Relevant bibliographies by topics / Penalty function with memory

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
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Academic literature on the topic 'Penalty function with memory'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Penalty function with memory"

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Han, Jungmin, Seong-Hee Kim, and Chuljin Park. "Improved Penalty Function with Memory for Stochastically Constrained Optimization via Simulation." ACM Transactions on Modeling and Computer Simulation 31, no. 4 (October 31, 2021): 1–26. http://dx.doi.org/10.1145/3465333.

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Penalty function with memory (PFM) in Park and Kim [2015] is proposed for discrete optimization via simulation problems with multiple stochastic constraints where performance measures of both an objective and constraints can be estimated only by stochastic simulation. The original PFM is shown to perform well, finding a true best feasible solution with a higher probability than other competitors even when constraints are tight or near-tight. However, PFM applies simple budget allocation rules (e.g., assigning an equal number of additional observations) to solutions sampled at each search iteration and uses a rather complicated penalty sequence with several user-specified parameters. In this article, we propose an improved version of PFM, namely IPFM, which can combine the PFM with any simulation budget allocation procedure that satisfies some conditions within a general DOvS framework. We present a version of a simulation budget allocation procedure useful for IPFM and introduce a new penalty sequence, namely PS 2 + , which is simpler than the original penalty sequence yet holds convergence properties within IPFM with better finite-sample performances. Asymptotic convergence properties of IPFM with PS 2 + are proved. Our numerical results show that the proposed method greatly improves both efficiency and accuracy compared to the original PFM.
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Park, Chuljin, and Seong-Hee Kim. "Penalty Function with Memory for Discrete Optimization via Simulation with Stochastic Constraints." Operations Research 63, no. 5 (October 2015): 1195–212. http://dx.doi.org/10.1287/opre.2015.1417.

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Watanabe, Takuji, Kazuteru Miyazaki, and Hiroaki Kobayashi. "A New Improved Penalty Avoiding Rational Policy Making Algorithm for Keepaway with Continuous State Spaces." Journal of Advanced Computational Intelligence and Intelligent Informatics 13, no. 6 (November 20, 2009): 675–82. http://dx.doi.org/10.20965/jaciii.2009.p0675.

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The penalty avoiding rational policy making algorithm (PARP) [1] previously improved to save memory and cope with uncertainty, i.e., IPARP [2], requires that states be discretized in real environments with continuous state spaces, using function approximation or some other method. Especially, in PARP, a method that discretizes state using a basis functions is known [3]. Because this creates a new basis function based on the current input and its next observation, however, an unsuitable basis function may be generated in some asynchronous multiagent environments. We therefore propose a uniform basis function and range extent of the basis function is estimated before learning. We show the effectiveness of our proposal using a soccer game task called “Keepaway.”
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Avdeev, Dmitry, and Anna Avdeeva. "3D magnetotelluric inversion using a limited-memory quasi-Newton optimization." GEOPHYSICS 74, no. 3 (May 2009): F45—F57. http://dx.doi.org/10.1190/1.3114023.

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The limited-memory quasi-Newton method with simple bounds is used to develop a novel, fully 3D magnetotelluric (MT) inversion technique. This nonlinear inversion is based on iterative minimization of a classical Tikhonov regularized penalty function. However, instead of the usual model space of log resistivities, the approach iterates in a model space with simple bounds imposed on the conductivities of the 3D target. The method requires storage proportional to [Formula: see text], where [Formula: see text] is the number of conductivities to be recovered and [Formula: see text] is the number of correction pairs (practically, only a few). These requirements are much less than those imposed by other Newton methods, which usually require storage proportional to [Formula: see text] or [Formula: see text], where [Formula: see text] is the number of data to be inverted. The derivatives of the penalty function are calculated using an adjoint method based on electromagnetic field reciprocity. The inversion involves all four entries of the MT impedance matrix; the [Formula: see text] integral equation forward-modeling code is used as an engine for this inversion. Convergence, performance, and accuracy of the inversion are demonstrated on synthetic numerical examples. After investigating erratic resistivities in the upper part of the model obtained for one of the examples, we conclude that the standard Tikhonov regularization is not enough to provide consistently smooth underground structures. An additional regularization helps to overcome the problem.
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Zhou, Huiyu, Shingo Mabu, Wei Wei, Kaoru Shimada, and Kotaro Hirasawa. "Traffic Flow Prediction with Genetic Network Programming (GNP)." Journal of Advanced Computational Intelligence and Intelligent Informatics 13, no. 6 (November 20, 2009): 713–25. http://dx.doi.org/10.20965/jaciii.2009.p0713.

