obtain the desired robustness against noise. However, this common practice overlooks the dynamic distribution of gradients from both clean and noisy-labeled samples at different stages of training, significantly limiting the model capability to adapt to the variable nature of gradients throughout the training process. To ad
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Callanan, Jesse, Revant Adlakha, Mohamed Mousa, and Mostafa Nouh. "Traveling wave thermoacoustic refrigeration with variable phase-coordinated boundary conditions." Journal of the Acoustical Society of America 154, no. 6 (2023): 3943–54. http://dx.doi.org/10.1121/10.0023954.

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Thermoacoustic refrigerators exploit the thermodynamic interaction between oscillating gas particles and a porous solid to generate a temperature gradient that provides a cooling effect. In this work, we present a resonator with dual enclosed driver end-caps and show that the temperature gradient across a ceramic thermoacoustic element placed in the cavity could be controlled by modifying the phase difference of the drivers, thus enabling precise control of the refrigeration capability via the temperature difference. Through deltaec simulation results, the response of the temperature gradient
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Chan, T.-H. Hubert, Hao Xie, and Mengshi Zhao. "Privacy Amplification by Iteration for ADMM with (Strongly) Convex Objective Functions." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 10 (2024): 11204–11. http://dx.doi.org/10.1609/aaai.v38i10.28998.

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We examine a private ADMM variant for (strongly) convex objectives which is a primal-dual iterative method. Each iteration has a user with a private function used to update the primal variable, masked by Gaussian noise for local privacy, without directly adding noise to the dual variable. Privacy amplification by iteration explores if noises from later iterations can enhance the privacy guarantee when releasing final variables after the last iteration. Cyffers et al. explored privacy amplification by iteration for the proximal ADMM variant, where a user's entire private function is accessed an
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Kozlowski, Tomasz, and Agata Ludynia. "Permeability Coefficient of Low Permeable Soils as a Single-Variable Function of Soil Parameter." Water 11, no. 12 (2019): 2500. http://dx.doi.org/10.3390/w11122500.

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Based on the results of experimental studies concerning the filtration coefficient, the Darcianity of the observed flows for eight cohesive soils at four hydraulic gradients was analyzed. It is observed that linear dependence of flow velocity on hydraulic gradient is an approximation only, and it is the worse the more cohesive a given soil is. Despite this, Darcy’s law can be a correct approximation of the empirical relationship between hydraulic gradient and the flow velocity, also in very cohesive soils. A statistical analysis was carried out to identify correlation between soil properties a
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36

O'Nolan, Daniel, Guanglong Huang, Gabrielle E. Kamm, et al. "A thermal-gradient approach to variable-temperature measurements resolved in space." Journal of Applied Crystallography 53, no. 3 (2020): 662–70. http://dx.doi.org/10.1107/s160057672000415x.

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Temperature is a ubiquitous environmental variable used to explore materials structure, properties and reactivity. This article reports a new paradigm for variable-temperature measurements that varies the temperature continuously across a sample such that temperature is measured as a function of sample position and not time. The gradient approach offers advantages over conventional variable-temperature studies, in which temperature is scanned during a series measurement, in that it improves the efficiency with which a series of temperatures can be probed and it allows the sample evolution at m
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37

Xing, Xiaoqing, and Yanping Chen. "Superconvergence of Mixed Methods for Optimal Control Problems Governed by Parabolic Equations." Advances in Applied Mathematics and Mechanics 3, no. 4 (2011): 401–19. http://dx.doi.org/10.4208/aamm.10-m1006.

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AbstractIn this paper, we investigate the superconvergence results for optimal control problems governed by parabolic equations with semidiscrete mixed finite element approximation. We use the lowest order mixed finite element spaces to discrete the state and costate variables while use piecewise constant function to discrete the control variable. Superconvergence estimates for both the state variable and its gradient variable are obtained.
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38

Thurston, Jeffrey B., and R. James Brown. "Automated source‐edge location with a new variable pass‐band horizontal‐gradient operator." GEOPHYSICS 59, no. 4 (1994): 546–54. http://dx.doi.org/10.1190/1.1443615.

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A new class of space‐domain convolution operators permits computation of the components of the horizontal gradient of gridded potential‐field data. These so‐called gradient‐component operators allow one to vary the passband and thus control the frequency content of the resulting horizontal‐gradient map. This facilitates computation of gradient maps that accommodate data of widely varying frequency content. Examination of the transfer functions of these operators suggests that this method of numerical differentiation is well suited to potential‐field data: in particular, the operators suppress
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39

Chen, Xiangyang, Jie Liu, and Yanbin Fan. "Hydrodynamics and Heat Transfer Characteristics of a Double Gradient Microchannel Porous Model: Fixed Gradients and Variable Gradients Structures." Advanced Theory and Simulations 3, no. 4 (2020): 1900238. http://dx.doi.org/10.1002/adts.201900238.

