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Journal articles on the topic 'Gradient-based model'

Author: Grafiati

Published: 17 May 2025

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1

Burrows, Wesley, and John Doherty. "Gradient-based model calibration with proxy-model assistance." Journal of Hydrology 533 (February 2016): 114–27. http://dx.doi.org/10.1016/j.jhydrol.2015.11.033.

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2

D'Oro, Pierluca, Alberto Maria Metelli, Andrea Tirinzoni, Matteo Papini, and Marcello Restelli. "Gradient-Aware Model-Based Policy Search." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 3801–8. http://dx.doi.org/10.1609/aaai.v34i04.5791.

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Traditional model-based reinforcement learning approaches learn a model of the environment dynamics without explicitly considering how it will be used by the agent. In the presence of misspecified model classes, this can lead to poor estimates, as some relevant available information is ignored. In this paper, we introduce a novel model-based policy search approach that exploits the knowledge of the current agent policy to learn an approximate transition model, focusing on the portions of the environment that are most relevant for policy improvement. We leverage a weighting scheme, derived from the minimization of the error on the model-based policy gradient estimator, in order to define a suitable objective function that is optimized for learning the approximate transition model. Then, we integrate this procedure into a batch policy improvement algorithm, named Gradient-Aware Model-based Policy Search (GAMPS), which iteratively learns a transition model and uses it, together with the collected trajectories, to compute the new policy parameters. Finally, we empirically validate GAMPS on benchmark domains analyzing and discussing its properties.

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Estrin, Y., B. Sluys, Y. Brechet, and A. Molinari. "A dislocation based gradient plasticity model." Le Journal de Physique IV 08, PR8 (November 1998): Pr8–135—Pr8–141. http://dx.doi.org/10.1051/jp4:1998817.

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4

Wallin, Mathias, and Matti Ristinmaa. "Deformation gradient based kinematic hardening model." International Journal of Plasticity 21, no. 10 (October 2005): 2025–50. http://dx.doi.org/10.1016/j.ijplas.2005.01.007.

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5

Kaleta, Małgorzata P., Remus G. Hanea, Arnold W. Heemink, and Jan-Dirk Jansen. "Model-reduced gradient-based history matching." Computational Geosciences 15, no. 1 (August 5, 2010): 135–53. http://dx.doi.org/10.1007/s10596-010-9203-5.

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Brinckmann, Steffen, Thomas Siegmund, and Yonggang Huang. "A dislocation density based strain gradient model." International Journal of Plasticity 22, no. 9 (September 2006): 1784–97. http://dx.doi.org/10.1016/j.ijplas.2006.01.005.

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7

Liu, Pengfei. "Pansharpening with transform-based gradient transferring model." IET Image Processing 13, no. 13 (November 14, 2019): 2614–22. http://dx.doi.org/10.1049/iet-ipr.2018.6080.

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Bengio, Yoshua. "Gradient-Based Optimization of Hyperparameters." Neural Computation 12, no. 8 (August 1, 2000): 1889–900. http://dx.doi.org/10.1162/089976600300015187.

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Many machine learning algorithms can be formulated as the minimization of a training criterion that involves a hyperparameter. This hyperparameter is usually chosen by trial and error with a model selection criterion. In this article we present a methodology to optimize several hyper-parameters, based on the computation of the gradient of a model selection criterion with respect to the hyperparameters. In the case of a quadratic training criterion, the gradient of the selection criterion with respect to the hyperparameters is efficiently computed by backpropagating through a Cholesky decomposition. In the more general case, we show that the implicit function theorem can be used to derive a formula for the hyper-parameter gradient involving second derivatives of the training criterion.

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9

Xu, He Long, Jun Xiao, and Yu Xin Zhang. "Dynamical Model Updating Based on Gradient Regularization Method." Applied Mechanics and Materials 351-352 (August 2013): 118–21. http://dx.doi.org/10.4028/www.scientific.net/amm.351-352.118.

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Modulus of elasticity is an important input parameter in all kinds of structural analyses. The mathematical model used to identify the structural elastic modulus with measured Frequencies and mode shapes at several points is thusly built up in this paper, and then Gradient-Regularization method, an inverse problem solution method, is employed to solve the problem. General finite element program is compiled, and numerical examples have proved that the method of this thesis is efficient. The issues such as the choice of model error and the choice of measuring points are discussed as well.

