Готові списки джерел за темами / Adaptive Penalty

Добірка наукової літератури з теми "Adaptive Penalty"

Автор: Grafiati

Опубліковано: 14 березня 2023

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Статті в журналах з теми "Adaptive Penalty"

Zhou, Zhenhua, and Haijun Wu. "Convergence and Quasi-Optimality of an Adaptive Multi-Penalty Discontinuous Galerkin Method." Numerical Mathematics: Theory, Methods and Applications 9, no. 1 (February 2016): 51–86. http://dx.doi.org/10.4208/nmtma.2015.m1412.

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AbstractAn adaptive multi-penalty discontinuous Galerkin method (AMPDG) for the diffusion problem is considered. Convergence and quasi-optimality of the AMPDG are proved. Compared with the analyses for the adaptive finite element method or the adaptive interior penalty discontinuous Galerkin method, extra works are done to overcome the difficulties caused by the additional penalty terms.

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Liu, Yufeng, Hao Helen Zhang, Cheolwoo Park, and Jeongyoun Ahn. "Support vector machines with adaptive penalty." Computational Statistics & Data Analysis 51, no. 12 (August 2007): 6380–94. http://dx.doi.org/10.1016/j.csda.2007.02.006.

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Gosain, Anjana, and Kavita Sachdeva. "Handling Constraints Using Penalty Functions in Materialized View Selection." International Journal of Natural Computing Research 8, no. 2 (April 2019): 1–17. http://dx.doi.org/10.4018/ijncr.2019040101.

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Materialized view selection (MVS) plays a vital role for efficiently making decisions in a data warehouse. This problem is NP-hard and constrained optimization problem. The authors have handled both the space and maintenance cost constraint using penalty functions. Three penalty function methods i.e. static, dynamic and adaptive penalty functions have been used for handling constraints and Backtracking Search Optimization algorithm (BSA) has been used for optimizing the total query processing cost. Experiments were conducted comparing the static, dynamic and adaptive penalty functions on varying the space constraint. The adaptive penalty function method yields the best results in terms of minimum query processing cost and achieves the optimality, scalability and feasibility of the problem on varying the lattice dimensions and on increasing the number of user queries. The authors proposed work has been compared with other evolutionary algorithms i.e. PSO and genetic algorithm and yields better results in terms of minimum total query processing cost of the materialized views.

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La Rivière, Patrick J., Junguo Bian, and Phillip A. Vargas. "Comparison of Quadratic- and Median-Based Roughness Penalties for Penalized-Likelihood Sinogram Restoration in Computed Tomography." International Journal of Biomedical Imaging 2006 (2006): 1–7. http://dx.doi.org/10.1155/ijbi/2006/41380.

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We have compared the performance of two different penalty choices for a penalized-likelihood sinogram-restoration strategy we have been developing. One is a quadratic penalty we have employed previously and the other is a new median-based penalty. We compared the approaches to a noniterative adaptive filter that loosely but not explicitly models data statistics. We found that the two approaches produced similar resolution-variance tradeoffs to each other and that they outperformed the adaptive filter in the low-dose regime, which suggests that the particular choice of penalty in our approach may be less important than the fact that we are explicitly modeling data statistics at all. Since the quadratic penalty allows for derivation of an algorithm that is guaranteed to monotonically increase the penalized-likelihood objective function, we find it to be preferable to the median-based penalty.

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Lambert-Lacroix, Sophie, and Laurent Zwald. "The adaptive BerHu penalty in robust regression." Journal of Nonparametric Statistics 28, no. 3 (June 13, 2016): 487–514. http://dx.doi.org/10.1080/10485252.2016.1190359.

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Marchetti, Yuliya, and Qing Zhou. "Solution path clustering with adaptive concave penalty." Electronic Journal of Statistics 8, no. 1 (2014): 1569–603. http://dx.doi.org/10.1214/14-ejs934.

