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Journal articles on the topic 'Gradient-free'

Author: Grafiati

Published: 20 June 2021

Last updated: 29 July 2025

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1

Yan, Liang, and Xiling Zou. "Gradient-free Stein variational gradient descent with kernel approximation." Applied Mathematics Letters 121 (November 2021): 107465. http://dx.doi.org/10.1016/j.aml.2021.107465.

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2

Schäfers, Kevin, Jacob Finkenrath, Michael Günther, and Francesco Knechtli. "Hessian-free force-gradient integrators." Computer Physics Communications 309 (April 2025): 109478. https://doi.org/10.1016/j.cpc.2024.109478.

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3

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 (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 gradien

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4

Carriero, M., A. Leaci, and F. Tomarelli. "Free gradient discontinuity and image inpainting." Journal of Mathematical Sciences 181, no. 6 (2012): 805–19. http://dx.doi.org/10.1007/s10958-012-0716-4.

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5

Pappas, Nathaniel. "Rank Gradient andp-gradient of Amalgamated Free Products and HNN Extensions." Communications in Algebra 43, no. 10 (2015): 4515–27. http://dx.doi.org/10.1080/00927872.2014.948631.

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6

Bosch, Jaime, and Juan Carlos García-Pagán. "Calculating Hepatic Venous Pressure Gradient: Feel Free to Stay Free." Journal of Vascular and Interventional Radiology 27, no. 8 (2016): 1138–39. http://dx.doi.org/10.1016/j.jvir.2016.03.048.

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7

Arrasmith, Andrew, M. Cerezo, Piotr Czarnik, Lukasz Cincio, and Patrick J. Coles. "Effect of barren plateaus on gradient-free optimization." Quantum 5 (October 5, 2021): 558. http://dx.doi.org/10.22331/q-2021-10-05-558.

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Barren plateau landscapes correspond to gradients that vanish exponentially in the number of qubits. Such landscapes have been demonstrated for variational quantum algorithms and quantum neural networks with either deep circuits or global cost functions. For obvious reasons, it is expected that gradient-based optimizers will be significantly affected by barren plateaus. However, whether or not gradient-free optimizers are impacted is a topic of debate, with some arguing that gradient-free approaches are unaffected by barren plateaus. Here we show that, indeed, gradient-free optimizers do not s

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8

Arrasmith, Andrew, M. Cerezo, Piotr Czarnik, Lukasz Cincio, and Patrick J. Coles. "Effect of barren plateaus on gradient-free optimization." Quantum 5 (October 5, 2021): 558. http://dx.doi.org/10.22331/q-2021-10-05-558.

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Barren plateau landscapes correspond to gradients that vanish exponentially in the number of qubits. Such landscapes have been demonstrated for variational quantum algorithms and quantum neural networks with either deep circuits or global cost functions. For obvious reasons, it is expected that gradient-based optimizers will be significantly affected by barren plateaus. However, whether or not gradient-free optimizers are impacted is a topic of debate, with some arguing that gradient-free approaches are unaffected by barren plateaus. Here we show that, indeed, gradient-free optimizers do not s

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9

Zhang, Wengang, Francis W. Starr, and Jack F. Douglas. "Activation free energy gradient controls interfacial mobility gradient in thin polymer films." Journal of Chemical Physics 155, no. 17 (2021): 174901. http://dx.doi.org/10.1063/5.0064866.

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10

Pang, Yipeng, and Guoqiang Hu. "Gradient-free distributed optimization with exact convergence." Automatica 144 (October 2022): 110474. http://dx.doi.org/10.1016/j.automatica.2022.110474.

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11

Diest, Kenneth, Luke A. Sweatlock, and Daniel E. Marthaler. "Metamaterials design using gradient-free numerical optimization." Journal of Applied Physics 108, no. 8 (2010): 084303. http://dx.doi.org/10.1063/1.3498816.

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12

Garg, Anupam. "Singular gradient free energy of superfluidA3atT=0." Physical Review B 36, no. 13 (1987): 6794–98. http://dx.doi.org/10.1103/physrevb.36.6794.

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13

Wang, B., K. Aihara, and L. Chen. "Jamming in weighted scale-free gradient networks." EPL (Europhysics Letters) 83, no. 2 (2008): 28006. http://dx.doi.org/10.1209/0295-5075/83/28006.

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14

Ji, Hao, and Yaohang Li. "A breakdown-free block conjugate gradient method." BIT Numerical Mathematics 57, no. 2 (2016): 379–403. http://dx.doi.org/10.1007/s10543-016-0631-z.

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15

Nesterov, Yurii, and Vladimir Spokoiny. "Random Gradient-Free Minimization of Convex Functions." Foundations of Computational Mathematics 17, no. 2 (2015): 527–66. http://dx.doi.org/10.1007/s10208-015-9296-2.