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In this paper, a method for traffic flow prediction has been proposed to obtain prediction rules from the past traffic data using Genetic Network Programming (GNP). GNP is an evolutionary approach which can evolve itself and find the optimal solutions. It has been clarified that GNP works well especially in dynamic environments since GNP is consisted of directed graph structures, creates quite compact programs and has an implicit memory function. In this paper, GNP is applied to create a traffic flow prediction model. And we proposed the spatial adjacency model for the prediction and two kinds of models forN-step prediction. Additionally, the adaptive penalty functions are adopted for the fitness function in order to alleviate the infeasible solutions containing loops in the training process. Furthermore, the sharing function is also used to avoid the premature convergence.
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Campo, Alexandre, Stamatios C. Nicolis, and Jean-Louis Deneubourg. "Collective Memory: Transposing Pavlov’s Experiment to Robot Swarms." Applied Sciences 11, no. 6 (March 16, 2021): 2632. http://dx.doi.org/10.3390/app11062632.

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Remembering information is a fundamental aspect of cognition present in numerous natural systems. It allows adaptation of the behavior as a function of previously encountered situations. For instance, many living organisms use memory to recall if a given situation incurred a penalty or a reward and rely on that information to avoid or reproduce that situation. In groups, memory is commonly studied in the case where individual members are themselves capable of learning and a few of them hold pieces of information that can be later retrieved for the benefits of the group. Here, we investigate how a group may display memory when the individual members have reactive behaviors and can not learn any information. The well known conditioning experiments of Pavlov illustrate how single animals can memorize stimuli associated with a reward and later trigger a related behavioral response even in the absence of reward. To study and demonstrate collective memory in artificial systems, we get inspiration from the Pavlov experiments and propose a setup tailored for testing our robotic swarm. We devised a novel behavior based on the fundamental process of aggregation with which robots exhibit collective memory. We show that the group is capable of encoding, storing, and retrieving information that is not present at the level of the individuals.
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Chin, Scott Y. L., Clarence S. P. Lee, and Steven J. E. Wilton. "On the Power Dissipation of Embedded Memory Blocks Used to Implement Logic in Field-Programmable Gate Arrays." International Journal of Reconfigurable Computing 2008 (2008): 1–13. http://dx.doi.org/10.1155/2008/751863.

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We investigate the power and energy implications of using embedded FPGA memory blocks to implement logic. Previous studies have shown that this technique provides extremely dense implementations of some types of logic circuits, however, these previous studies did not evaluate the impact on power. In this paper, we measure the effects on power and energy as a function of three architectural parameters: the number of available memory blocks, the size of the memory blocks, and the flexibility of the memory blocks. We show that although embedded memories provide area efficient implementations of many circuits, this technique results in additional power consumption. We also show that blocks containing smaller-memory arrays are more power efficient than those containing large arrays, but for most array sizes, the memory blocks should be as flexible as possible. Finally, we show that by combining physical arrays into larger logical memories, and mapping logic in such a way that some physical arrays can be disabled on each access, can reduce the power consumption penalty. The results were obtained from place and routed circuits using standard experimental physical design tools and a detailed power model. Several results were also verified through current measurements on a 0.13 μm CMOS FPGA.
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Asghar, Ali, Muhammad Mazher Iqbal, Waqar Ahmed, Mujahid Ali, Husain Parvez, and Muhammad Rashid. "Exploring Shared SRAM Tables in FPGAs for Larger LUTs and Higher Degree of Sharing." International Journal of Reconfigurable Computing 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/7021056.

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In modern SRAM based Field Programmable Gate Arrays, a Look-Up Table (LUT) is the principal constituent logic element which can realize every possible Boolean function. However, this flexibility of LUTs comes with a heavy area penalty. A part of this area overhead comes from the increased amount of configuration memory which rises exponentially as the LUT size increases. In this paper, we first present a detailed analysis of a previously proposed FPGA architecture which allows sharing of LUTs memory (SRAM) tables among NPN-equivalent functions, to reduce the area as well as the number of configuration bits. We then propose several methods to improve the existing architecture. A new clustering technique has been proposed which packs NPN-equivalent functions together inside a Configurable Logic Block (CLB). We also make use of a recently proposed high performance Boolean matching algorithm to perform NPN classification. To enhance area savings further, we evaluate the feasibility of more than two LUTs sharing the same SRAM table. Consequently, this work explores the SRAM table sharing approach for a range of LUT sizes (4–7), while varying the cluster sizes (4–16). Experimental results on MCNC benchmark circuits set show an overall area reduction of ~7% while maintaining the same critical path delay.
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Liu, Luping, Wensheng Jia, and Akemi Gálvez. "A New Algorithm to Solve the Generalized Nash Equilibrium Problem." Mathematical Problems in Engineering 2020 (August 17, 2020): 1–9. http://dx.doi.org/10.1155/2020/1073412.

