Bibliografías temáticas / Steplength selection

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Literatura académica sobre el tema "Steplength selection"

Autor: Grafiati
Publicado: 10 de marzo de 2023
Última modificación: 19 de julio de 2025

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Artículos de revistas sobre el tema "Steplength selection"

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Curtis, Frank, and Jorge Nocedal. "Steplength selection in interior-point methods for quadratic programming." Applied Mathematics Letters 20, no. 5 (2007): 516–23. http://dx.doi.org/10.1016/j.aml.2006.05.020.

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di Serafino, Daniela, Valeria Ruggiero, Gerardo Toraldo, and Luca Zanni. "On the steplength selection in gradient methods for unconstrained optimization." Applied Mathematics and Computation 318 (February 2018): 176–95. http://dx.doi.org/10.1016/j.amc.2017.07.037.

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Crisci, Serena, Valeria Ruggiero, and Luca Zanni. "Steplength selection in gradient projection methods for box-constrained quadratic programs." Applied Mathematics and Computation 356 (September 2019): 312–27. http://dx.doi.org/10.1016/j.amc.2019.03.039.

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Gunzburger, Max D., and Janet S. Peterson. "Predictor and steplength selection in continuation methods for the Navier-Stokes equations." Computers & Mathematics with Applications 22, no. 8 (1991): 73–81. http://dx.doi.org/10.1016/0898-1221(91)90015-v.

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Porta, Federica, Marco Prato, and Luca Zanni. "A New Steplength Selection for Scaled Gradient Methods with Application to Image Deblurring." Journal of Scientific Computing 65, no. 3 (2015): 895–919. http://dx.doi.org/10.1007/s10915-015-9991-9.

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Loris, I., M. Bertero, C. De Mol, R. Zanella та L. Zanni. "Accelerating gradient projection methods for ℓ1-constrained signal recovery by steplength selection rules". Applied and Computational Harmonic Analysis 27, № 2 (2009): 247–54. http://dx.doi.org/10.1016/j.acha.2009.02.003.

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Franchini, Giorgia, Valeria Ruggiero, Federica Porta, and Luca Zanni. "Neural architecture search via standard machine learning methodologies." Mathematics in Engineering 5, no. 1 (2022): 1–21. http://dx.doi.org/10.3934/mine.2023012.

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<abstract><p>In the context of deep learning, the more expensive computational phase is the full training of the learning methodology. Indeed, its effectiveness depends on the choice of proper values for the so-called hyperparameters, namely the parameters that are not trained during the learning process, and such a selection typically requires an extensive numerical investigation with the execution of a significant number of experimental trials. The aim of the paper is to investigate how to choose the hyperparameters related to both the architecture of a Convolutional Neural Netwo
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Franchini, Giorgia, Valeria Ruggiero, and Luca Zanni. "Ritz-like values in steplength selections for stochastic gradient methods." Soft Computing 24, no. 23 (2020): 17573–88. http://dx.doi.org/10.1007/s00500-020-05219-6.

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Pellegrini, Riccardo, Andrea Serani, Giampaolo Liuzzi, Francesco Rinaldi, Stefano Lucidi, and Matteo Diez. "A Derivative-Free Line-Search Algorithm for Simulation-Driven Design Optimization Using Multi-Fidelity Computations." Mathematics 10, no. 3 (2022): 481. http://dx.doi.org/10.3390/math10030481.

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The paper presents a multi-fidelity extension of a local line-search-based derivative-free algorithm for nonsmooth constrained optimization (MF-CS-DFN). The method is intended for use in the simulation-driven design optimization (SDDO) context, where multi-fidelity computations are used to evaluate the objective function. The proposed algorithm starts using low-fidelity evaluations and automatically switches to higher-fidelity evaluations based on the line-search step length. The multi-fidelity algorithm is driven by a suitably defined threshold and initialization values for the step length, w
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Crisci, Serena, Federica Porta, Valeria Ruggiero, and Luca Zanni. "Hybrid limited memory gradient projection methods for box-constrained optimization problems." Computational Optimization and Applications, September 4, 2022. http://dx.doi.org/10.1007/s10589-022-00409-4.

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AbstractGradient projection methods represent effective tools for solving large-scale constrained optimization problems thanks to their simple implementation and low computational cost per iteration. Despite these good properties, a slow convergence rate can affect gradient projection schemes, especially when high accurate solutions are needed. A strategy to mitigate this drawback consists in properly selecting the values for the steplength along the negative gradient. In this paper, we consider the class of gradient projection methods with line search along the projected arc for box-constrain
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Tesis sobre el tema "Steplength selection"

1

DE, ASMUNDIS ROBERTA. "New gradient methods: spectral properties and steplength selection." Doctoral thesis, 2014. http://hdl.handle.net/11573/918064.

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Sgattoni, Cristina. "Solving systems of nonlinear equations via spectral residual methods." Doctoral thesis, 2021. http://hdl.handle.net/2158/1238325.

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This thesis addresses the numerical solution of systems of nonlinear equations via spectral residual methods. Spectral residual methods are iterative procedures, they use the residual vector as search direction and a spectral steplength, i.e., a steplength that is related to the spectrum of the average matrices associated to the Jacobian matrix of the system. Such procedures are widely studied and employed since they are derivative-free and low-cost per iteration. The first aim of the work is to analyze the properties of the spectral residual steplengths and study how they affect the perfo
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Capítulos de libros sobre el tema "Steplength selection"

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Franchini, Giorgia, Valeria Ruggiero, and Luca Zanni. "On the Steplength Selection in Stochastic Gradient Methods." In Lecture Notes in Computer Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39081-5_17.

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Franchini, Giorgia, Valeria Ruggiero, and Luca Zanni. "Steplength and Mini-batch Size Selection in Stochastic Gradient Methods." In Machine Learning, Optimization, and Data Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-64580-9_22.

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Franchini, Giorgia, Valeria Ruggiero, and Ilaria Trombini. "Thresholding Procedure via Barzilai-Borwein Rules for the Steplength Selection in Stochastic Gradient Methods." In Machine Learning, Optimization, and Data Science. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-95470-3_21.

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