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Bahmani, Sohail, Petros T. Boufounos, and Bhiksha Raj. "Learning Model-Based Sparsity via Projected Gradient Descent." IEEE Transactions on Information Theory 62, no. 4 (April 2016): 2092–99. http://dx.doi.org/10.1109/tit.2016.2515078.

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Jirásek, Milan, Ondřej Rokoš, and Jan Zeman. "Localization analysis of variationally based gradient plasticity model." International Journal of Solids and Structures 50, no. 1 (January 2013): 256–69. http://dx.doi.org/10.1016/j.ijsolstr.2012.09.022.

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12

Sadek, Rida, Gabriele Facciolo, Pablo Arias, and Vicent Caselles. "A Variational Model for Gradient-Based Video Editing." International Journal of Computer Vision 103, no. 1 (December 4, 2012): 127–62. http://dx.doi.org/10.1007/s11263-012-0597-5.

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13

La Cava, William G., and Kourosh Danai. "Gradient-based adaptation of continuous dynamic model structures." International Journal of Systems Science 47, no. 1 (August 3, 2015): 249–63. http://dx.doi.org/10.1080/00207721.2015.1069905.

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14

Zhao, Junfeng, Shenjie Zhou, Binglei Wang, and Xiping Wang. "Nonlinear microbeam model based on strain gradient theory." Applied Mathematical Modelling 36, no. 6 (June 2012): 2674–86. http://dx.doi.org/10.1016/j.apm.2011.09.051.

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15

Halil, Isam H., Issam A. R. Moghrabi, Ahmed A. Fawze, Basim A. Hassan, and Hisham M. Khudhur. "A Quadratic Model based Conjugate Gradient Optimization Method." WSEAS TRANSACTIONS ON MATHEMATICS 22 (December 6, 2023): 925–30. http://dx.doi.org/10.37394/23206.2023.22.101.

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In this paper, we introduce a nonlinear scaled conjugate gradient method, operating on the premise of a descent and conjugacy relationship. The proposed algorithm employs a conjugacy parameter that is determined to ensure that the method generates conjugate directions. It also utilizes a parameter that scales the gradient to enhance the convergence behavior of the method. The derived method not only exhibits the crucial feature of global convergence but also maintains the generation of descent directions. The efficiency of the method is established through numerical tests conducted on a variety of high-dimensional nonlinear test functions. The obtained results attest to the improved behavior of the derived algorithm and support the presented theory.

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Lachenmaier, Nicolas, Daniel Baumgärtner, Heinz-Peter Schiffer, and Johannes Kech. "Gradient-Free and Gradient-Based Optimization of a Radial Turbine." International Journal of Turbomachinery, Propulsion and Power 5, no. 3 (July 6, 2020): 14. http://dx.doi.org/10.3390/ijtpp5030014.

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A turbocharger’s radial turbine has a strong impact on the fuel consumption and transient response of internal combustion engines. This paper summarizes the efforts to design a new radial turbine aiming at high efficiency and low inertia by applying two different optimization techniques to a parametrized CAD model. The first workflow wraps 3D fluid and solid simulations within a meta-model assisted genetic algorithm to find an efficient turbine subjected to several constraints. In the next step, the chosen turbine is re-parametrized and fed into the second workflow which makes use of a gradient projection algorithm to further fine-tune the design. This requires the computation of gradients with respect to the CAD parametrization, which is done by calculating and combining surface sensitivities and design velocities. Both methods are applied successfully, i.e., the first delivers a well-performing turbine, which, by the second method, is further improved by 0.34% in efficiency.

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17

Ma, Wen Xu, and Ying Guang Fang. "Gradient of Soil Constitutive Model." Advanced Materials Research 168-170 (December 2010): 1126–29. http://dx.doi.org/10.4028/www.scientific.net/amr.168-170.1126.