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Towfic, Zaid J., and Ali H. Sayed. "Adaptive Penalty-Based Distributed Stochastic Convex Optimization." IEEE Transactions on Signal Processing 62, no. 15 (August 2014): 3924–38. http://dx.doi.org/10.1109/tsp.2014.2331615.

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Yu, Xinghuo, and Baolin Wu. "An Adaptive Penalty Function Method for Constrained Optimization with Evolutionary Programming." Journal of Advanced Computational Intelligence and Intelligent Informatics 4, no. 2 (March 20, 2000): 164–70. http://dx.doi.org/10.20965/jaciii.2000.p0164.

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In this paper, we propose a novel adaptive penalty function method for constrained optimization problems using the evolutionary programming technique. This method incorporates an adaptive tuning algorithm that adjusts the penalty parameters according to the population landscape so that it allows fast escape from a local optimum and quick convergence toward a global optimum. The method is simple and computationally effective in the sense that only very few penalty parameters are needed for tuning. Simulation results of five well-known benchmark problems are presented to show the performance of the proposed method.

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YEH, CHANG-CHING, KUEI-CHUNG CHANG, TIEN-FU CHEN, and CHINGWEI YEH. "ADAPTIVE PIPELINE VOLTAGE SCALING IN HIGH PERFORMANCE MICROPROCESSOR." Journal of Circuits, Systems and Computers 19, no. 08 (December 2010): 1817–34. http://dx.doi.org/10.1142/s0218126610007146.

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Deep pipeline has traditionally been widely used in high performance microprocessor. To allow continuous program execution, branch prediction provides a necessary method of speculatively executing instructions without compromising performance. However, branch misprediction penalty significantly impacts the performance of the deep pipeline processor. This study presents a new Adaptive Pipeline Voltage Scaling (APVS) technique to reduce branch misprediction penalty. For a likely mispredicted branch entering the processor, APVS begins increasing voltage and merging deep pipeline whereby shorter pipeline length permits less branch misprediction penalty. Once the branch is resolved, the merged stages are split and the supply voltage is reduced again. With dedicated shorter pipeline length within each branch misprediction, APVS achieves great performance improvement. The evaluation of APVS in a 13-stage superscalar processor with benchmarks from SPEC2000 applications shows a performance improvement (between 3–12%, average 8%) over baseline processor that does not exploit APVS.

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Wang, Yuanxin. "An Adaptive Variational Mode Decomposition Technique with Differential Evolution Algorithm and Its Application Analysis." Shock and Vibration 2021 (November 11, 2021): 1–5. http://dx.doi.org/10.1155/2021/2030128.

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Variational mode decomposition is an adaptive nonrecursive signal decomposition and time-frequency distribution estimation method. The improper selection of the decomposition number will cause under decomposition or over decomposition, and the improper selection of the penalty factor will affect the bandwidth of modal components, so it is very necessary to look for the optimal parameter combination of the decomposition number and the penalty factor of variational mode decomposition. Hence, differential evolution algorithm is used to look for the optimization combination of the decomposition number and the penalty factor of variational mode decomposition because differential evolution algorithm has a good ability at global searching. The method is called adaptive variational mode decomposition technique with differential evolution algorithm. Application analysis and discussion of the adaptive variational mode decomposition technique with differential evolution algorithm are employed by combining with the experiment. The conclusions of the experiment are that the decomposition performance of the adaptive variational mode decomposition technique with differential evolution algorithm is better than that of variational mode decomposition.

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Більше джерел

Дисертації з теми "Adaptive Penalty"

Li, Jonathan Chi Fai. "Eye closure penalty based signal quality metric for intelligent all-optical networks /." Connect to thesis, 2009. http://repository.unimelb.edu.au/10187/7047.

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Sarkar, Abhishek. "The Gambler's Fallacy and Hot Outcome: Cognitive Biases or Adaptive Thinking for Goalkeepers' Decisions on Dive Direction During Penalty Shootouts." Bowling Green State University / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1483529030818181.

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Svensson, Daniel. "Generaliseringsförmåga vid genetisk programmering." Thesis, University of Skövde, Department of Computer Science, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-789.