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40

Chang, Dick Mun, Hong Seng Sim, Yong Kheng Goh, Sing Yee Chua, and Wah June Leong. "Diagonal Variable Matrix Method in Solving Inverse Problem in Image Processing." ITM Web of Conferences 67 (2024): 01039. http://dx.doi.org/10.1051/itmconf/20246701039.

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In this paper, we introduce a new gradient method called the Diagonal Variable Matrix method. Our proposed method is aimed to minimize Hk+1 over the log-determinant norm subject to weak secant relation. The derived diagonal matrix Hk+1 is the approximation of the inverse Hessian matrix, which enables the calculation of the search direction, dk = −Hk+1gk, where gk denotes the gradient of the objective function. The proposed method is coupled with the backtracking Armijo line search. The proposed method is specifically designed to reduce the number of iterations and training duration, particular
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41

Maksimov, A. B., I. P. Shevchenko, and I. S. Erohina. "Sheet metal with variable mechanical properties over its thickness." Izvestiya. Ferrous Metallurgy 62, no. 8 (2019): 587–93. http://dx.doi.org/10.17073/0368-0797-2019-8-587-593.

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The influence of one-sided accelerated cooling of A32 plate shipbuilding steel with thickness of 10·10–3 m on structure and mechanical properties was investigated. As a result of such cooling, continuous spectrum of microstructures from ferrite-bainite on the rapidly cooled surface to ferrite-perlite on the opposite surface is formed along the billet thickness. Therefore, over the billet thickness strength properties are reduced from rapidly cooled surface to the opposite one. Thus, the gradient of strength characteristics (hardness, yield strength and rupture strength) along the billet thickn
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42

Chakraborty, Nilanjan, and Cesar Dopazo. "Timescales Associated with the Evolution of Reactive Scalar Gradient in Premixed Turbulent Combustion: A Direct Numerical Simulation Analysis." Fire 7, no. 3 (2024): 73. http://dx.doi.org/10.3390/fire7030073.

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The fractional change in the reaction progress variable gradient depends on the flow normal straining within the flame and also upon the corresponding normal gradients of the reaction rate and its molecular diffusion transport. The statistical behaviours of the normal strain rate and the contributions arising from the normal gradients of the reaction rate and molecular diffusion rate within the flame were analysed by means of a Direct Numerical Simulation (DNS) database of statistically planar turbulent premixed flames ranging from the wrinkled/corrugated flamelets regime to the thin reaction
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43

Kaltenbach, A., and M. Růžička. "Variable exponent Bochner–Lebesgue spaces with symmetric gradient structure." Journal of Mathematical Analysis and Applications 503, no. 2 (2021): 125355. http://dx.doi.org/10.1016/j.jmaa.2021.125355.

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44

Zhang, Y., and J. A. Chambers. "Variable tap-length natural gradient blind deconvolution/equalisation algorithm." Electronics Letters 43, no. 14 (2007): 781. http://dx.doi.org/10.1049/el:20071346.

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45

Carlson, J. R., N. Duquesne, C. L. Rumsey, and T. B. Gatski. "Computation of turbulent wake flows in variable pressure gradient." Computers & Fluids 30, no. 2 (2001): 161–87. http://dx.doi.org/10.1016/s0045-7930(00)00007-4.

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46

Knyazev, Andrew V., and Ilya Lashuk. "Steepest Descent and Conjugate Gradient Methods with Variable Preconditioning." SIAM Journal on Matrix Analysis and Applications 29, no. 4 (2008): 1267–80. http://dx.doi.org/10.1137/060675290.

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47

Voyiadjis, George Z., and Rashid K. Abu Al-Rub. "Gradient plasticity theory with a variable length scale parameter." International Journal of Solids and Structures 42, no. 14 (2005): 3998–4029. http://dx.doi.org/10.1016/j.ijsolstr.2004.12.010.

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48

Shen, Jie, Jie Xu, and Jiang Yang. "The scalar auxiliary variable (SAV) approach for gradient flows." Journal of Computational Physics 353 (January 2018): 407–16. http://dx.doi.org/10.1016/j.jcp.2017.10.021.

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Jakovetic, Dusan, Dragana Bajovic, Natasa Krejic, and Natasa Krklec Jerinkic. "Distributed Gradient Methods with Variable Number of Working Nodes." IEEE Transactions on Signal Processing 64, no. 15 (2016): 4080–95. http://dx.doi.org/10.1109/tsp.2016.2560133.

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50

Castillo-Garza, R., and U. Mohideen. "Variable-temperature device for precision Casimir-force-gradient measurement." Review of Scientific Instruments 84, no. 2 (2013): 025110. http://dx.doi.org/10.1063/1.4790195.

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