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For the soil is a very complex natural material, significant strain gradient effect exist in soil analysis. Based on the "gradient" phenomenon, we add the plastic strain gradient hardening item into the traditional Cambridge yield surface. By using the consistency conditions and associated flow rule, we get the explicit expression of plastic strain gradient stiffness matrix. And the finite element method of plastic strain gradient is also shown in this article. Plastic strain gradient is actually a phenomenological non-local model containing microstructure information of the material. It may overcome the difficulties in simulating the gradient phenomenon by traditional mechanical model.

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18

Pouriayevali, Habib, and Bai-Xiang Xu. "A study of gradient strengthening based on a finite-deformation gradient crystal-plasticity model." Continuum Mechanics and Thermodynamics 29, no. 6 (July 22, 2017): 1389–412. http://dx.doi.org/10.1007/s00161-017-0589-3.

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Dai, Xiu Yue, Qun Wang, and Yi Fang Tang. "P2P VoD Content Distribution Strategy Based on Gradient Model." Advanced Materials Research 694-697 (May 2013): 2265–69. http://dx.doi.org/10.4028/www.scientific.net/amr.694-697.2265.

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The characteristics of P2P VoD (Video on Demand) system, such as low cost, great scalability and high quality of service, make it become the research hotspots and development trends of current network applications, while content distribution policy is one of its key technologies. To solve the problems of data scheduling strategy priority and node performance discrepancy in content distribution, the influences on scheduling policies of network performance information, the degree of content scarcity and playback emergency were studied. Based on the investigation, the gradient model based content distribution strategy is proposed. Distributed gradient model based strategy, sender startup strategy and receiver startup strategy were introduced to make each peer merely interact with parts of peers to reduce the collisions caused by the load balance. The experimental results show that gradient model based content distribution strategy improves the delivery speed and reduces the network load.

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20

Cheah, Yuat Hoong, and Su Hoe Yeak. "The fitting of roundabout model with gradient-based minimization." Journal of Physics: Conference Series 2609, no. 1 (October 1, 2023): 012003. http://dx.doi.org/10.1088/1742-6596/2609/1/012003.

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Abstract The objective of this research is to create an appropriate roundabout model for all countries. To date, the four-arm roundabout macroscopic model has been created. In order to ensure the feasibility and suitability of the model for all countries, the fitting process must be implemented since the speed of vehicles varies in each country. Thus, the parameter estimation on the rate of exiting roundabout is to be determined because the mean speed of vehicles is related to the rate of exiting the roundabout. In the minimization process, we have proposed an efficient and reliable framework as it includes the calculation of gradients used in minimization so called the user supplied-gradient minimization, as compared to non-user supplied-gradient minimization. The including of the calculation of gradient is to produce more accurate results by the built-in MATLAB minimization routine for parameter fitting. In this research, five pseudo experiments with numerous parameters are carried out. The rate of exiting the roundabout is set initially in order to compute the Total Travel Time and Total Waiting Time. The simulation showed a highly converged and accurate solution by the user supplied-gradient minimization. Lastly, this parameter estimation can be implemented that will enable the roundabout model to be applied worldwide if there is actual data for Total Travel Time and Total Waiting Time.

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21

Shishvan, Siamak S., Saeid Assadpour-asl, and Emilio Martínez-Pañeda. "A mechanism-based gradient damage model for metallic fracture." Engineering Fracture Mechanics 255 (October 2021): 107927. http://dx.doi.org/10.1016/j.engfracmech.2021.107927.

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22

Badnava, Hojjat, Mohammad Mashayekhi, and Mahmoud Kadkhodaei. "An anisotropic gradient damage model based on microplane theory." International Journal of Damage Mechanics 25, no. 3 (May 8, 2015): 336–57. http://dx.doi.org/10.1177/1056789515586072.

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23

Kim, Hyunjoong, and Paul C. Bressloff. "Impulsive signaling model of cytoneme-based morphogen gradient formation." Physical Biology 16, no. 5 (July 22, 2019): 056005. http://dx.doi.org/10.1088/1478-3975/ab2c5a.

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24

Lu, Mengda. "Insurance Fraud Prediction Model Based on eXtreme Gradient Boosting." Journal of Computing and Electronic Information Management 15, no. 3 (December 26, 2024): 53–57. https://doi.org/10.54097/rd1m7n64.

