Gotowe bibliografie tematyczne / Smoothed functional algorithms

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  1. Artykuły w czasopismach
  2. Rozprawy doktorskie
  3. Książki
  4. Części książek
  5. Streszczenia konferencji

Gotowa bibliografia na temat „Smoothed functional algorithms”

Autor: Grafiati
Data publikacji: 6 września 2023

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Artykuły w czasopismach na temat "Smoothed functional algorithms"

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Bhatnagar, Shalabh. "Adaptive Newton-based multivariate smoothed functional algorithms for simulation optimization." ACM Transactions on Modeling and Computer Simulation 18, no. 1 (2007): 1–35. http://dx.doi.org/10.1145/1315575.1315577.

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Bhatnagar, Shalabh, and Vivek S. Borkar. "Multiscale Chaotic SPSA and Smoothed Functional Algorithms for Simulation Optimization." SIMULATION 79, no. 10 (2003): 568–80. http://dx.doi.org/10.1177/0037549703039988.

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Ghoshdastidar, Debarghya, Ambedkar Dukkipati, and Shalabh Bhatnagar. "Smoothed Functional Algorithms for Stochastic Optimization Using q -Gaussian Distributions." ACM Transactions on Modeling and Computer Simulation 24, no. 3 (2014): 1–26. http://dx.doi.org/10.1145/2628434.

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Ghoshdastidar, Debarghya, Ambedkar Dukkipati, and Shalabh Bhatnagar. "Newton-based stochastic optimization using q-Gaussian smoothed functional algorithms." Automatica 50, no. 10 (2014): 2606–14. http://dx.doi.org/10.1016/j.automatica.2014.08.021.

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Prasad, H. L., L. A. Prashanth, Shalabh Bhatnagar, and Nirmit Desai. "Adaptive Smoothed Functional Algorithms for Optimal Staffing Levels in Service Systems." Service Science 5, no. 1 (2013): 29–55. http://dx.doi.org/10.1287/serv.1120.0035.

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Lakshmanan, K., and Shalabh Bhatnagar. "Quasi-Newton smoothed functional algorithms for unconstrained and constrained simulation optimization." Computational Optimization and Applications 66, no. 3 (2016): 533–56. http://dx.doi.org/10.1007/s10589-016-9875-4.

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NOROUZZADEH, P., B. RAHMANI, and M. S. NOROUZZADEH. "FORECASTING SMOOTHED NON-STATIONARY TIME SERIES USING GENETIC ALGORITHMS." International Journal of Modern Physics C 18, no. 06 (2007): 1071–86. http://dx.doi.org/10.1142/s0129183107011133.

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We introduce kernel smoothing method to extract the global trend of a time series and remove short time scales variations and fluctuations from it. A multifractal detrended fluctuation analysis (MF-DFA) shows that the multifractality nature of TEPIX returns time series is due to both fatness of the probability density function of returns and long range correlations between them. MF-DFA results help us to understand how genetic algorithm and kernel smoothing methods act. Then we utilize a recently developed genetic algorithm for carrying out successful forecasts of the trend in financial time s
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Kostjukov, V. A., M. Y. Medvedev, and V. Kh Pshikhopov. "Algorithms for Planning Smoothed Individual Trajectories of Ground Robots." Mekhatronika, Avtomatizatsiya, Upravlenie 23, no. 11 (2022): 585–95. http://dx.doi.org/10.17587/mau.23.585-595.

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The article is devoted to the development of an algorithm for constructing the trajectory of a robotic platform moving in an environment with obstacles. This algorithm is based on the application of a special local optimization procedure at each planning step and allows us to obtain feasible program trajectories without increasing the computational complexity of algorithms compared to existing methods. The algorithm is based on the application of the improved method of potential fields and subsequent smoothing of the resulting trajectory. The improving of the potential field method consists in
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Vijayan, Nithia, and Prashanth L.A. "Smoothed functional-based gradient algorithms for off-policy reinforcement learning: A non-asymptotic viewpoint." Systems & Control Letters 155 (September 2021): 104988. http://dx.doi.org/10.1016/j.sysconle.2021.104988.

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Cheng, Chuen-Sheng, Pei-Wen Chen, and Yu-Tang Wu. "Phase I Analysis of Nonlinear Profiles Using Anomaly Detection Techniques." Applied Sciences 13, no. 4 (2023): 2147. http://dx.doi.org/10.3390/app13042147.

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In various industries, the process or product quality is evaluated by a functional relationship between a dependent variable y and one or a few input variables x, expressed as y=fx. This relationship is called a profile in the literature. Recently, profile monitoring has received a lot of research attention. In this study, we formulated profile monitoring as an anomaly-detection problem and proposed an outlier-detection procedure for phase I nonlinear profile analysis. The developed procedure consists of three key processes. First, we obtained smoothed nonlinear profiles using the spline smoot
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Rozprawy doktorskie na temat "Smoothed functional algorithms"

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Sällberg, Gustav, and 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.