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16

Kumar, G. Naresh, Md Shafeeq Ahmed, A. K. Sarkar, and S. E. Talole. "Reentry Trajectory Optimization using Gradient Free Algorithms." IFAC-PapersOnLine 51, no. 1 (2018): 650–55. http://dx.doi.org/10.1016/j.ifacol.2018.05.109.

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17

Jennrich, Robert I. "Derivative free gradient projection algorithms for rotation." Psychometrika 69, no. 3 (2004): 475–80. http://dx.doi.org/10.1007/bf02295647.

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18

Mondal, Arindam, and Laxmidhar Behera. "Gradient-Based Collision Free Desired Formation Generation." IFAC Proceedings Volumes 47, no. 1 (2014): 448–54. http://dx.doi.org/10.3182/20140313-3-in-3024.00241.

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19

Tian, Jingxuan, Yibo Gao, Bingpu Zhou, Wenbin Cao, Xiaoxiao Wu, and Weijia Wen. "A valve-free 2D concentration gradient generator." RSC Advances 7, no. 45 (2017): 27833–39. http://dx.doi.org/10.1039/c7ra02139a.

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20

Li, Jueyou, Changzhi Wu, Zhiyou Wu, and Qiang Long. "Gradient-free method for nonsmooth distributed optimization." Journal of Global Optimization 61, no. 2 (2014): 325–40. http://dx.doi.org/10.1007/s10898-014-0174-2.

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21

De Silva, Daniela, and David Jerison. "A gradient bound for free boundary graphs." Communications on Pure and Applied Mathematics 64, no. 4 (2010): 538–55. http://dx.doi.org/10.1002/cpa.20354.

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22

Huang, C., J. M. Lei, M. B. Liu, and X. Y. Peng. "A kernel gradient free (KGF) SPH method." International Journal for Numerical Methods in Fluids 78, no. 11 (2015): 691–707. http://dx.doi.org/10.1002/fld.4037.

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23

Zhou, Xiangkui, Yan Wang, Renru Wang, et al. "Preparation and microstructure of layered structure functional gradient cemented carbides." Functional Materials Letters 12, no. 04 (2019): 1950053. http://dx.doi.org/10.1142/s179360471950053x.

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In this study, ultrafine WC powder was used as raw material, cubic carbonitrides Ti(C,N) and (W,Ti)C were added as gradient former, the cemented carbides with cubic carbide free layer (CCFL) structure was prepared by two-step sintering which was pre-sintering combined gradient sintering. The effect of gradient sintering temperature on the free-cubic layer structure formation and grain growth was studied. The results show that the layer structure gradient-cemented carbide with free-cubic phase in the surface can be prepared by two-step sintering when the ultrafine WC powder was used as raw mate

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24

de Bruin, Tim, Jens Kober, Karl Tuyls, and Robert Babuška. "Fine-tuning Deep RL with Gradient-Free Optimization." IFAC-PapersOnLine 53, no. 2 (2020): 8049–56. http://dx.doi.org/10.1016/j.ifacol.2020.12.2240.

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25

Amstutz, Samuel, and Nicolas Van Goethem. "Topology optimization methods with gradient-free perimeter approximation." Interfaces and Free Boundaries 14, no. 3 (2012): 401–30. http://dx.doi.org/10.4171/ifb/286.

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26

Kurmi, Vinod K., Rishabh Sharma, Yash Vardhan Sharma, and Vinay P. Namboodiri. "Gradient Based Activations for Accurate Bias-Free Learning." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 7 (2022): 7255–62. http://dx.doi.org/10.1609/aaai.v36i7.20687.

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Bias mitigation in machine learning models is imperative, yet challenging. While several approaches have been proposed, one view towards mitigating bias is through adversarial learning. A discriminator is used to identify the bias attributes such as gender, age or race in question. This discriminator is used adversarially to ensure that it cannot distinguish the bias attributes. The main drawback in such a model is that it directly introduces a trade-off with accuracy as the features that the discriminator deems to be sensitive for discrimination of bias could be correlated with classification

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27

Farup, Ivar. "Individualised Halo-Free Gradient-Domain Colour Image Daltonisation." Journal of Imaging 6, no. 11 (2020): 116. http://dx.doi.org/10.3390/jimaging6110116.

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Daltonisation refers to the recolouring of images such that details normally lost by colour vision deficient observers become visible. This comes at the cost of introducing artificial colours. In a previous work, we presented a gradient-domain colour image daltonisation method that outperformed previously known methods both in behavioural and psychometric experiments. In the present paper, we improve the method by (i) finding a good first estimate of the daltonised image, thus reducing the computational time significantly, and (ii) introducing local linear anisotropic diffusion, thus effective

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28

Pan, Gui-Jun, Xiao-Qing Yan, Zhong-Bing Huang, and Wei-Chuan Ma. "Gradient networks on uncorrelated random scale-free networks." Physica Scripta 83, no. 3 (2011): 035803. http://dx.doi.org/10.1088/0031-8949/83/03/035803.