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We try a new algorithm to solve the generalized Nash equilibrium problem (GNEP) in the paper. First, the GNEP is turned into the nonlinear complementarity problem by using the Karush–Kuhn–Tucker (KKT) condition. Then, the nonlinear complementarity problem is converted into the nonlinear equation problem by using the complementarity function method. For the nonlinear equation equilibrium problem, we design a coevolutionary immune quantum particle swarm optimization algorithm (CIQPSO) by involving the immune memory function and the antibody density inhibition mechanism into the quantum particle swarm optimization algorithm. Therefore, this algorithm has not only the properties of the immune particle swarm optimization algorithm, but also improves the abilities of iterative optimization and convergence speed. With the probability density selection and quantum uncertainty principle, the convergence of the CIQPSO algorithm is analyzed. Finally, some numerical experiment results indicate that the CIQPSO algorithm is superior to the immune particle swarm algorithm, the Newton method for normalized equilibrium, or the quasivariational inequalities penalty method. Furthermore, this algorithm also has faster convergence and better off-line performance.
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Gorji-Bandpy, M., and A. Mozaffari. "Multiobjective Optimization of Irreversible Thermal Engine Using Mutable Smart Bee Algorithm." Applied Computational Intelligence and Soft Computing 2012 (2012): 1–13. http://dx.doi.org/10.1155/2012/652391.

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A new method called mutable smart bee (MSB) algorithm proposed for cooperative optimizing of the maximum power output (MPO) and minimum entropy generation (MEG) of an Atkinson cycle as a multiobjective, multi-modal mechanical problem. This method utilizes mutable smart bee instead of classical bees. The results have been checked with some of the most common optimizing algorithms like Karaboga’s original artificial bee colony, bees algorithm (BA), improved particle swarm optimization (IPSO), Lukasik firefly algorithm (LFFA), and self-adaptive penalty function genetic algorithm (SAPF-GA). According to obtained results, it can be concluded that Mutable Smart Bee (MSB) is capable to maintain its historical memory for the location and quality of food sources and also a little chance of mutation is considered for this bee. These features were found as strong elements for mining data in constraint areas and the results will prove this claim.
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Dissertations / Theses on the topic "Penalty function with memory"

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Park, Chuljin. "Discrete optimization via simulation with stochastic constraints." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/49088.

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In this thesis, we first develop a new method called penalty function with memory (PFM). PFM consists of a penalty parameter and a measure of constraint violation and it converts a discrete optimization via simulation (DOvS) problem with stochastic constraints into a series of DOvS problems without stochastic constraints. PFM determines a penalty of a visited solution based on past results of feasibility checks on the solution. Specifically, assuming a minimization problem, a penalty parameter of PFM, namely the penalty sequence, diverges to infinity for an infeasible solution but converges to zero almost surely for any strictly feasible solution under certain conditions. For a feasible solution located on the boundary of feasible and infeasible regions, the sequence converges to zero either with high probability or almost surely. As a result, a DOvS algorithm combined with PFM performs well even when optimal solutions are tight or nearly tight. Second, we design an optimal water quality monitoring network for river systems. The problem is to find the optimal location of a finite number of monitoring devices, minimizing the expected detection time of a contaminant spill event while guaranteeing good detection reliability. When uncertainties in spill and rain events are considered, both the expected detection time and detection reliability need to be estimated by stochastic simulation. This problem is formulated as a stochastic DOvS problem with the objective of minimizing expected detection time and with a stochastic constraint on the detection reliability; and it is solved by a DOvS algorithm combined with PFM. Finally, we improve PFM by combining it with an approximate budget allocation procedure. We revise an existing optimal budget allocation procedure so that it can handle active constraints and satisfy necessary conditions for the convergence of PFM.
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Meini, Méndez Iván Fabio. "The penalty: function and requirements." Pontificia Universidad Católica del Perú, 2013. http://repositorio.pucp.edu.pe/index/handle/123456789/116002.

