Gotowa bibliografia na temat „Smoothed functional algorithms”
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Artykuły w czasopismach na temat "Smoothed functional algorithms"
Bhatnagar, Shalabh. "Adaptive Newton-based multivariate smoothed functional algorithms for simulation optimization". ACM Transactions on Modeling and Computer Simulation 18, nr 1 (grudzień 2007): 1–35. http://dx.doi.org/10.1145/1315575.1315577.
Pełny tekst źródłaBhatnagar, Shalabh, i Vivek S. Borkar. "Multiscale Chaotic SPSA and Smoothed Functional Algorithms for Simulation Optimization". SIMULATION 79, nr 10 (październik 2003): 568–80. http://dx.doi.org/10.1177/0037549703039988.
Pełny tekst źródłaGhoshdastidar, Debarghya, Ambedkar Dukkipati i Shalabh Bhatnagar. "Smoothed Functional Algorithms for Stochastic Optimization Using q -Gaussian Distributions". ACM Transactions on Modeling and Computer Simulation 24, nr 3 (2.05.2014): 1–26. http://dx.doi.org/10.1145/2628434.
Pełny tekst źródłaGhoshdastidar, Debarghya, Ambedkar Dukkipati i Shalabh Bhatnagar. "Newton-based stochastic optimization using q-Gaussian smoothed functional algorithms". Automatica 50, nr 10 (październik 2014): 2606–14. http://dx.doi.org/10.1016/j.automatica.2014.08.021.
Pełny tekst źródłaPrasad, H. L., L. A. Prashanth, Shalabh Bhatnagar i Nirmit Desai. "Adaptive Smoothed Functional Algorithms for Optimal Staffing Levels in Service Systems". Service Science 5, nr 1 (marzec 2013): 29–55. http://dx.doi.org/10.1287/serv.1120.0035.
Pełny tekst źródłaLakshmanan, K., i Shalabh Bhatnagar. "Quasi-Newton smoothed functional algorithms for unconstrained and constrained simulation optimization". Computational Optimization and Applications 66, nr 3 (15.09.2016): 533–56. http://dx.doi.org/10.1007/s10589-016-9875-4.
Pełny tekst źródłaNOROUZZADEH, P., B. RAHMANI i M. S. NOROUZZADEH. "FORECASTING SMOOTHED NON-STATIONARY TIME SERIES USING GENETIC ALGORITHMS". International Journal of Modern Physics C 18, nr 06 (czerwiec 2007): 1071–86. http://dx.doi.org/10.1142/s0129183107011133.
Pełny tekst źródłaKostjukov, V. A., M. Y. Medvedev i V. Kh Pshikhopov. "Algorithms for Planning Smoothed Individual Trajectories of Ground Robots". Mekhatronika, Avtomatizatsiya, Upravlenie 23, nr 11 (3.11.2022): 585–95. http://dx.doi.org/10.17587/mau.23.585-595.
Pełny tekst źródłaVijayan, Nithia, i Prashanth L.A. "Smoothed functional-based gradient algorithms for off-policy reinforcement learning: A non-asymptotic viewpoint". Systems & Control Letters 155 (wrzesień 2021): 104988. http://dx.doi.org/10.1016/j.sysconle.2021.104988.
Pełny tekst źródłaCheng, Chuen-Sheng, Pei-Wen Chen i Yu-Tang Wu. "Phase I Analysis of Nonlinear Profiles Using Anomaly Detection Techniques". Applied Sciences 13, nr 4 (7.02.2023): 2147. http://dx.doi.org/10.3390/app13042147.
Pełny tekst źródłaRozprawy doktorskie na temat "Smoothed functional algorithms"
Sällberg, Gustav, i Pontus Söderbäck. "Thesis - Optimizing Smooth Local Volatility Surfaces with Power Utility Functions". Thesis, Linköpings universitet, Produktionsekonomi, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-120090.
Pełny tekst źródłaBarajas, Leandro G. "Process Control in High-Noise Environments Using A Limited Number Of Measurements". Diss., Georgia Institute of Technology, 2003. http://hdl.handle.net/1853/7741.
Pełny tekst źródłaVestin, Albin, i Gustav Strandberg. "Evaluation of Target Tracking Using Multiple Sensors and Non-Causal Algorithms". Thesis, Linköpings universitet, Reglerteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-160020.
Pełny tekst źródłaLakshmanan, K. "Online Learning and Simulation Based Algorithms for Stochastic Optimization". Thesis, 2012. http://hdl.handle.net/2005/3245.
Pełny tekst źródłaKsiążki na temat "Smoothed functional algorithms"
Moerder, Daniel D. Constrained minimization of smooth functions using a genetic algorithm. Hampton: National Aeronautics and Space Administration, Langley Research Center, 1994.
Znajdź pełny tekst źródłaN, Pamadi Bandu, i Langley Research Center, red. Constrained minimization of smooth functions using a genetic algorithm. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1994.
Znajdź pełny tekst źródłaCzęści książek na temat "Smoothed functional algorithms"
Bhatnagar, S., H. Prasad i L. Prashanth. "Smoothed Functional Gradient Schemes". W Stochastic Recursive Algorithms for Optimization, 77–102. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-4285-0_6.
