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Relevant bibliographies by topics / Gradient-free

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Academic literature on the topic 'Gradient-free'

Author: Grafiati

Published: 20 June 2021

Last updated: 29 July 2025

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Journal articles on the topic "Gradient-free"

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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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Gradient-free"

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Werner, Tino [Verfasser], Peter [Akademischer Betreuer] Ruckdeschel, and Matthias [Akademischer Betreuer] Schmid. "Gradient-Free Gradient Boosting / Tino Werner ; Peter Ruckdeschel, Matthias Schmid." Oldenburg : BIS der Universität Oldenburg, 2020. http://d-nb.info/120419968X/34.

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Walton, Sean Peter. "Gradient free optimisation in selected engineering applications." Thesis, Swansea University, 2013. https://cronfa.swan.ac.uk/Record/cronfa43116.

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Yucelen, Tansel. "Advances in adaptive control theory: gradient- and derivative-free approaches." Diss., Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/43731.

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In this dissertation, we present new approaches to improve standard designs in adaptive control theory, and novel adaptive control architectures. We first present a novel Kalman filter based approach for approximately enforcing a linear constraint in standard adaptive control design. One application is that this leads to alternative forms for well known modification terms such as e-modification. In addition, it leads to smaller tracking errors without incurring significant oscillations in the system response and without requiring high modification gain. We derive alternative forms of e- and ad

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Harrison, Stephen C. W. "Free field modeling of a MEMS-Based pressure gradient microphone." Thesis, Monterey, California : Naval Postgraduate School, 2009. http://edocs.nps.edu/npspubs/scholarly/theses/2009/Dec/09Dec%5FHarrison.pdf.

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Thesis (M.S. in Engineering Acoustics and M.S. in Applied Physics)--Naval Postgraduate School, December 2009.<br>Thesis Advisor(s): Karunasiri, Gamani; Baker, Steven. "December 2009." Description based on title screen as viewed on January 26, 2010. Author(s) subject terms: MEMS, Model, Simulation, Pressure Gradient, Particle Velocity, Directional, Microphone, SOMSOL, Acoustic. Includes bibliographical references (p. 51-52). Also available in print.

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Refsnæs, Runar Heggelien. "Matrix-Free Conjugate Gradient Methods for Finite Element Simulations on GPUs." Thesis, Norges Teknisk-Naturvitenskaplige Universitet, Institutt for fysikk, 2010. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-10826.

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<p>A block-structured approach for solving 2-dimensional finite element approximations of the Poisson equation on graphics processing units(GPUs) is developed. Linear triangular elements are used, and a matrix-free version of the conjugate gradient method is utilized for solving test problems with over 30 million elements. A speedup of 24 is achieved on a NVIDIA Tesla C1060 GPU when compared to a serial CPU version of the same solution approach, and a comparison is made with previous GPU implementations of the same problem.</p>

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Nutini, Julie Ann. "A derivative-free approximate gradient sampling algorithm for finite minimax problems." Thesis, University of British Columbia, 2012. http://hdl.handle.net/2429/42200.

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Mathematical optimization is the process of minimizing (or maximizing) a function. An algorithm is used to optimize a function when the minimum cannot be found by hand, or finding the minimum by hand is inefficient. The minimum of a function is a critical point and corresponds to a gradient (derivative) of 0. Thus, optimization algorithms commonly require gradient calculations. When gradient information of the objective function is unavailable, unreliable or ‘expensive’ in terms of computation time, a derivative-free optimization algorithm is ideal. As the name suggests, derivative-free optimi

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Fejes, Niklas. "High Performance Multi-Objective Voyage Planning Using Local Gradient-Free Methods." Thesis, Uppsala universitet, Avdelningen för beräkningsvetenskap, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-305582.

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A number of parallel gradient-free local optimization methods are investigated in application to problems of voyage planning for maritime ships. Two optimization algorithms are investigated, a parallel version of the Nelder-Mead Simplex method and the Subplex method with Nelder-Mead Simplex as its inner solver. Additionally, two new formulations of the optimization problem are suggested which together with an improved implementation of the objective function increases the overall performance of the model. Numerical results show the efficiency of these methods in comparison with the earlier int

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Li, Futong. "Global Optimization Techniques Based on Swarm-intelligent and Gradient-free Algorithms." Thesis, Université d'Ottawa / University of Ottawa, 2021. http://hdl.handle.net/10393/42307.

