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Journal articles on the topic 'Penalty function with memory'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

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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2

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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3

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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7

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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9

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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10

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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11

Ma, Wen, Zongxu Pan, Feng Yuan, and Bin Lei. "Super-Resolution of Remote Sensing Images via a Dense Residual Generative Adversarial Network." Remote Sensing 11, no. 21 (November 3, 2019): 2578. http://dx.doi.org/10.3390/rs11212578.

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Single image super-resolution (SISR) has been widely studied in recent years as a crucial technique for remote sensing applications. In this paper, a dense residual generative adversarial network (DRGAN)-based SISR method is proposed to promote the resolution of remote sensing images. Different from previous super-resolution (SR) approaches based on generative adversarial networks (GANs), the novelty of our method mainly lies in the following factors. First, we made a breakthrough in terms of network architecture to improve performance. We designed a dense residual network as the generative network in GAN, which can make full use of the hierarchical features from low-resolution (LR) images. We also introduced a contiguous memory mechanism into the network to take advantage of the dense residual block. Second, we modified the loss function and altered the model of the discriminative network according to the Wasserstein GAN with a gradient penalty (WGAN-GP) for stable training. Extensive experiments were performed using the NWPU-RESISC45 dataset, and the results demonstrated that the proposed method outperforms state-of-the-art methods in terms of both objective evaluation and subjective perspective.

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12

Yoon, Sang-Joon, and Dong-Hoon Choi. "Topology Designs of Slider Air Bearings." Journal of Tribology 126, no. 2 (April 1, 2004): 342–46. http://dx.doi.org/10.1115/1.1611501.

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A new approach for topology designs of slider air bearings in magnetic recording disk drives is suggested by using large-scale discrete variable optimization techniques. Conventional optimization techniques are restricted to the original topology of the slider by modifying the initial designs. To overcome the restriction, a new topology design approach is presented with enhanced mathematical techniques. Topology optimization of slider air bearings typically has a large number of design variables because the finite mesh must be fine enough to represent the shape of the air bearing surface (ABS). To handle a large number of design variables, an efficient strategy for the optimization including the sensitivity analysis must be established. As a gradient-based local optimization algorithm, the sequential unconstrained minimization technique (SUMT) using an exterior penalty function is used, which requires little computational effort and computer memory. For the gradient calculation, the analytical design sensitivity analysis method introducing an adjoint variable is employed. A topology design problem is formulated as a function of the residuals which is calculated by solving the generalized Reynolds equation. A very large number of discrete design variables (=9409) are dealt with, which denote the rail heights at grid cells. To validate the suggested design methodology, a developed program is applied to two slider models with one and three trailing rails. The simulation results demonstrated the effectiveness of the proposed design methodology by showing that the optimized topologies have reasonable shapes without any initial designs.

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13

Richards, Evelyn W., and Eldon A. Gunn. "Tabu search design for difficult forest management optimization problems." Canadian Journal of Forest Research 33, no. 6 (June 1, 2003): 1126–33. http://dx.doi.org/10.1139/x03-039.

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A series of tabu search (TS) methods for solving the stand harvesting and road access optimization problem was developed and evaluated. This challenging forest management problem includes spatial constraints for maximum opening size, adjacency delay (green up), as well as timber-flow targets derived exogenously from a strategic planning process. The base harvest decision unit is the stand, and harvest blocks are created dynamically as adjacent stands are scheduled for treatments. The road network subproblem is solved using a fast heuristic method to select a minimum discounted cost set of road construction projects so that scheduled stands are accessible. The TS methods range from simple ones with fixed tabu tenure to an adaptive search with feedback mechanisms to control tabu tenure and to direct the search near constraint boundaries. It was found that while simple TS algorithms can find feasible solutions to the problem, these may be far from optimal. A good short-term memory strategy, constraint boundaries smoothed using penalty functions, and customized diversification moves were important design elements in the most successful TS algorithm for this problem. This paper points out the necessity to design the TS method carefully, since there are many possible TS designs and the design choices matter.

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14

Vamaraju, Janaki, Mrinal K. Sen, Jonas De Basabe, and Mary Wheeler. "A hybrid Galerkin finite element method for seismic wave propagation in fractured media." Geophysical Journal International 221, no. 2 (January 21, 2020): 857–78. http://dx.doi.org/10.1093/gji/ggaa037.

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SUMMARY The discontinuous Galerkin finite element method (DGM) is a promising algorithm for modelling wave propagation in fractured media. It allows for discontinuities in the displacement field to simulate fractures or faults in a model. Our approach is based on the interior-penalty formulation of DGM, and the fractures are simulated using the linear-slip model, which is incorporated into the weak formulation. On the other hand, the spectral element method (SEM) can be used to simulate elastic wave propagation in non-fractured media. SEM uses continuous basis functions which do not allow for discontinuities in the displacement field. However, the computation cost of DGM is significantly larger than SEM due primarily to increase in the number of degrees of freedom. Here we propose a hybrid Galerkin method (HGM) for elastic wave propagation in fractured media that combines the salient features of each of the algorithm resulting in significant reduction in computational cost compared to DGM. We use DGM in areas containing fractures and SEM in regions without fractures. The coupling between the domains at the interfaces is satisfied in the weak form through interface conditions. The degree of reduction in computation time depends primarily on the density of fractures in the medium. In this paper, we formulate and implement HGM for seismic wave propagation in fractured media. Using realistic 2-D/3-D numerical examples, we show that our proposed HGM outperforms DGM with reduced computation cost and memory requirement while maintaining the same level of accuracy.