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As the volume of insurance transactions continues to increase, combating insurance fraud has become increasingly important. One of the key challenges is how to predict fraud based on high-dimensional data. In the current research landscape, deep learning methods that perform well often require large amounts of data, but their training and deployment also pose significant computational challenges. This paper, based on the Alibaba Tianchi dataset, explores the feasibility of constructing a low-cost prediction system through feature engineering and parameter tuning. Additionally, this paper verifies the robustness of the eXtreme Gradient Boosting (XGBoost) model in handling high-dimensional sparse features and imbalanced data.

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25

Geers, M. G. D., R. de Borst, W. A. M. Brekelmans, and R. H. J. Peerlings. "Strain-based transient-gradient damage model for failure analyses." Computer Methods in Applied Mechanics and Engineering 160, no. 1-2 (July 1998): 133–53. http://dx.doi.org/10.1016/s0045-7825(98)80011-x.

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26

Asghari, M., SA Momeni, and R. Vatankhah. "The second strain gradient theory-based Timoshenko beam model." Journal of Vibration and Control 23, no. 13 (October 20, 2015): 2155–66. http://dx.doi.org/10.1177/1077546315611822.

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27

Maani Miandoab, Ehsan, Aghil Yousefi-Koma, and Hossein Nejat Pishkenari. "Poly silicon nanobeam model based on strain gradient theory." Mechanics Research Communications 62 (December 2014): 83–88. http://dx.doi.org/10.1016/j.mechrescom.2014.09.001.

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28

Dai, Xiaoying, Qiao Wang, and Aihui Zhou. "Gradient Flow Based Kohn--Sham Density Functional Theory Model." Multiscale Modeling & Simulation 18, no. 4 (January 2020): 1621–63. http://dx.doi.org/10.1137/19m1276170.

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29

毕, 小然. "Personalized Recommendation Model Based on Mini-Batch Gradient Descent." Computer Science and Application 09, no. 04 (2019): 695–702. http://dx.doi.org/10.12677/csa.2019.94079.

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30

Krapu, Christopher, Mark Borsuk, and Mukesh Kumar. "Gradient‐Based Inverse Estimation for a Rainfall‐Runoff Model." Water Resources Research 55, no. 8 (August 2019): 6625–39. http://dx.doi.org/10.1029/2018wr024461.

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31

Han, Chang, Nong Sang, Hongyan Zhang, and Liangpei Zhang. "Gradient transferred pansharpening method based on cosparse analysis model." Journal of Applied Remote Sensing 11, no. 2 (May 11, 2017): 025009. http://dx.doi.org/10.1117/1.jrs.11.025009.

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32

Li, Xiu-Ying, and Jian Wang. "Improved Recursive-gradient-based Model-free Adaptive Control Algorithm." International Journal of Control, Automation and Systems 20, no. 11 (November 2022): 3512–23. http://dx.doi.org/10.1007/s12555-021-0290-y.

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33

Tabesh, Majid, James Boyd, and Dimitris Lagoudas. "A Gradient-Based Constitutive Model for Shape Memory Alloys." Shape Memory and Superelasticity 3, no. 2 (March 22, 2017): 84–108. http://dx.doi.org/10.1007/s40830-017-0100-9.

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34

Chemingui, Yassine, Aryan Deshwal, Trong Nghia Hoang, and Janardhan Rao Doppa. "Offline Model-Based Optimization via Policy-Guided Gradient Search." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 10 (March 24, 2024): 11230–39. http://dx.doi.org/10.1609/aaai.v38i10.29001.

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Offline optimization is an emerging problem in many experimental engineering domains including protein, drug or aircraft design, where online experimentation to collect evaluation data is too expensive or dangerous. To avoid that, one has to optimize an unknown function given only its offline evaluation at a fixed set of inputs. A naive solution to this problem is to learn a surrogate model of the unknown function and optimize this surrogate instead. However, such a naive optimizer is prone to erroneous overestimation of the surrogate (possibly due to over-fitting on a biased sample of function evaluation) on inputs outside the offline dataset. Prior approaches addressing this challenge have primarily focused on learning robust surrogate models. However, their search strategies are derived from the surrogate model rather than the actual offline data. To fill this important gap, we introduce a new learning-to-search perspective for offline optimization by reformulating it as an offline reinforcement learning problem. Our proposed policy-guided gradient search approach explicitly learns the best policy for a given surrogate model created from the offline data. Our empirical results on multiple benchmarks demonstrate that the learned optimization policy can be combined with existing offline surrogates to significantly improve the optimization performance.