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Legitimacy of criminal sanction is originated on its own purposes pursued in a state governed by the Rule of Law. That legitimacy should include the penalty as well as security measures, bearing in mind that both are imposed to someone breaking a rule of conduct, and therefore, someone capable to do it. Reviewing penal capacity or criminal liability concepts is required because if penal capacity means the capacity to understand the reality and adjust the behavior to it, and if every legitimate criminal sanction have to be imposed to someone who have the capacity of break it, then security measures also have to be imposed only to people responsible, capable to understand rules and act in accordance. With regard to people not subject to criminal liability they are standing outside Criminal Law and punish them would be illegitimate. In this line, criminal liability should be seen not only as a crime assumption but also as a basic statement for any dialogue the state shall have with the citizens: at the level of crime itself, proceedings and sentence execution .
La legitimación de la sanción penal se deriva de los fines que persigue en un Estado de derecho. Dicha legitimación debe abarcar tanto a la pena como a la medida de seguridad, y tener en cuenta que tanto la pena como la medida de seguridad se imponen a quien infringe una norma de conducta y, por tanto, a quien tiene capacidad para infringirla. Esto presupone revisar el concepto de capacidad penal o imputabilidad,pues si imputabilidad es capacidad para comprender la realidad y adecuar el comportamiento a dicha comprensión, y toda sanción penal legítima ha de imponerse a quien tiene dicha capacidad, también las medidas de seguridad han de ser impuestas solo a imputables. Los verdaderos inimputables son aquellos que están al margen del derecho penal y a quienes resulta ilegítimo imponer alguna sanción. En esta línea, la imputabilidad ha de ser vista no solo como presupuesto del delito, sino como presupuesto de cualquier diálogo que tenga el Estado con el ciudadano con respecto al delito, al proceso y a la ejecución de la pena.
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Smith, Stephen Bevis. "Exact penalty function algorithms for constrained optimal control problems." Thesis, Imperial College London, 2011. http://hdl.handle.net/10044/1/7996.

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Zhao, Xiaobing. "A Penalty Function-Based Dynamic Hybrid Shop Floor Control System." Diss., The University of Arizona, 2006. http://hdl.handle.net/10150/195300.

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To cope with dynamics and uncertainties, a novel penalty function-based hybrid, multi-agent shop floor control system is proposed in this dissertation. The key characteristic of the proposed system is the capability of adaptively distributing decision-making power across different levels of control agents in response to different levels of disturbance. The subordinate agent executes tasks based on the schedule from the supervisory level agent in the absence of disturbance. Otherwise, it optimizes the original schedule before execution by revising it with regard to supervisory level performance (via penalty function) and disturbance. Penalty function, mathematical programming formulations, and quantitative metrics are presented to indicate the disturbance levels and levels of autonomy. These formulations are applied to diverse performance measurements such as completion time related metrics, makespan, and number of late jobs. The proposed control system is illustrated, tested with various job shop problems, and benchmarked against other shop floor control systems. In today's manufacturing system, man still plays an important role together with the control system Therefore, better coordination of humans and control systems is an inevitable topic. A novel BDI agent-based software model is proposed in this work to replace the partial decision-making function of a human. This proposed model is capable of 1) generating plans in real-time to adapt the system to a changing environment, 2) supporting not only reactive, but also proactive decision-making, 3) maintaining situational awareness in human language-like logic to facilitate real human decision-making, and 4) changing the commitment strategy adaptive to historical performance. The general purposes human operator model is then customized and integrated with an automated shop floor control system to serve as the error detection and recovery system. This model has been implemented in JACK software; however, JACK does not support real-time generation of a plan. Therefore, the planner sub-module has been developed in Java and then integrated with the JACK. To facilitate integration of an agent, real-human, and the environment, a distributed computing platform based on DOD High Level Architecture has been used. The effectiveness of the proposed model is then tested in several scenarios in a simulated automated manufacturing environment.
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Dew, M. C. "An exact penalty function algorithm for accurate optimisation of industrial problems." Thesis, University of Hertfordshire, 1985. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.353622.

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Case, Lori Michelle. "An l??1 penalty function approach to the nonlinear bilevel programming problem." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/nq21334.pdf.

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Chang, Kcomt Romy Alexandra. "Constitutional function assigned to the penalty: Bases for a criminal policy plan." Pontificia Universidad Católica del Perú, 2013. http://repositorio.pucp.edu.pe/index/handle/123456789/116385.

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This article intends to analyze treatment and functions assigned to the penalty by our Peruvian Constitution and the way this legal institution is conducted at the prescribed basic penalty level (imposed by the legislator ineach type of criminal offence), the specific penalty level (imposed by the judge according to its individual characteristics in each case) and at the penitentiary enforcement level. Finally recommends some considerations for carrying out a possible legislative reform in accordance with a criminal policy plan within our constitutional framework.
El presente trabajo busca efectuar un análisis en torno al tratamiento y las funciones que nuestra Constitución política asigna a la pena, y la manera como dicha institución se desarrolla en nuestro país con respectoa la pena abstracta (la impuesta por el legislador en cada tipo penal), la pena concreta (la impuesta por el juez luego de una individualización en cada casoconcreto), y su ejecución en el ámbito penitenciario. Finaliza proponiendo algunas consideraciones para una eventual reforma legislativa conforme conun plan de política criminal que se encuentre dentro del marco constitucional.
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Nilsson, Jonna. "Allocentric memory and hippocampal function." Thesis, University of Newcastle Upon Tyne, 2013. http://hdl.handle.net/10443/1864.