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29

Li, Jueyou, Guoquan Li, Zhiyou Wu, et al. "Incremental gradient-free method for nonsmooth distributed optimization." Journal of Industrial & Management Optimization 13, no. 4 (2017): 1841–57. http://dx.doi.org/10.3934/jimo.2017021.

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30

Ko, Young-Gwang, Carlos C. Co, and Chia-Chi Ho. "Gradient-free directional cell migration in continuous microchannels." Soft Matter 9, no. 8 (2013): 2467–74. http://dx.doi.org/10.1039/c2sm27567h.

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31

Sengupta, Biswa, Karl J. Friston, and Will D. Penny. "Gradient-free MCMC methods for dynamic causal modelling." NeuroImage 112 (May 2015): 375–81. http://dx.doi.org/10.1016/j.neuroimage.2015.03.008.

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32

Burago, N. G., and I. S. Nikitin. "Matrix-Free Conjugate Gradient Implementation of Implicit Schemes." Computational Mathematics and Mathematical Physics 58, no. 8 (2018): 1247–58. http://dx.doi.org/10.1134/s0965542518080043.

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33

Rasoulianboroujeni, Morteza, Mostafa Yazdimamaghani, Payam Khoshkenar, et al. "From solvent-free microspheres to bioactive gradient scaffolds." Nanomedicine: Nanotechnology, Biology and Medicine 13, no. 3 (2017): 1157–69. http://dx.doi.org/10.1016/j.nano.2016.10.008.

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34

Po, Giacomo, Markus Lazar, Dariush Seif, and Nasr Ghoniem. "Singularity-free dislocation dynamics with strain gradient elasticity." Journal of the Mechanics and Physics of Solids 68 (August 2014): 161–78. http://dx.doi.org/10.1016/j.jmps.2014.03.005.

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35

Grushkovska, V. V. "Gradient-free control algorithms for dynamic optimization problems." Visnik Nacional'noi' academii' nauk Ukrai'ni 08 (August 20, 2018): 66–75. http://dx.doi.org/10.15407/visn2018.08.066.

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36

Lin, Yi-Hsin, Chih-Ming Yang, Chun-Hsiang Lo, Yan-Rung Lin, Shie-Chang Jeng, and Chi-Chang Liao. "Polarizer-Free Gradient Dye-Doped Liquid Crystal Gels." Molecular Crystals and Liquid Crystals 511, no. 1 (2009): 309/[1779]—318/[1788]. http://dx.doi.org/10.1080/15421400903054402.

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37

Ilinsky, Roman. "Gradient-index meniscus lens free of spherical aberration." Journal of Optics A: Pure and Applied Optics 2, no. 5 (2000): 449–51. http://dx.doi.org/10.1088/1464-4258/2/5/316.

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38

Rosenau, Philip. "Free-energy functionals at the high-gradient limit." Physical Review A 41, no. 4 (1990): 2227–30. http://dx.doi.org/10.1103/physreva.41.2227.

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39

Pinto, Jefferson Wellano Oliveira, Juan Alberto Rojas Tueros, Bernardo Horowitz, Silvana Maria Bastos Afonso da Silva, Ramiro Brito Willmersdorf, and Diego Felipe Barbosa de Oliveira. "Gradient-free strategies to robust well control optimization." Computational Geosciences 24, no. 6 (2019): 1959–78. http://dx.doi.org/10.1007/s10596-019-09888-7.

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40

Michael, Elad, Chris Manzie, Tony A. Wood, Daniel Zelazo, and Iman Shames. "Gradient free cooperative seeking of a moving source." Automatica 152 (June 2023): 110948. http://dx.doi.org/10.1016/j.automatica.2023.110948.

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41

Marumo, Naoki, and Akiko Takeda. "Parameter-Free Accelerated Gradient Descent for Nonconvex Minimization." SIAM Journal on Optimization 34, no. 2 (2024): 2093–120. http://dx.doi.org/10.1137/22m1540934.

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42

Riel, Bryan, and Tobias Bischoff. "Gradient-free score-based sampling methods with ensembles." Applied Mathematical Modelling 147 (November 2025): 116224. https://doi.org/10.1016/j.apm.2025.116224.

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43

Lin, Zhenwei, Jingfan Xia, Qi Deng, and Luo Luo. "Decentralized Gradient-Free Methods for Stochastic Non-smooth Non-convex Optimization." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 16 (2024): 17477–86. http://dx.doi.org/10.1609/aaai.v38i16.29697.