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I detta arbete undersöks hur bestraffningsmetoder för att bestraffa storleken på GP-program påverkar generaliseringsförmågan. Arbetet grundar sig på ett arbete som Cavaretta och Chellapilla gjort, där de undersöker skillnaden i generaliseringsförmåga mellan bestraffningsmetoden ”Complexity Penalty functions” och ingen bestraffningsmetod.

I detta arbete har nya experiment gjorts med ”Complexity Penalty functions” och ”Adaptive parsimony pressure”, som är en annan bestraffningsmetod. Dessa bestraffningsmetoder har undersökts i samma domän som Cavaretta och Chellapilla och ytterligare i en domän för att ge en bättre bild av hur de generaliserar.

I arbetet visar det sig att användningen av någon av bestraffningsmetoderna ”Complexity Penalty functions” och ”Adaptive parsimony pressure” oftast ger bättre generaliseringsförmåga hos GP-program. Detta motsäger det Cavaretta och Chellapilla kommer fram till i sitt arbete. ”Adaptive parsimony pressure” verkar också vara bättre på att generalisera än ”Complexity Penalty functions”.

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Yeh, Kuo-Chih, and 葉國智. "Differential Evolution Algorithm with Adaptive Penalty for Constrained Continuous Global Optimization." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/78691247950621499584.

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碩士
元智大學
工業工程與管理學系
95
The applications of Metaheuristic algorithms that used to solve optimization problems in researches are very popular, but most of them were generally for unconstrained optimization procedures. However, these normally exiting problems in real-world are always under constrained. This paper presents a differential-evolution-type algorithm for solving constrained continuous optimization problems. The proposed differential evolution (DE) algorithm is developed based upon the penalty function approach, where constraint violation is penalized by placing the constraints into the objective function. Penalty functions can deal both with equality and inequality constraints; in this study, equality constraints are transformed into inequality ones. In addition, to handle infeasibility during DE search, a random re-initialization procedure is executed to produce a new potential solution inside the allowable ranges. Three different types of increasing penalty factors and one adaptive penalty that adjust by constraint violations are compared for their performance on convergence. A dynamic tolerance allowed of equality constraints is executed, too. The performance measure includes the best objective value achieved and the number of function evaluations required. The recommendation for the selection of parameter setting in the new algorithm is given through a series of simulation optimizations and analysis by the design of experiments (DOE). The experimental results obtained by solving a variety of benchmark functions are used to demonstrate the effectiveness and efficiency of the penalty-function DE algorithm.

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Joung, Ming-Huei, and 鐘銘輝. "Research and Programming of Constrained Optimization Problem by Penalty Function and Adaptive Lagrange Function." Thesis, 1993. http://ndltd.ncl.edu.tw/handle/02081783409800506940.

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博士
國立臺灣大學
土木工程研究所
81
In this paper, four new optimization methods are presented. First, Bezier Curve is used as line-searching direction. Second, Hyperbola is used in optima fitting. Third, a new penalty function is proposed in Penalty Method. Fourth, Adaptive Lagrange Function is proposed to solve constrained opimization problem. With the good behavior of being defined everywhere, accurate and differentiable, the Ideal Penalty Function can be used in all of the optimization methods. In the Adaptive Lagrange Method, a quadratic term is added to the saddle point of the original Lagrange function, and the Hessian matrix of the adaptive function will be positive definite. After that, the line-searching method can be used to search the optimal point, and only one unconstrained optimization iteration is required. The program based on both the new methods of this paper and the common used methods is set up with the characteristics of effectiveness, friendliness and completeness. Furthermore, the linkage program of engineering package and the optimization program is included and the user could set up the optimization package without being interfered in the overall engineering program. Several numerical examples are provided to illustrate the he package.

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Garg, Vikram Vinod 1985. "Coupled flow systems, adjoint techniques and uncertainty quantification." Thesis, 2012. http://hdl.handle.net/2152/ETD-UT-2012-08-6034.