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Nur Idalisa, Nurul Hafawati Fadhilah, Norhaslinda Zullpakkal, Mohd Rivaie, Wan Rosanisah Wan Mohd, Imza Fakhri, and Ibrahim Mohammed Sulaiman. "Conjugate Gradient MATLAB GUI using AHP-Based Usability Model." Journal of Advanced Research in Applied Sciences and Engineering Technology 58, no. 1 (October 9, 2024): 1–12. https://doi.org/10.37934/araset.58.1.112.

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The Conjugate Gradient (CG) method is a proficient numerical technique for solving Unconstrained Optimisation (UO) problems. The greatest challenge of the CG algorithm is the complexity and time-consuming nature of data collection, especially when dealing with complex or extensive problems. Hence, a suitable interface is required to overcome this drawback. One implementation of such an interface is a graphical user interface (GUI), which can offer a user-friendly means of inputting parameters and displaying results. The GUI simplifies the data collection process, enhancing efficiency and speeding up its application in CG method research. This paper utilised MATLAB application designer to construct a GUI using an Analytic Hierarchy Process (AHP)-based evaluation method as a guideline. The integration of AHP helped to optimise the GUI design in terms of usability and effectiveness, thereby ensuring that the final product meets essential criteria. The AHP analysis revealed that accuracy, task completion time, response time, consistency, completeness, and ease of use are the five most important criteria for assessing GUI usability. The new CG-MATLAB GUI has been noted to meet the essential usability criteria.

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36

Gamini, Sridevi, Vishnu Vardhan Gudla, and Ch Hima Bindu. "Fractional-order Diffusion based Image Denoising Model." International Journal of Electrical and Electronics Research 10, no. 4 (December 30, 2022): 837–42. http://dx.doi.org/10.37391/ijeer.100413.

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Edge indicating operators such as gradient, mean curvature, and Gauss curvature-based image noise removal algorithms are incapable of classifying edges, ramps, and flat areas adequately. These operators are often affected by the loss of fine textures. In this paper, these problems are addressed and proposed a new coefficient of diffusion for noise removal. This new coefficient consists of two edge indicating operators, namely fractional-order difference curvature and fractional-order gradient. The fractional-order difference curvature is capable of analyzing flat surfaces, edges, ramps, and tiny textures. The fractional-order gradient can able to distinguish texture regions. The selection of the order is more flexible for the fractional order gradient and fractional-order difference curvature. This will result in effective image denoising. Since the discrete Fourier transform is simple to numerically implement, it is taken into consideration for the implementation of fractional-order gradient. The proposed method can give results that are visually appealing and improved quantitative outputs in terms of the Figure of Merit (FoM), Mean Structural Similarity (MSSIM), and Peak Signal to Noise Ratio (PSNR), according to comparative analysis.

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Zhao, Changyuan, Hongyang Du, Guangyuan Liu, and Dusit Niyato. "Supervised Score-Based Modeling by Gradient Boosting." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 21 (April 11, 2025): 22768–76. https://doi.org/10.1609/aaai.v39i21.34437.

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Score-based generative models can effectively learn the distribution of data by estimating the gradient of the distribution. Due to the multi-step denoising characteristic, researchers have recently considered combining score-based generative models with the gradient boosting algorithm, a multi-step supervised learning algorithm, to solve supervised learning tasks. However, existing generative model algorithms are often limited by the stochastic nature of the models and the long inference time, impacting prediction performances. Therefore, we propose a Supervised Score-based Model (SSM), which can be viewed as a gradient boosting algorithm combining score matching. We provide a theoretical analysis of learning and sampling for SSM to balance inference time and prediction accuracy. Via the ablation experiment in selected examples, we demonstrate the outstanding performances of the proposed techniques. Additionally, we compare our model with other probabilistic models, including Natural Gradient Boosting (NGboost), Classification and Regression Diffusion Models (CARD), Diffusion Boosted Trees (DBT), and non-probabilistic gradient boosting models. The experimental results show that our model outperforms existing models in both accuracy and inference time.