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Approximately one-third of trauma patients are coagulopathic on arrival to the emergency department. Acute traumatic coagulopathy and systemic inflammatory responses are serious secondary consequences of severe trauma and are linked to increased morbidity and mortality. Early tissue hypoxia is a major component in the aetiology of both complications. New resuscitation strategies are aimed at improving tissue oxygenation in the pre-hospital phase, and may attenuate coagulopathy and inflammatory sequelae. This is of particular importance in military personnel who suffer complex injuries, often from blast exposure, and may have extended evacuation times. This thesis evaluates the effect of a novel hybrid (NH) resuscitation strategy on coagulation and inflammation. Terminally anaesthetised pigs were randomised to one of two injury strands of haemorrhage +/- blast injury; initially resuscitated with 0.9% Saline to a hypotensive systolic blood pressure of 80mmHg for one hour. This was followed by either a return to a normotensive pressure (110mmHg) (NH) or a continuation at the hypotensive level. Over both injury strands NH significantly reduced Prothrombin Time, PT (mean proportion of baseline: 1.40±0.05 vs. 1.80±0.09; p=0.001) and interleukin-6 (IL6) levels (mean 1106±153 vs. 429±79 pg/ml; p=0.001) compared to the hypotensive groups. PT was positively correlated with IL6 (p=0.002) and base deficit (p=0.0004). These findings indicate that improving tissue oxygenation reduces the coagulation derangement and the pro-inflammatory response. No difference in coagulopathy was found between injury strands although blast did cause greater inflammation. Early identification of coagulopathic casualties is essential and a separate feasibility field study was preformed to assess the use of thromboelastometry in a deployed military hospital, evaluating the degree of coagulopathy in battlefield casualties and to monitor the coagulation status during the resuscitation process. In conclusion, NH attenuated the acute traumatic coagulopathy and inflammatory responses and therefore should be considered when an extended casualty evacuation is enforced.
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Camp, Sophie Jane. "Memory function in multiple sclerosis." Thesis, University College London (University of London), 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.327043.

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Ozdaryal, Burak. "Exterior Penalty Approaches for Solving Linear Programming Problems." Thesis, Virginia Tech, 1999. http://hdl.handle.net/10919/33862.

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In this research effort, we study three exterior penalty function approaches for solving linear programming problems. These methods are an active set l2 penalty approach (ASL2), an inequality-equality based l2 penalty approach (IEL2), and an augmented Lagrangian approach (ALAG). Particular effective variants are presented for each method, along with comments and experience on alternative algorithmic strategies that were empirically investigated. Our motivation is to examine the relative performance of these different approaches based on the basic l2 penalty function in order to provide insights into the viability of these methods for solving linear programs. To test the performance of these algorithms, a set of randomly generated problems as well as a set of NETLIB test problems from the public domain are used. By way of providing a benchmark for comparisons, we also solve the test problems using CPLEX 6.0, an advanced simplex implementation. While a particular variant (ALAG2) of ALAG performed the best for randomly generated test problems, ASL2 performed the best for the NETLIB test problems. Moreover, for test problems having only equality constraints, IEL2, and ASL2 (which is a finer-tuned version of IEL2 in this case) were comparable and yielded a second-best performance in comparison with ALAG2. Furthermore, a set of problems with relatively higher density parameter values, as well as a set of low-density problems were used to determine the effect of density on the relative performances of these methods. This experiment revealed that for linear programs with a high density parameter, ASL2 is the best alternative among the tested algorithms; whereas, for low-density problems ALAG2 is the fastest method. Moreover, although our implementation was rudimentary in comparison with CPLEX, all of the tested methods attained a final solution faster than CPLEX for the set of large-scale low-density problems, sometimes as fast as requiring only 16-23% of the effort consumed by CPLEX. Average rank tests based on the computational results obtained are performed using two different statistics, that assess the speed of convergence and the quality or accuracy of the solution, in order to determine the relative effectiveness of the algorithms and to validate our conclusions. Overall, the results provide insights into selecting algorithmic strategies based on problem structure and indicate that while this class of methods is viable for computing near optimal solutions, more research is needed to design robust and competitive exterior point methods for solving linear programming problems. However, the use of the proposed variant of the augmented Lagrangian method to solve large-scale low-density linear programs is promising and should be explored more extensively.
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Books on the topic "Penalty function with memory"

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Langenmayr, Felix. Organisational Memory as a Function. Wiesbaden: Springer Fachmedien Wiesbaden, 2016. http://dx.doi.org/10.1007/978-3-658-12868-5.