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Coupled systems are ubiquitous in modern engineering and science. Such systems can encompass fluid dynamics, structural mechanics, chemical species transport and electrostatic effects among other components, all of which can be coupled in many different ways. In addition, such models are usually multiscale, making their numerical simulation challenging, and necessitating the use of adaptive modeling techniques. The multiscale, multiphysics models of electrosomotic flow (EOF) constitute a particularly challenging coupled flow system. A special feature of such models is that the coupling between the electric physics and hydrodynamics is via the boundary. Numerical simulations of coupled systems are typically targeted towards specific Quantities of Interest (QoIs). Adjoint-based approaches offer the possibility of QoI targeted adaptive mesh refinement and efficient parameter sensitivity analysis. The formulation of appropriate adjoint problems for EOF models is particularly challenging, due to the coupling of physics via the boundary as opposed to the interior of the domain. The well-posedness of the adjoint problem for such models is also non-trivial. One contribution of this dissertation is the derivation of an appropriate adjoint problem for slip EOF models, and the development of penalty-based, adjoint-consistent variational formulations of these models. We demonstrate the use of these formulations in the simulation of EOF flows in straight and T-shaped microchannels, in conjunction with goal-oriented mesh refinement and adjoint sensitivity analysis. Complex computational models may exhibit uncertain behavior due to various reasons, ranging from uncertainty in experimentally measured model parameters to imperfections in device geometry. The last decade has seen a growing interest in the field of Uncertainty Quantification (UQ), which seeks to determine the effect of input uncertainties on the system QoIs. Monte Carlo methods remain a popular computational approach for UQ due to their ease of use and "embarassingly parallel" nature. However, a major drawback of such methods is their slow convergence rate. The second contribution of this work is the introduction of a new Monte Carlo method which utilizes local sensitivity information to build accurate surrogate models. This new method, called the Local Sensitivity Derivative Enhanced Monte Carlo (LSDEMC) method can converge at a faster rate than plain Monte Carlo, especially for problems with a low to moderate number of uncertain parameters. Adjoint-based sensitivity analysis methods enable the computation of sensitivity derivatives at virtually no extra cost after the forward solve. Thus, the LSDEMC method, in conjuction with adjoint sensitivity derivative techniques can offer a robust and efficient alternative for UQ of complex systems. The efficiency of Monte Carlo methods can be further enhanced by using stratified sampling schemes such as Latin Hypercube Sampling (LHS). However, the non-incremental nature of LHS has been identified as one of the main obstacles in its application to certain classes of complex physical systems. Current incremental LHS strategies restrict the user to at least doubling the size of an existing LHS set to retain the convergence properties of LHS. The third contribution of this research is the development of a new Hierachical LHS algorithm, that creates designs which can be used to perform LHS studies in a more flexibly incremental setting, taking a step towards adaptive LHS methods.
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Книги з теми "Adaptive Penalty"

Gordon, Gregory S. Adopting Incitement to Commit War Crimes. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780190612689.003.0011.

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Chapter 10 proposes a new atrocity speech offense—incitement to commit war crimes. It demonstrates that any imagined ills related to this proposed expansion of speech criminalization in the military context-- from supposed chilling effects to drags on operational efficiency – are easily outweighed by the salutary impact of wider proscription. The chapter contends that exposing intraforce military relations to the specter of greater verbal regulation will promote law of armed conflict (LOAC) compliance and esprit de corps, thereby ultimately enhancing broader functional objectives. It also explains why the proposal’s timing is propitious. War weapons have become more lethal and war tactics more savage. And the incitement offense has fossilized as a penal option within the narrow target-crime confines of genocide. As the international legal imagination has begun to visualize its utility in relation to other global crimes, notably terrorism, its adaption for LOAC violation purposes seems prudently incremental and normatively sound.

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Частини книг з теми "Adaptive Penalty"

Huerta-Amante, Daniel Ángel, and Hugo Terashima-Marín. "Adaptive Penalty Weights When Solving Congress Timetabling." In Advances in Artificial Intelligence – IBERAMIA 2004, 144–53. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30498-2_15.