Pełny tekst źródłaBhatnagar, S., H. Prasad i L. Prashanth. "Newton-Based Smoothed Functional Algorithms". W Stochastic Recursive Algorithms for Optimization, 133–48. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-4285-0_8.
Pełny tekst źródłaLakshmanan, K., i Shalabh Bhatnagar. "Smoothed Functional and Quasi-Newton Algorithms for Routing in Multi-stage Queueing Network with Constraints". W Distributed Computing and Internet Technology, 175–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19056-8_12.
Pełny tekst źródłaBläser, Markus, Bodo Manthey i B. V. Raghavendra Rao. "Smoothed Analysis of Partitioning Algorithms for Euclidean Functionals". W Lecture Notes in Computer Science, 110–21. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22300-6_10.
Pełny tekst źródłaJ. Zaslavski, Alexander. "Gradient Algorithm with a Smooth Objective Function". W Convex Optimization with Computational Errors, 127–50. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-37822-6_4.
Pełny tekst źródłaZaslavski, Alexander J. "Gradient Algorithm with a Smooth Objective Function". W Springer Optimization and Its Applications, 59–72. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-30921-7_4.
Pełny tekst źródłaMok, RenHao, i Mohd Ashraf Ahmad. "Power Production Optimization of Model-Free Wind Farm Using Smoothed Functional Algorithm". W Lecture Notes in Electrical Engineering, 679–89. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8690-0_60.
Pełny tekst źródłaGiesl, Peter. "Towards Calculating the Basin of Attraction of Non-Smooth Dynamical Systems Using Radial Basis Functions". W Approximation Algorithms for Complex Systems, 205–25. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16876-5_9.
Pełny tekst źródłaCruttwell, Geoffrey S. H., Bruno Gavranović, Neil Ghani, Paul Wilson i Fabio Zanasi. "Categorical Foundations of Gradient-Based Learning". W Programming Languages and Systems, 1–28. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-99336-8_1.
Pełny tekst źródłaBredies, Kristian. "Recovering Piecewise Smooth Multichannel Images by Minimization of Convex Functionals with Total Generalized Variation Penalty". W Efficient Algorithms for Global Optimization Methods in Computer Vision, 44–77. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54774-4_3.
Pełny tekst źródłaStreszczenia konferencji na temat "Smoothed functional algorithms"
Ghoshdastidar, Debarghya, Ambedkar Dukkipati i Shalabh Bhatnagar. "q-Gaussian based Smoothed Functional algorithms for stochastic optimization". W 2012 IEEE International Symposium on Information Theory - ISIT. IEEE, 2012. http://dx.doi.org/10.1109/isit.2012.6283013.
Pełny tekst źródłaSegeth, Karel. "Multivariate smooth interpolation that employs polyharmonic functions". W Programs and Algorithms of Numerical Mathematics 19. Institute of Mathematics, Czech Academy of Sciences, 2019. http://dx.doi.org/10.21136/panm.2018.15.
Pełny tekst źródłaKhamisov, O. V. "Optimization with quadratic support functions in nonconvex smooth optimization". W NUMERICAL COMPUTATIONS: THEORY AND ALGORITHMS (NUMTA–2016): Proceedings of the 2nd International Conference “Numerical Computations: Theory and Algorithms”. Author(s), 2016. http://dx.doi.org/10.1063/1.4965331.
Pełny tekst źródłaMohanty, Amit, i Bin Yao. "Indirect Adaptive Robust Control of Uncertain Systems With Unknown Asymmetric Input Deadband Using a Smooth Inverse". W ASME 2009 Dynamic Systems and Control Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/dscc2009-2771.
Pełny tekst źródłaJuan Geng, Lai-Sheng Wang, Ai-Min Fu i Qi-Qing Song. "A smoothed rank function algorithm based Hyperbolic Tangent function for matrix completion". W 2012 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2012. http://dx.doi.org/10.1109/icmlc.2012.6359558.
Pełny tekst źródłaZhifu Cui, Hang Zhang i Wei Lu. "An improved smoothed l0-norm algorithm based on multiparameter approximation function". W 2010 12th IEEE International Conference on Communication Technology (ICCT). IEEE, 2010. http://dx.doi.org/10.1109/icct.2010.5688553.
Pełny tekst źródłaShadloo, Mostafa Safdari, Amir Zainali i Mehmet Yildiz. "Fluid-Structure Interaction Simulation by Smoothed Particle Hydrodynamics". W ASME 2010 3rd Joint US-European Fluids Engineering Summer Meeting collocated with 8th International Conference on Nanochannels, Microchannels, and Minichannels. ASMEDC, 2010. http://dx.doi.org/10.1115/fedsm-icnmm2010-31137.
Pełny tekst źródłaBorup, Liana, i Alan Parkinson. "Comparison of Four Non-Derivative Optimization Methods on Two Problems Containing Heuristic and Analytic Knowledge". W ASME 1992 Design Technical Conferences. American Society of Mechanical Engineers, 1992. http://dx.doi.org/10.1115/detc1992-0114.
Pełny tekst źródłaVenkataraman, P. "Determining the Ordinary Differential Equation From Noisy Data". W ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/detc2011-47658.
Pełny tekst źródłaXiao, Yichi, Zhe Li, Tianbao Yang i Lijun Zhang. "SVD-free Convex-Concave Approaches for Nuclear Norm Regularization". W Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/436.
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