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The master thesis is focused on how a local volatility surfaces can be extracted by optimization with respectto smoothness and price error. The pricing is based on utility based pricing, and developed to be set in arisk neutral pricing setting. The pricing is done in a discrete multinomial recombining tree, where the timeand price increments optionally can be equidistant. An interpolation algorithm is used if the option that shallbe priced is not matched in the tree discretization. Power utility functions are utilized, where the log-utilitypreference is especially studied, which coincides with
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Barajas, 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.

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The topic of this dissertation is the derivation, development, and evaluation of novel hybrid algorithms for process control that use a limited number of measurements and that are suitable to operate in the presence of large amounts of process noise. As an initial step, affine and neural network statistical process models are developed in order to simulate the steady-state system behavior. Such models are vitally important in the evaluation, testing, and improvement of all other process controllers referred to in this work. Afterwards, fuzzy logic controller rules are assimilated into a mathe
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Vestin, Albin, and 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.

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Today, the main research field for the automotive industry is to find solutions for active safety. In order to perceive the surrounding environment, tracking nearby traffic objects plays an important role. Validation of the tracking performance is often done in staged traffic scenarios, where additional sensors, mounted on the vehicles, are used to obtain their true positions and velocities. The difficulty of evaluating the tracking performance complicates its development. An alternative approach studied in this thesis, is to record sequences and use non-causal algorithms, such as smoothing, i
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Lakshmanan, K. "Online Learning and Simulation Based Algorithms for Stochastic Optimization." Thesis, 2012. http://hdl.handle.net/2005/3245.

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In many optimization problems, the relationship between the objective and parameters is not known. The objective function itself may be stochastic such as a long-run average over some random cost samples. In such cases finding the gradient of the objective is not possible. It is in this setting that stochastic approximation algorithms are used. These algorithms use some estimates of the gradient and are stochastic in nature. Amongst gradient estimation techniques, Simultaneous Perturbation Stochastic Approximation (SPSA) and Smoothed Functional(SF) scheme are widely used. In this thesis we hav
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Książki na temat "Smoothed functional algorithms"

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Moerder, Daniel D. Constrained minimization of smooth functions using a genetic algorithm. National Aeronautics and Space Administration, Langley Research Center, 1994.

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N, Pamadi Bandu, and Langley Research Center, eds. Constrained minimization of smooth functions using a genetic algorithm. National Aeronautics and Space Administration, Langley Research Center, 1994.

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Fitting Smooth Functions to Data. American Mathematical Society, 2020.

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Części książek na temat "Smoothed functional algorithms"

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Bhatnagar, S., H. Prasad, and L. Prashanth. "Smoothed Functional Gradient Schemes." In Stochastic Recursive Algorithms for Optimization. Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-4285-0_6.

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Bhatnagar, S., H. Prasad, and L. Prashanth. "Newton-Based Smoothed Functional Algorithms." In Stochastic Recursive Algorithms for Optimization. Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-4285-0_8.

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Lakshmanan, K., and Shalabh Bhatnagar. "Smoothed Functional and Quasi-Newton Algorithms for Routing in Multi-stage Queueing Network with Constraints." In Distributed Computing and Internet Technology. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19056-8_12.

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Bläser, Markus, Bodo Manthey, and B. V. Raghavendra Rao. "Smoothed Analysis of Partitioning Algorithms for Euclidean Functionals." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22300-6_10.

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J. Zaslavski, Alexander. "Gradient Algorithm with a Smooth Objective Function." In Convex Optimization with Computational Errors. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-37822-6_4.

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Zaslavski, Alexander J. "Gradient Algorithm with a Smooth Objective Function." In Springer Optimization and Its Applications. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-30921-7_4.

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Mok, RenHao, and Mohd Ashraf Ahmad. "Power Production Optimization of Model-Free Wind Farm Using Smoothed Functional Algorithm." In Lecture Notes in Electrical Engineering. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8690-0_60.

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Giesl, Peter. "Towards Calculating the Basin of Attraction of Non-Smooth Dynamical Systems Using Radial Basis Functions." In Approximation Algorithms for Complex Systems. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16876-5_9.

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Cruttwell, Geoffrey S. H., Bruno Gavranović, Neil Ghani, Paul Wilson, and Fabio Zanasi. "Categorical Foundations of Gradient-Based Learning." In Programming Languages and Systems. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-99336-8_1.

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AbstractWe propose a categorical semantics of gradient-based machine learning algorithms in terms of lenses, parametric maps, and reverse derivative categories. This foundation provides a powerful explanatory and unifying framework: it encompasses a variety of gradient descent algorithms such as ADAM, AdaGrad, and Nesterov momentum, as well as a variety of loss functions such as MSE and Softmax cross-entropy, shedding new light on their similarities and differences. Our approach to gradient-based learning has examples generalising beyond the familiar continuous domains (modelled in categories of smooth maps) and can be realized in the discrete setting of boolean circuits. Finally, we demonstrate the practical significance of our framework with an implementation in Python.
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Bredies, Kristian. "Recovering Piecewise Smooth Multichannel Images by Minimization of Convex Functionals with Total Generalized Variation Penalty." In Efficient Algorithms for Global Optimization Methods in Computer Vision. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54774-4_3.