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Memory and brain dynamics: Oscillations integrating function and memory. Boca Raton: CRC Press, 2004.

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Waldman, W. A penalty element formulation for calculating bulk stress. Melbourne, Australia: Aeronautical Research Laboratory, 1989.

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Ross, Alloway, ed. The working memory advantage: Train your brain to function stronger, smarter, faster. New York: Simon & Schuster, 2013.

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name, No. Language and function: To the memory of Jan Firbas. Amsterdam: Benjamins, 2003.

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Aphasia: A pathophysiological key to memory function and "volitional" naming. Commack, N.Y: Nova Science Publishers, 1996.

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Peggy, Dace, ed. The memory solution: Dr. Julian Whitaker's 10-step program to optimize your memory and brain function. Garden City Park, NY: Avery Pub. Group, 1999.

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Povey, Gail. Memory as a function of the emotional characteristics associated with words. Sudbury, Ont: Laurentian University, Department of Psychology, 1987.

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Funaro, Daniele. Convergence results for pseudospectral approximations of hyperbolic systems by a penalty type boundary treatment. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1989.

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Funaro, Daniele. Convergence results for pseudospectral approximations of hyperbolic systems by a penalty type boundary treatment. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1989.

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Book chapters on the topic "Penalty function with memory"

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Bartholomew–Biggs, Michael. "Penalty Function Methods." In Nonlinear Optimization with Engineering Applications, 1–14. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-78723-7_18.

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Choi, ByoungSeon. "Penalty Function Methods." In ARMA Model Identification, 43–74. New York, NY: Springer US, 1992. http://dx.doi.org/10.1007/978-1-4613-9745-8_3.

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Evtushenko, Yurij G. "The Penalty Function Method." In Numerical Optimization Techniques, 196–263. New York, NY: Springer New York, 1985. http://dx.doi.org/10.1007/978-1-4612-5022-7_4.

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Cuvelier, C., A. Segal, and A. A. van Steenhoven. "The penalty function method." In Finite Element Methods and Navier-Stokes Equations, 263–87. Dordrecht: Springer Netherlands, 1986. http://dx.doi.org/10.1007/978-94-010-9333-0_8.

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Weik, Martin H. "memory function." In Computer Science and Communications Dictionary, 999. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_11324.

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Loring, David W., Kimford J. Meador, Gregory P. Lee, and Don W. King. "Memory." In Amobarbital Effects and Lateralized Brain Function, 24–62. New York, NY: Springer New York, 1992. http://dx.doi.org/10.1007/978-1-4612-2874-5_2.

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Si, Chengyong, Jianqiang Shen, Xuan Zou, Yashuai Duo, Lei Wang, and Qidi Wu. "A Dynamic Penalty Function for Constrained Optimization." In Advances in Swarm and Computational Intelligence, 261–72. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20472-7_28.

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Matias, J., P. Mestre, A. Correia, P. Couto, C. Serodio, and P. Melo-Pinto. "Penalty Fuzzy Function for Derivative-Free Optimization." In Advances in Intelligent and Soft Computing, 293–301. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24001-0_27.

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Langenmayr, Felix. "Social memory studies." In Organisational Memory as a Function, 25–66. Wiesbaden: Springer Fachmedien Wiesbaden, 2016. http://dx.doi.org/10.1007/978-3-658-12868-5_2.

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Kulkarni, Anand Jayant, Kang Tai, and Ajith Abraham. "Constrained Probability Collectives with a Penalty Function Approach." In Intelligent Systems Reference Library, 61–72. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16000-9_4.

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Conference papers on the topic "Penalty function with memory"

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Ma, Jingsen, Chao-Tsung Hsiao, and Georges L. Chahine. "Shared-Memory Parallelization for Two-Way Coupled Euler-Lagrange Modeling of Bubbly Flows." In ASME 2014 4th Joint US-European Fluids Engineering Division Summer Meeting collocated with the ASME 2014 12th International Conference on Nanochannels, Microchannels, and Minichannels. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/fedsm2014-22057.