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The need for solving nonlinear optimization problems is pervasive in many fields. Particle swarm optimization, advantageous with the simple underlying implementation logic, and simultaneous perturbation stochastic approximation, which is famous for its saving in the computational power with the gradient-free attribute, are two solutions that deserve attention. Many researchers have exploited their merits in widely challenging applications. However, there is a known fact that both of them suffer from a severe drawback, non- effectively converging to the global best solution, because of the loca

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Cantergiani, Elisa. "Mechanical Properties of Functionally Graded Materials: Carbon Gradient inside Interstitial Free Steel." Thesis, Université d'Ottawa / University of Ottawa, 2016. http://hdl.handle.net/10393/34314.

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In the last decade aluminium started to be considered as an alternative to steel to produce car body panels, especially considering the strict demands to decrease fuel consumption which require vehicle weight reduction. In order to keep their leading role, steel companies have to produce stronger materials to reduce the thickness of steel sheets used in cars and are now considering non-conventional steel making processes. The purpose of this PhD research was to investigate the possibility of strengthening thin sheets of interstitial free steel (IF steel) by using carbon rich films deposited o

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Alkhamis, A. T. A. "Structural optimization for static and free vibration conditions using genetic and gradient-based algorithms." Thesis, Swansea University, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.635759.

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This thesis is concerned with the development and application of reliable, creative and efficient computational tools for the analysis and structural optimization of 2D and 3D trusses under static and free vibration conditions and prismatic structures under static and free vibration conditions. The first part of the thesis deals with the analysis and optimization of 2D and 3D trusses under static and free vibration conditions. The second part of the thesis deals with prismatic structures under static and free vibration conditions which are idealised as effectively one-dimensional problems. The

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Books on the topic "Gradient-free"

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L, Healey D., United States. Dept. of Energy. Nevada Operations Office., and Geological Survey (U.S.), eds. Free-air gradient observations in Yucca Flat, Nye County, Nevada. U.S. Dept. of Interior, Geological Survey, 1985.

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L, Healey D., United States. Dept. of Energy. Nevada Operations Office, and Geological Survey (U.S.), eds. Free-air gradient observations in Yucca Flat, Nye County, Nevada. U.S. Dept. of Interior, Geological Survey, 1985.

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L, Healey D., United States. Dept. of Energy. Nevada Operations Office., and Geological Survey (U.S.), eds. Free-air gradient observations in Yucca Flat, Nye County, Nevada. U.S. Dept. of Interior, Geological Survey, 1985.

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L, Healey D., United States. Dept. of Energy. Nevada Operations Office., and Geological Survey (U.S.), eds. Free-air gradient observations in Yucca Flat, Nye County, Nevada. U.S. Dept. of Interior, Geological Survey, 1985.

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5

Wang, Yan Ming. Downward continuation of the free-air gravity anomalies to the ellipsoid using the gradient solution, Poisson's integral and terrain correction-numerical comparison and the computations. Dept. of Geodetic Science and Surveying, Ohio State University, 1988.

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1949-, Müller S. C., ed. Synthetic peptides as antigens. Elsevier, 1999.

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Free-air gradient observations in Yucca Flat, Nye County, Nevada. U.S. Dept. of Interior, Geological Survey, 1985.

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8

Deruelle, Nathalie, and Jean-Philippe Uzan. Lagrangian mechanics. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198786399.003.0008.

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This chapter shows how the Newtonian law of motion of a particle subject to a gradient force derived from a ‘potential energy’ can always be obtained from an extremal principle, or ‘principle of least action’. According to Newton’s first law, the trajectory representing the motion of a free particle between two points p1 and p2 is a straight line. In other words, out of all the possible paths between p1 and p2, the trajectory effectively followed by a free particle is the one that minimizes the length. However, even though the use of the principle of extremal length of the paths between two po

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Aarts, D. G. A. L. Soft interfaces: the case of colloid–polymer mixtures. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198789352.003.0013.