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38

Martins, Márcio AF. "A Gradient-Based Economic Model Predictive Controller with Zone Control Scheme Applied to Natural Gas Pipeline Networks." Petroleum & Petrochemical Engineering Journal 7, no. 2 (April 4, 2023): 1–9. http://dx.doi.org/10.23880/ppej-16000342.

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This paper addresses a one-layer model predictive control (MPC) strategy that simultaneously deals with safety and economic issues for natural gas networks (NGN). The simulations consider a nonlinear pipeline model based on the non-isothermal flow and non-deal gas behavior. The proposed NGN-oriented MPC strategy uses an adaptive scheme that relies upon the successive linearization of the nonlinear NGN model and the surge prevention constraints of the compression stations, incorporated into the control law to avoid unsafe operating conditions. The controller has the guarantee of feasibility by incorporating a suitable set of slack variables into its formulation, mainly in the surge avoidance constraints. At the same time, the resulting control law is more flexible by adopting output zone tracking cases rather than setpoint tracking. The simulated study, aiming at minimizing the power consumption of the centrifugal compressors, sought to control the pressures in the consumer nodes of NGN into a predefined zone while meeting the process constraints. In all scenarios of zone changes, the controller could lead the controlled outputs in their respective zones, accommodating the operation in steady states with a minimal power consumption of three compression stations considered in NGN. By respecting the surge prevention constraints flexibly, and using the slack variables when necessary, immediately after perturbation, the proposed NGN-oriented adaptive zone MPC controller has proved to be a suitable tool to manage the NGN with control performance, operational safe and economic competitivity.

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Baciu, Andrei, and Corneliu Lazar. "Gradient vs. Non-Gradient-Based Model Free Control Algorithms: Analysis and Applications to Nonlinear Systems." Applied Sciences 15, no. 5 (March 4, 2025): 2766. https://doi.org/10.3390/app15052766.

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Against the background of the development of control systems, Data Driven Control (DDC) methods are becoming more and more popular, given the system’s independence from physical models and the possibility of quickly tuning the controller. The usefulness of such tuning algorithms increases with the complexity of the plants. Nonlinear models are the main class of processes for which such laws are amenable. According to the literature, a class of DDC methods exist that perform online estimation of plant behavior with an unknown structure, which is generically called Model Free. This title is assumed by two types of algorithms, which contain it in the name. One is the gradient-based algorithm, Model Free Adaptive Control, defined by Hou, which uses the concept of dynamic linearization through pseudo partial derivatives (PPD) and pseudo gradient (PG). The other is a non-gradient based algorithm, Model Free Control, defined by Fliess and Join, which uses the concept of the ultralocal model and intelligent PID controllers (iPID). For the gradient-based methods, in the compact form of dynamic linearization (CFDL), i.e., partial form dynamic linearization (PFDL), two algorithms are proposed to determine the initial value of the time-varying parameters PPD and PG from the dynamic performance perspective as they offer the best responses. The CFDL and PFDL variants of the MFAC control law, which have parameters that result from the application of the proposed algorithms, are compared with iP and iPD controllers on nonlinear control systems.

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40

Yu, Sudan, Ali Asghar Heidari, Guoxi Liang, Chi Chen, Huiling Chen, and Qike Shao. "Solar photovoltaic model parameter estimation based on orthogonally-adapted gradient-based optimization." Optik 252 (February 2022): 168513. http://dx.doi.org/10.1016/j.ijleo.2021.168513.

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41

Kari, Dariush, Andrew C. Singer, Hari Vishnu, and Amir Weiss. "Underwater acoustic localization via gradient-based optimization." Journal of the Acoustical Society of America 153, no. 3_supplement (March 1, 2023): A177. http://dx.doi.org/10.1121/10.0018579.