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We consider decentralized gradient-free optimization of minimizing Lipschitz continuous functions that satisfy neither smoothness nor convexity assumption. We propose two novel gradient-free algorithms, the Decentralized Gradient-Free Method (DGFM) and its variant, the Decentralized Gradient-Free Method+ (DGFM+). Based on the techniques of randomized smoothing and gradient tracking, DGFM requires the computation of the zeroth-order oracle of a single sample in each iteration, making it less demanding in terms of computational resources for individual computing nodes. Theoretically, DGFM achiev

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44

Wilson, M. E., T. K. Yeoman, L. J. Baddeley, and B. J. Kellet. "A Statistical investigation of the invariant latitude dependence of unstable magnetospheric ion populations in relation to high m ULF wave generation." Annales Geophysicae 24, no. 11 (2006): 3027–40. http://dx.doi.org/10.5194/angeo-24-3027-2006.

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Abstract. A statistical study is presented of the unstable proton populations, which contain the free energy required to drive small-scale poloidal mode ULF waves in the magnetosphere, observed at invariant latitudes of 60° to 80°. The data are all in the form of Ion Distribution Functions (IDFs) amassed over ~6 years using the CAMMICE (MICS) instrument on the Polar spacecraft, and cover proton energies of 1 keV to 328 keV. The free energy contained in the unstable, positive gradient regions of the IDFs is available to drive resonant wave growth. The results show that positive gradient regions

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45

D. MILLER, DEBORAH, DALE A. CALLAHAM, DAVID J. GROSS, and PETER K. HEPLER. "Free Ca2+ Gradient in Growing Pollen Tubes of Lillium." Journal of Cell Science 101, no. 1 (1992): 7–12. http://dx.doi.org/10.1242/jcs.101.1.7.

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Fluorescence ratiometric imaging of Lilium pollen tubes loaded with the Ca2+ indicator Fura-2 dextran has revealed a distinct elevation of free intracellular calcium ion concentration ([Ca2+]i) at the extreme tip of actively growing Lilium pollen tubes that declines to a uniform basal level of 170 nM throughout the length of the tube. The calcium gradient occurs within the first 10–20 μm proximal to the tip. Experimental inhibition of tip growth, usually achieved through the injection of the Ca2+ buffer 5,5′-dibromo BAPTA, results in the loss of the [Ca2+]i gradient. Occasionally these inhibit

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46

Heung Soo Kim, Jaehun Lee, and Maenghyo Cho. "CM-KR-2 Reduction of Free-Edge Peeling Stress in Laminated Composites Using Thermal Gradient." Proceedings of Mechanical Engineering Congress, Japan 2012 (2012): _CM—KR—2–1—_CM—KR—2–2. http://dx.doi.org/10.1299/jsmemecj.2012._cm-kr-2-1.

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47

Ramamoorthy, Vivek T., Ender Özcan, Andrew J. Parkes, Luc Jaouen, and François-Xavier Bécot. "Multi-objective topology optimisation for acoustic porous materials using gradient-based, gradient-free, and hybrid strategies." Journal of the Acoustical Society of America 153, no. 5 (2023): 2945. http://dx.doi.org/10.1121/10.0019455.

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When designing passive sound-attenuation structures, one of the challenging problems that arise is optimally distributing acoustic porous materials within a design region so as to maximise sound absorption while minimising material usage. To identify efficient optimisation strategies for this multi-objective problem, several gradient, non-gradient, and hybrid topology optimisation strategies are compared. For gradient approaches, the solid-isotropic-material-with-penalisation method and a gradient-based constructive heuristic are considered. For gradient-free approaches, hill climbing with a w

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48

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 (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 assu

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49

Toğan, Vedat. "Optimization of Monopod Offshore Tower under Uncertainties with Gradient-Based and Gradient-Free Optimization Algorithms." Advances in Structural Engineering 15, no. 12 (2012): 2021–32. http://dx.doi.org/10.1260/1369-4332.15.12.2021.

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50

Xie, Jiahao, Zebang Shen, Chao Zhang, Boyu Wang, and Hui Qian. "Efficient Projection-Free Online Methods with Stochastic Recursive Gradient." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 6446–53. http://dx.doi.org/10.1609/aaai.v34i04.6116.

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This paper focuses on projection-free methods for solving smooth Online Convex Optimization (OCO) problems. Existing projection-free methods either achieve suboptimal regret bounds or have high per-round computational costs. To fill this gap, two efficient projection-free online methods called ORGFW and MORGFW are proposed for solving stochastic and adversarial OCO problems, respectively. By employing a recursive gradient estimator, our methods achieve optimal regret bounds (up to a logarithmic factor) while possessing low per-round computational costs. Experimental results demonstrate the eff

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