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In this chapter we discuss the interface of a phase separated colloid-polymer mixture. We start by highlighting a number of experimental studies, illustrating the richness of colloidal interface phenomena. This is followed by a derivation of the bulk phase behaviour within free volume theory. We subsequently calculate the interfacial tension using a squared gradient approach. The interfacial tension turns out to be ultralow, easily a million times smaller than a molecular interfacial tension. From the bulk and interface calculations we obtain the capillary length and compare to experiments, wh

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Book chapters on the topic "Gradient-free"

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Xiao, Zhiqing. "Policy-Based Gradient-Free Algorithms." In Reinforcement Learning. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-19-4933-3_11.

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Sudharshan, S., and G. Jeyakumar. "Gradient-Based Versus Gradient-Free Algorithms for Reinforcement Learning." In Advances in Intelligent Systems and Computing. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2709-5_10.

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Mielke, Alexander. "Free Energy, Free Entropy, and a Gradient Structure for Thermoplasticity." In Innovative Numerical Approaches for Multi-Field and Multi-Scale Problems. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-39022-2_7.

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Gasnikov, Alexander, Darina Dvinskikh, Pavel Dvurechensky, Eduard Gorbunov, Aleksandr Beznosikov, and Alexander Lobanov. "Randomized Gradient-Free Methods in Convex Optimization." In Encyclopedia of Optimization. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-030-54621-2_859-1.

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Safin, Kamil, Pavel Dvurechensky, and Alexander Gasnikov. "Adaptive Gradient-Free Method for Stochastic Optimization." In Communications in Computer and Information Science. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-92711-0_7.

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Yuan, Xin, Liangliang Ren, Jiwen Lu, and Jie Zhou. "Relaxation-Free Deep Hashing via Policy Gradient." In Computer Vision – ECCV 2018. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01225-0_9.

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Paat, Joseph, Miriam Schlöter, and Emily Speakman. "Constructing Lattice-Free Gradient Polyhedra in Dimension Two." In Integer Programming and Combinatorial Optimization. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45771-6_28.

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Croce, Francesco, and Matthias Hein. "A Randomized Gradient-Free Attack on ReLU Networks." In Lecture Notes in Computer Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-12939-2_16.

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Ghosh, Ranadhir, Moumita Ghosh, and Adil Bagirov. "Derivative Free Stochastic Discrete Gradient Method with Adaptive Mutation." In Advances in Data Mining. Applications in Medicine, Web Mining, Marketing, Image and Signal Mining. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11790853_21.

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Bisui, Nantu Kumar, Samit Mazumder, and Geetanjali Panda. "A Gradient-Free Method for Multi-objective Optimization Problem." In Optimization, Variational Analysis and Applications. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1819-2_2.

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Conference papers on the topic "Gradient-free"

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Jin, Zhenghong, Hua Li, Jinming Xu, and Jiming Chen. "A Gradient-Free Source Seeking Strategy." In 2024 IEEE 19th Conference on Industrial Electronics and Applications (ICIEA). IEEE, 2024. http://dx.doi.org/10.1109/iciea61579.2024.10664709.

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Beck, Edgar, Carsten Bockelmann, and Armin Dekorsy. "Model-free Reinforcement Learning of Semantic Communication by Stochastic Policy Gradient." In 2024 IEEE International Conference on Machine Learning for Communication and Networking (ICMLCN). IEEE, 2024. http://dx.doi.org/10.1109/icmlcn59089.2024.10625190.

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Bu, Hanfei. "Back-Propagation-Free Adversarial Attacks with Zero-th Order Gradient Estimation." In 2024 7th International Conference on Information Communication and Signal Processing (ICICSP). IEEE, 2024. https://doi.org/10.1109/icicsp62589.2024.10809255.

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Rozario, Turibius, Parham Oveissi, and Ankit Goel. "Matrix-Based Representations and Gradient-Free Algorithms for Neural Network Training." In 2024 International Conference on Machine Learning and Applications (ICMLA). IEEE, 2024. https://doi.org/10.1109/icmla61862.2024.00050.