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Streszczenia konferencji na temat "Smoothed functional algorithms"

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Ghoshdastidar, Debarghya, Ambedkar Dukkipati, and Shalabh Bhatnagar. "q-Gaussian based Smoothed Functional algorithms for stochastic optimization." In 2012 IEEE International Symposium on Information Theory - ISIT. IEEE, 2012. http://dx.doi.org/10.1109/isit.2012.6283013.

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Segeth, Karel. "Multivariate smooth interpolation that employs polyharmonic functions." In Programs and Algorithms of Numerical Mathematics 19. Institute of Mathematics, Czech Academy of Sciences, 2019. http://dx.doi.org/10.21136/panm.2018.15.

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Khamisov, O. V. "Optimization with quadratic support functions in nonconvex smooth optimization." In 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.

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Mohanty, Amit, and Bin Yao. "Indirect Adaptive Robust Control of Uncertain Systems With Unknown Asymmetric Input Deadband Using a Smooth Inverse." In ASME 2009 Dynamic Systems and Control Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/dscc2009-2771.

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In this paper, we present an indirect adaptive robust controller (IARC) for output tracking of a class of uncertain nonlinear systems with unknown input asymmetric deadband in presence of uncertain nonlinearities and parametric uncertainties. Most of the parameter adaptation algorithms, such as, gradient-type and least squares-type require that the unknown parameters of a system appear in affine with known regressor functions globally. However, deadband nonlinearity can not be represented in those global linear parametric form. Therefore, the existing parameter estimation algorithms for deadba
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Juan Geng, Lai-Sheng Wang, Ai-Min Fu, and Qi-Qing Song. "A smoothed rank function algorithm based Hyperbolic Tangent function for matrix completion." In 2012 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2012. http://dx.doi.org/10.1109/icmlc.2012.6359558.

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Zhifu Cui, Hang Zhang, and Wei Lu. "An improved smoothed l0-norm algorithm based on multiparameter approximation function." In 2010 12th IEEE International Conference on Communication Technology (ICCT). IEEE, 2010. http://dx.doi.org/10.1109/icct.2010.5688553.

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Shadloo, Mostafa Safdari, Amir Zainali, and Mehmet Yildiz. "Fluid-Structure Interaction Simulation by Smoothed Particle Hydrodynamics." In 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.

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In this article, a modified SPH algorithm is proposed to solve Fluid-Structure Interaction (FSI) problems including fluid flow in interaction with compatible structures under a large deformation. To validate the current algorithm against available data in literature, we consider two important benchmark cases; namely, an oscillating elastic beam and dam breaking problems. The proposed algorithm is based on the elimination of the intermediate data transfer steps between the fluid and the solid structures, whereby resulting in an easy and time-saving simulation method. With the test application s
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Borup, Liana, and Alan Parkinson. "Comparison of Four Non-Derivative Optimization Methods on Two Problems Containing Heuristic and Analytic Knowledge." In ASME 1992 Design Technical Conferences. American Society of Mechanical Engineers, 1992. http://dx.doi.org/10.1115/detc1992-0114.

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Abstract Hybrid engineering models — models that combine both analytic and heuristic knowledge — can address a wide range of design issues. However, models that combine these two types of information are often non-smooth and thus cannot be optimized by traditional derivative-based optimization algorithms. This paper is a study of four non-derivative optimization methods: Simulated Annealing, Genetic Algorithms, Flexible Polyhedron Search, and Function Approximation. Each algorithm was tested on two engineering problems that combine heuristic and analytic information. The algorithms were then e
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Venkataraman, P. "Determining the Ordinary Differential Equation From Noisy Data." In ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/detc2011-47658.

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A challenging inverse problem is to identify the smooth function and the differential equation it represents from uncertain data. This paper extends the procedure previously developed for smooth data. The approach involves two steps. In the first step the data is smoothed using a recursive Bezier filter. For smooth data a single application of the filter is sufficient. The final set of data points provides a smooth estimate of the solution. More importantly, it will also identify smooth derivatives of the function away from the edges of the domain. In the second step the values of the function
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Xiao, Yichi, Zhe Li, Tianbao Yang, and Lijun Zhang. "SVD-free Convex-Concave Approaches for Nuclear Norm Regularization." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/436.

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Minimizing a convex function of matrices regularized by the nuclear norm arises in many applications such as collaborative filtering and multi-task learning. In this paper, we study the general setting where the convex function could be non-smooth. When the size of the data matrix, denoted by m x n, is very large, existing optimization methods are inefficient because in each iteration, they need to perform a singular value decomposition (SVD) which takes O(m^2 n) time. To reduce the computation cost, we exploit the dual characterization of the nuclear norm to introduce a convex-concave optimiz
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