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In underwater acoustic localization via matched-field-processing, given a propagation model and a suitable environmental parameterization, one searches for the location (of the transmitter or receiver) whose replica field is closest to the observed one. The high computational complexity of such non-gradient-based optimization methods renders them infeasible for many real-time scenarios, especially when an accurate solution is desired due to resolution of the search grid required, or as the search dimensionality increases (e.g., when it is necessary to optimize over uncertain environmental parameters such as sound speed or bathymetry). In this talk, we propose a ray-based, differentiable model for acoustic propagation that can be exploited in a gradient-based optimization for localization. For localization applications in which accurate times of arrival might not be available (e.g., due to the signal's relatively small bandwidth), the proposed method does not directly rely upon times of arrivals. Rather, it seeks the location (and possibly environmental parameters) that minimize the squared-error between the observed signal and its estimation via the differentiable model. We leverage the PyTorch optimization and auto-differentiation tools for the implementation and demonstrate successful localization on synthetic data in a dense multipath environment.

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42

Hassan, Basim Abbas, and Abdulameer A. Saad. "Explaining new parameters conjugate analysis based on the quadratic model." Journal of Interdisciplinary Mathematics 26, no. 6 (2023): 1219–29. http://dx.doi.org/10.47974/jim-1620.

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The coefficient conjugate is often the subject of conjugate gradient techniques. In this work, we use the quadratic function to unconstrained optimization problems and construct an unique coefficient conjugate gradient method. Based on a novel coefficient conjugate, we provided a search direction and fascinating conjugate gradient approach. We demonstrate that, given the appropriate circumstances, the proposed technique is universally convergent. Our numerical findings show that this method is effective for unconstrained optimization problems.

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43

Chen, Jing, Feng Ding, Quanmin Zhu, and Yanjun Liu. "Interval Error Correction Auxiliary Model Based Gradient Iterative Algorithms for Multirate ARX Models." IEEE Transactions on Automatic Control 65, no. 10 (October 2020): 4385–92. http://dx.doi.org/10.1109/tac.2019.2955030.

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44

Tang, Jia. "Fractional Gradient Descent-Based Auxiliary Model Algorithm for FIR Models with Missing Data." Complexity 2023 (February 9, 2023): 1–12. http://dx.doi.org/10.1155/2023/7527478.

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This study proposes a fractional gradient descent (FGD) algorithm for FIR models with missing data. By using the auxiliary model method, the missing data can be obtained. Then, the FGD algorithm is applied to update the parameters of the FIR models. Because of the fractional term in the conventional GD algorithm, the convergence rates of the GD algorithm can be increased. In addition, to avoid the step-size calculation, an Aitken FGD-based auxiliary model algorithm is also introduced. The convergence analysis and simulation examples are provided to show the effectiveness of the proposed algorithms.

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Graedel, Nadine N., Lars Kasper, Maria Engel, Jennifer Nussbaum, Bertram J. Wilm, Klaas P. Pruessmann, and S. Johanna Vannesjo. "Feasibility of spiral fMRI based on an LTI gradient model." NeuroImage 245 (December 2021): 118674. http://dx.doi.org/10.1016/j.neuroimage.2021.118674.

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黄, 丽. "Research on XGBoost Model Based on Improved Gradient Descent Algorithm." Operations Research and Fuzziology 14, no. 06 (2024): 266–79. https://doi.org/10.12677/orf.2024.146529.

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Rajabi, Farshid, and Shojaa Ramezani. "A nonlinear microbeam model based on strain gradient elasticity theory." Acta Mechanica Solida Sinica 26, no. 1 (February 2013): 21–34. http://dx.doi.org/10.1016/s0894-9166(13)60003-8.

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Makvandi, Resam, Sascha Duczek, and Daniel Juhre. "A phase-field fracture model based on strain gradient elasticity." Engineering Fracture Mechanics 220 (October 2019): 106648. http://dx.doi.org/10.1016/j.engfracmech.2019.106648.

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Zeighampour, Hamid, and Y. Tadi Beni. "Cylindrical thin-shell model based on modified strain gradient theory." International Journal of Engineering Science 78 (May 2014): 27–47. http://dx.doi.org/10.1016/j.ijengsci.2014.01.004.

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Szklarz, Slawomir P., Marielba Rojas, and Malgorzata P. Kaleta. "The optimization problem in model-reduced gradient-based history matching." IFAC Proceedings Volumes 45, no. 8 (2012): 13–18. http://dx.doi.org/10.3182/20120531-2-no-4020.00051.

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