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Schäfers, Kevin, Jacob Finkenrath, Michael Günther, and Francesco Knechtli. "Hessian-free force-gradient integrators and their application to lattice QCD simulations." In The 41st International Symposium on Lattice Field Theory. Sissa Medialab, 2025. https://doi.org/10.22323/1.466.0025.

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Zhang, Xuewei, Qingzhao Liu, Hongyu Cao, and Bailing Tian. "A Gradient-Free and Parallel Hierarchical Motion Planning Framework for Quadrotor Swarm." In 2024 18th International Conference on Control, Automation, Robotics and Vision (ICARCV). IEEE, 2024. https://doi.org/10.1109/icarcv63323.2024.10821567.

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Zeng, Zihang, Chenxi Zhong, and Xiaojun Yuan. "Channel-Estimation-Free Gradient Aggregation for OFDM Over-the-Air Federated Learning." In 2024 16th International Conference on Wireless Communications and Signal Processing (WCSP). IEEE, 2024. https://doi.org/10.1109/wcsp62071.2024.10826990.

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Fu, Tingchen, Yupeng Hou, Julian McAuley, and Rui Yan. "Unlocking Decoding-time Controllability: Gradient-Free Multi-Objective Alignment with Contrastive Prompts." In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). Association for Computational Linguistics, 2025. https://doi.org/10.18653/v1/2025.naacl-long.18.

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Fei, Zhengcong, Mingyuan Fan, and Junshi Huang. "Gradient-Free Textual Inversion." In MM '23: The 31st ACM International Conference on Multimedia. ACM, 2023. http://dx.doi.org/10.1145/3581783.3612599.

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Shen, Maohao, Soumya Ghosh, Prasanna Sattigeri, Subhro Das, Yuheng Bu, and Gregory Wornell. "Reliable Gradient-free and Likelihood-free Prompt Tuning." In Findings of the Association for Computational Linguistics: EACL 2023. Association for Computational Linguistics, 2023. http://dx.doi.org/10.18653/v1/2023.findings-eacl.183.

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Reports on the topic "Gradient-free"

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Williams, Brian J., Kayla Coleman, Ralph C. Smith, and Max D. Morris. Gradient-Free Construction of Active Subspaces for Dimension Reduction. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1523205.

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Cruz, Edward J. Chemical Free Surface Processing for High Gradient Superconducting RF Cavities. Office of Scientific and Technical Information (OSTI), 2015. http://dx.doi.org/10.2172/1464736.

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Hartemann, F. V., A. L. Troha, and H. A. Baldis. Chirped-Pulse Inverse Free Electron Laser: A Tabletop, High-Gradient Vacuum Laser Accelerator. Office of Scientific and Technical Information (OSTI), 2001. http://dx.doi.org/10.2172/15007297.

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Bhushan, Shanti, Greg Burgreen, Wesley Brewer, and Ian Dettwiller. Assessment of neural network augmented Reynolds averaged Navier Stokes turbulence model in extrapolation modes. Engineer Research and Development Center (U.S.), 2025. https://doi.org/10.21079/11681/49702.

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A machine-learned model enhances the accuracy of turbulence transport equations of RANS solver and applied for periodic hill test case. The accuracy is investigated in extrapolation modes. A parametric study is also performed to understand the effect of network hyperparameters on training and model accuracy and to quantify the uncertainty in model accuracy due to the non-deterministic nature of the neural network training. For any network, less than optimal mini-batch size results in overfitting, and larger than optimal reduces accuracy. Data clustering is an efficient approach to prevent the

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Oliynyk, Kateryna, and Matteo Ciantia. Application of a finite deformation multiplicative plasticity model with non-local hardening to the simulation of CPTu tests in a structured soil. University of Dundee, 2021. http://dx.doi.org/10.20933/100001230.

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In this paper an isotropic hardening elastoplastic constitutive model for structured soils is applied to the simulation of a standard CPTu test in a saturated soft structured clay. To allow for the extreme deformations experienced by the soil during the penetration process, the model is formulated in a fully geometric non-linear setting, based on: i) the multiplicative decomposition of the deformation gradient into an elastic and a plastic part; and, ii) on the existence of a free energy function to define the elastic behaviour of the soil. The model is equipped with two bonding-related intern

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