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Cavitating bubbly flows are encountered in many engineering problems involving propellers, pumps, valves, ultrasonic biomedical applications, … etc. In this contribution an OpenMP parallelized Euler-Lagrange model of two-phase flow problems and cavitation is presented. The two-phase medium is treated as a continuum and solved on an Eulerian grid, while the discrete bubbles are tracked in a Lagrangian fashion with their dynamics computed. The intimate coupling between the two description levels is realized through the local void fraction, which is computed from the instantaneous bubble volumes and locations, and provides the continuum properties. Since, in practice, any such flows will involve large numbers of bubbles, schemes for significant speedup are needed to reduce computation times. We present here a shared-memory parallelization scheme combining domain decomposition for the continuum domain and number decomposition for the bubbles; both selected to realize maximum speed up and good load balance. The Eulerian computational domain is subdivided based on geometry into several subdomains, while for the Lagrangian computations, the bubbles are subdivided based on their indices into several subsets. The number of fluid subdomains and bubble subsets are matched with the number of CPU cores available in a share-memory system. Computation of the continuum solution and the bubble dynamics proceeds sequentially. During each computation time step, all selected OpenMP threads are first used to evolve the fluid solution, with each handling one subdomain. Upon completion, the OpenMP threads selected for the Lagrangian solution are then used to execute the bubble computations. All data exchanges are executed through the shared memory. Extra steps are taken to localize the memory access pattern to minimize non-local data fetch latency, since severe performance penalty may occur on a Non-Uniform Memory Architecture multiprocessing system where thread access to non-local memory is much slower than to local memory. This parallelization scheme is illustrated on a typical non-uniform bubbly flow problem, cloud bubble dynamics near a rigid wall driven by an imposed pressure function.
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Karajgikar, Saket, Dereje Agonafer, Kanad Ghose, Bahgat Sammakia, Cristina Amon, and Gamal Refai-Ahmad. "Effect of Relocation of Functional Units of a Non-Uniformly Powered Microprocessor on Thermal and Device Clock Performance." In ASME 2009 International Mechanical Engineering Congress and Exposition. ASMEDC, 2009. http://dx.doi.org/10.1115/imece2009-11769.

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Integration of different functional components such as level two (L2) cache memory, high-speed I/O interfaces, memory controller, etc. has enhanced microprocessor performance. In this architecture, certain functional units on the microprocessor dissipate a significant fraction of the total power while other functional units dissipate little or no power. This highly non-uniform power distribution results in a large temperature gradient with localized hot spots that may have detrimental effects on computer performance, product reliability, and yield. Moving the functional units may reduce the junction temperature but can also affect performance by as much as 30%. In this paper, multi-objective optimization is performed to minimize the junction temperature without significantly altering the computer performance. From the results, the minimum and the maximum temperature was 56.6°C and 62.2°C with a corresponding penalty on the performance of 14% and 0% respectively. The numerical analysis was performed for 90 nm Pentium® IV Northwood architecture at 3 GHz clock speed.
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Karajgikar, Saket, Dereje Agonafer, Kanad Ghose, Bahgat Sammakia, Cristina Amon, and Gamal Refai-Ahmed. "Development of a Numerical Model for Non-Uniformly Powered Die to Improve Both Thermal and Device Clock Performance." In ASME 2009 InterPACK Conference collocated with the ASME 2009 Summer Heat Transfer Conference and the ASME 2009 3rd International Conference on Energy Sustainability. ASMEDC, 2009. http://dx.doi.org/10.1115/interpack2009-89188.

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Integration of different functional components such as level two (L2) cache memory, high-speed I/O interfaces, memory controller, etc. has enhanced microprocessor performance. In this architecture, certain functional units on the microprocessor dissipate a significant fraction of the total power while other functional blocks dissipate little or no power. This highly non-uniform power distribution results in a large temperature gradient with localized hot spots that may have detrimental effect on computer performance and product reliability as well as yield. Moving the functional blocks may reduce the junction temperature but can also affect the performance by a factor as high as 35%. In this paper, multi-objective optimization is performed to minimize the junction temperature without significantly altering the computer performance. From the results, the minimum and the maximum temperature was 82.4°C and 94.5°C with a corresponding penalty on the performance of 35% and 0% respectively. The optimized location of the functional blocks resulted in a temperature of 83.2°C for a performance loss of 5%.
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Wang, Ran, Jie He, Liyuan Xu, and Qin Wang. "Penalty Function Based Anchor-Free Positioning." In 2015 11th International Conference on Mobile Ad-hoc and Sensor Networks (MSN). IEEE, 2015. http://dx.doi.org/10.1109/msn.2015.10.

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Qin, Jiangning, and Duc Nguyen. "Generalized exponential penalty function for nonlinear programming." In 35th Structures, Structural Dynamics, and Materials Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1994. http://dx.doi.org/10.2514/6.1994-1360.