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Barbosa, Helio J. C., and Afonso C. C. Lemonge. "An Adaptive Penalty Scheme for Steady-State Genetic Algorithms." In Genetic and Evolutionary Computation — GECCO 2003, 718–29. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-45105-6_87.

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Boon, W. M., and J. M. Nordbotten. "An Adaptive Penalty Method for Inequality Constrained Minimization Problems." In Lecture Notes in Computational Science and Engineering, 155–64. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-55874-1_14.

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Gao, Lei, and William F. Rosenberger. "Adaptive Bayesian Design with Penalty Based on Toxicity-Efficacy Response." In Contributions to Statistics, 91–98. Heidelberg: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-00218-7_11.

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Barbosa, Helio J. C., Afonso C. C. Lemonge, and Heder S. Bernardino. "A Critical Review of Adaptive Penalty Techniques in Evolutionary Computation." In Infosys Science Foundation Series, 1–27. New Delhi: Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-2184-5_1.

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Kale, Ishaan R., and Anand J. Kulkarni. "Constraint Handling Using the Self-Adaptive Penalty Function (SAPF) Approach." In Constraint Handling in Cohort Intelligence Algorithm, 49–59. New York: CRC Press, 2021. http://dx.doi.org/10.1201/9781003245193-4.

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Ross, Peter, and Emma Hart. "An adaptive mutation scheme for a penalty-based graph-colouring GA." In Lecture Notes in Computer Science, 795–802. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0056921.

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Yen, Gary G. "An Adaptive Penalty Function for Handling Constraint in Multi-objective Evolutionary Optimization." In Constraint-Handling in Evolutionary Optimization, 121–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00619-7_6.

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Chenggang, Cui, Yang Xiaofei, and Gao Tingyu. "A Self-adaptive Interior Penalty Based Differential Evolution Algorithm for Constrained Optimization." In Lecture Notes in Computer Science, 309–18. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11897-0_37.

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Chen, Wentao, and Fei Han. "An Improved Multi-objective Particle Swarm Optimization with Adaptive Penalty Value for Feature Selection." In Communications in Computer and Information Science, 649–61. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3425-6_51.

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Тези доповідей конференцій з теми "Adaptive Penalty"

Fathy, Mohammed, and Michael Rotkowitz. "Essential Matrix Estimation Using Adaptive Penalty Formulations." In British Machine Vision Conference 2014. British Machine Vision Association, 2014. http://dx.doi.org/10.5244/c.28.50.

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Nunez-Martinez, Jose, and Josep Mangues-Bafalluy. "Distributed Lyapunov drift-plus-penalty routing for WiFi mesh networks with adaptive penalty weight." In 2012 IEEE Thirteenth International Symposium on "A World of Wireless, Mobile and Multimedia Networks" (WoWMoM). IEEE, 2012. http://dx.doi.org/10.1109/wowmom.2012.6263779.

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Takahama, Tetsuyuki, and Setsuko Sakai. "An Equivalent Penalty Coefficient Method: An Adaptive Penalty Approach for Population-Based Constrained Optimization." In 2019 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2019. http://dx.doi.org/10.1109/cec.2019.8790360.

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Kronvall, Ted, Filip Elvander, Stefan Ingi Adalbjornsson, and Andreas Jakobsson. "An adaptive penalty approach to multi-pitch estimation." In 2015 23rd European Signal Processing Conference (EUSIPCO). IEEE, 2015. http://dx.doi.org/10.1109/eusipco.2015.7362339.

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Xinchao Li, Qianhua He, Yanxiong Li, Changbin Li, and Zhingfeng Wang. "An adaptive premium penalty ant colony optimization algorithm." In 2013 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2013. http://dx.doi.org/10.1109/icmlc.2013.6890509.

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Wang, Shiyuan, Yunfei Zheng, and Shukai Duan. "Sparse Huber adaptive filter with correntropy induced metric penalty." In 2016 35th Chinese Control Conference (CCC). IEEE, 2016. http://dx.doi.org/10.1109/chicc.2016.7554125.

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Alaboudi, Dheyaa, and Ali Alkenani. "Sparse sliced inverse regression based on adaptive lasso penalty." In PROCEEDING OF THE 1ST INTERNATIONAL CONFERENCE ON ADVANCED RESEARCH IN PURE AND APPLIED SCIENCE (ICARPAS2021): Third Annual Conference of Al-Muthanna University/College of Science. AIP Publishing, 2022. http://dx.doi.org/10.1063/5.0093717.

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Djebedjian, Berge, Ashraf Yaseen, and Magdy Abou Rayan. "A New Adaptive Penalty Method for Constrained Genetic Algorithm and Its Application to Water Distribution Systems." In 2006 International Pipeline Conference. ASMEDC, 2006. http://dx.doi.org/10.1115/ipc2006-10235.

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This paper presents a new adaptive penalty method for genetic algorithms (GA). External penalty functions have been used to convert a constrained optimization problem into an unconstrained problem for GA-based optimization. The success of the genetic algorithm application to the design of water distribution systems depends on the choice of the penalty function. The optimal design of water distribution systems is a constrained non-linear optimization problem. Constraints (for example, the minimum pressure requirements at the nodes) are generally handled within genetic algorithm optimization by introducing a penalty cost function. The optimal solution is found when the pressures at some nodes are close to the minimum required pressure. The goal of an adaptive penalty function is to change the value of the penalty draw-down coefficient during the search allowing exploration of infeasible regions to find optimal building blocks, while preserving the feasibility of the final solution. In this study, a new penalty coefficient strategy is assumed to increase with the total cost at each generation and inversely with the total number of nodes. The application of the computer program to case studies shows that it finds the least cost in a favorable number of function evaluations if not less than that in previous studies and it is computationally much faster when compared with other studies.

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Kramer, Oliver, Uli Schlachter, and Valentin Spreckels. "An adaptive penalty function with meta-modeling for constrained problems." In 2013 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2013. http://dx.doi.org/10.1109/cec.2013.6557721.

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Vilaça, Rita, and Ana Maria A. C. Rocha. "An adaptive penalty method for DIRECT algorithm in engineering optimization." In NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2012: International Conference of Numerical Analysis and Applied Mathematics. AIP, 2012. http://dx.doi.org/10.1063/1.4756265.

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Звіти організацій з теми "Adaptive Penalty"

Multiple Engine Faults Detection Using Variational Mode Decomposition and GA-K-means. SAE International, March 2022. http://dx.doi.org/10.4271/2022-01-0616.

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As a critical power source, the diesel engine is widely used in various situations. Diesel engine failure may lead to serious property losses and even accidents. Fault detection can improve the safety of diesel engines and reduce economic loss. Surface vibration signal is often used in non-disassembly fault diagnosis because of its convenient measurement and stability. This paper proposed a novel method for engine fault detection based on vibration signals using variational mode decomposition (VMD), K-means, and genetic algorithm. The mode number of VMD dramatically affects the accuracy of extracting signal components. Therefore, a method based on spectral energy distribution is proposed to determine the parameter, and the quadratic penalty term is optimized according to SNR. The results show that the optimized VMD can adaptively extract the vibration signal components of the diesel engine. In the actual fault diagnosis case, it is difficult to obtain the data with labels. The clustering algorithm can complete the classification without labeled data, but it is limited by the low accuracy. In this paper, the optimized VMD is used to decompose and standardize the vibration signal. Then the correlation-based feature selection method is implemented to obtain the feature results after dimensionality reduction. Finally, the results are input into the classifier combined by K-means and genetic algorithm (GA). By introducing and optimizing the genetic algorithm, the number of classes can be selected automatically, and the accuracy is significantly improved. This method can carry out adaptive multiple fault detection of a diesel engine without labeled data. Compared with many supervised learning algorithms, the proposed method also has high accuracy.

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