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Datta, Rituparna, and Kalyanmoy Deb. "Individual penalty based constraint handling using a hybrid bi-objective and penalty function approach." In 2013 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2013. http://dx.doi.org/10.1109/cec.2013.6557898.

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Saroare, Md Kibria, Md Syadus Sefat, Sajib Sen, and Md Shahjahan. "A modified penalty function in fuzzy clustering algorithm." In 2017 Intelligent Systems Conference (IntelliSys). IEEE, 2017. http://dx.doi.org/10.1109/intellisys.2017.8324332.

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Zeng, Huiwen, and H. J. Trussell. "Feature Selection using a Mixed-Norm Penalty Function." In 2006 International Conference on Image Processing. IEEE, 2006. http://dx.doi.org/10.1109/icip.2006.312667.

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Demarcke, Pieterjan, and Hendrik Rogier. "Penalty function method for constrained DOA-based beamforming." In 2009 IEEE Antennas and Propagation Society International Symposium (APSURSI). IEEE, 2009. http://dx.doi.org/10.1109/aps.2009.5171466.

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Jadaan, Omar Al, Lakshmi Rajamani, and C. R. Rao. "Parameterless penalty function for solving constrained evolutionary optimization." In 2009 IEEE Workshop on Hybrid Intelligent Models and Applications (HIMA). IEEE, 2009. http://dx.doi.org/10.1109/hima.2009.4937826.

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Reports on the topic "Penalty function with memory"

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Yan, Lok K. Hardware Based Function Level Mandatory Access Control for Memory Structures. Fort Belvoir, VA: Defense Technical Information Center, April 2008. http://dx.doi.org/10.21236/ada481104.

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Griffey, Richard. Controlled Enhancemnt of Long-Term Memory by Modulating Neuronal miRNA Function. Fort Belvoir, VA: Defense Technical Information Center, September 2012. http://dx.doi.org/10.21236/ada577054.

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Biryukov, A., D. Dinu, D. Khovratovich, and S. Josefsson. Argon2 Memory-Hard Function for Password Hashing and Proof-of-Work Applications. RFC Editor, September 2021. http://dx.doi.org/10.17487/rfc9106.

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Stone, Peter, and Manuela Veloso. Beating a Defender in Robotic Soccer: Memory-Based Learning of a Continuous Function,. Fort Belvoir, VA: Defense Technical Information Center, December 1995. http://dx.doi.org/10.21236/ada303088.

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Camenzind, Lauren, Molly Kafader, Rachel Schwam, Mikayla Taylor, Zoie Wilkes, and Madison Williams. Space Retrieval Training for Memory Enhancement in Adults with Dementia. University of Tennessee Health Science Center, May 2021. http://dx.doi.org/10.21007/chp.mot2.2021.0013.

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The final portfolio contains 8 research articles from national and international journals. Study designs include one systematic review, one randomized control trial with pretest-posttest design, three small-scale randomized control trials, one quasi-experimental study with no control, one time-series study, and one case study. All studies relate directly to components of the evidence-based practice question and will be used to draft new recommendations for implementation regarding spaced retrieval training for memory enhancement in adults with dementia. Seven out of the eight articles looked at the effects of SR techniques on functional tasks. Articles looked at eating difficulty (1), independent use of walkers (1), iADL function (3), use of technology (1), and ADL function (1). One out of eight articles looked at benefits of spaced retrieval techniques on episodic memory, which is not necessarily a functional task, but is needed to perform functional tasks.
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Pettit, Chris, and D. Wilson. A physics-informed neural network for sound propagation in the atmospheric boundary layer. Engineer Research and Development Center (U.S.), June 2021. http://dx.doi.org/10.21079/11681/41034.

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We describe what we believe is the first effort to develop a physics-informed neural network (PINN) to predict sound propagation through the atmospheric boundary layer. PINN is a recent innovation in the application of deep learning to simulate physics. The motivation is to combine the strengths of data-driven models and physics models, thereby producing a regularized surrogate model using less data than a purely data-driven model. In a PINN, the data-driven loss function is augmented with penalty terms for deviations from the underlying physics, e.g., a governing equation or a boundary condition. Training data are obtained from Crank-Nicholson solutions of the parabolic equation with homogeneous ground impedance and Monin-Obukhov similarity theory for the effective sound speed in the moving atmosphere. Training data are random samples from an ensemble of solutions for combinations of parameters governing the impedance and the effective sound speed. PINN output is processed to produce realizations of transmission loss that look much like the Crank-Nicholson solutions. We describe the framework for implementing PINN for outdoor sound, and we outline practical matters related to network architecture, the size of the training set, the physics-informed loss function, and challenge of managing the spatial complexity of the complex pressure.
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