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15

Xu, Xinsheng, Zhiqing Meng, Jianwu Sun, and Rui Shen. "A penalty function method based on smoothing lower order penalty function." Journal of Computational and Applied Mathematics 235, no. 14 (May 2011): 4047–58. http://dx.doi.org/10.1016/j.cam.2011.02.031.

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16

Meng, Zhiqing, Rui Shen, Chuangyin Dang, and Min Jiang. "Augmented Lagrangian Objective Penalty Function." Numerical Functional Analysis and Optimization 36, no. 11 (November 2, 2015): 1471–92. http://dx.doi.org/10.1080/01630563.2015.1070864.

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17

Agarwal, Vivek, Andrei V. Gribok, and Mongi A. Abidi. "Image restoration usingL1norm penalty function." Inverse Problems in Science and Engineering 15, no. 8 (December 2007): 785–809. http://dx.doi.org/10.1080/17415970600971987.

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18

Huyer, Waltraud, and Arnold Neumaier. "A New Exact Penalty Function." SIAM Journal on Optimization 13, no. 4 (January 2003): 1141–58. http://dx.doi.org/10.1137/s1052623401390537.

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19

Feiring, Bruce R., Don T. Phillips, and Gary L. Hogg. "Penalty function techniques: A tutorial." Computers & Industrial Engineering 9, no. 4 (January 1985): 307–26. http://dx.doi.org/10.1016/0360-8352(85)90019-1.

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20

Shandiz, Roohollah Aliakbari, and Emran Tohidi. "Decrease of the Penalty Parameter in Differentiable Penalty Function Methods." Theoretical Economics Letters 01, no. 01 (2011): 8–14. http://dx.doi.org/10.4236/tel.2011.11003.

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21

Zheng, Ying, Zhiqing Meng, and Rui Shen. "An M-Objective Penalty Function Algorithm Under Big Penalty Parameters." Journal of Systems Science and Complexity 29, no. 2 (August 29, 2015): 455–71. http://dx.doi.org/10.1007/s11424-015-3204-3.

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22

Pantoja, J. F. A. De O., and D. Q. Mayne. "Exact penalty function algorithm with simple updating of the penalty parameter." Journal of Optimization Theory and Applications 69, no. 3 (June 1991): 441–67. http://dx.doi.org/10.1007/bf00940684.

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23

Mongeau, Marcel, and Annick Sartenaer. "Automatic decrease of the penalty parameter in exact penalty function methods." European Journal of Operational Research 83, no. 3 (June 1995): 686–99. http://dx.doi.org/10.1016/0377-2217(93)e0339-y.

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24

Shen, Rui, Zhiqing Meng, Chuangyin Dang, and Min Jiang. "Algorithm of Barrier Objective Penalty Function." Numerical Functional Analysis and Optimization 38, no. 11 (July 12, 2017): 1473–89. http://dx.doi.org/10.1080/01630563.2017.1338732.

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25

Yang, X. Q., and Y. Y. Zhou. "Second-Order Analysis of Penalty Function." Journal of Optimization Theory and Applications 146, no. 2 (February 12, 2010): 445–61. http://dx.doi.org/10.1007/s10957-010-9666-5.

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26

Fletcher, Roger, and Sven Leyffer. "Nonlinear programming without a penalty function." Mathematical Programming 91, no. 2 (January 1, 2002): 239–69. http://dx.doi.org/10.1007/s101070100244.

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27

Fiacco, Anthony V. "Perturbed variations of penalty function methods." Annals of Operations Research 27, no. 1 (December 1990): 371–80. http://dx.doi.org/10.1007/bf02055202.

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28

Stetsyuk, Petro, Andreas Fischer, and Olha Khomiak. "Maximum Penalty Function in Linear Programming." Physico-mathematical modelling and informational technologies, no. 33 (September 6, 2021): 156–60. http://dx.doi.org/10.15407/fmmit2021.33.156.

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A linear program can be equivalently reformulated as an unconstrained nonsmooth minimization problem, whose objective is the sum of the original objective and a penalty function with a sufficiently large penalty parameter. The article presents two methods for choosing this parameter. The first one applies to linear programs with usual linear inequality constraints. Then, we use a corresponding theorem by N.Z. Shor on the equivalence of a convex program to an unconstrained nonsmooth minimization problem. The second method is for linear programs of a special type. This means that all inequalities are of the form that a linear expression on the left-hand side is less or equal to a positive constant on the right-hand side. For this special type, we use a corresponding theorem of B.N. Pshenichny on establishing a penalty parameter for convex programs. For differently sized linear programs of the special type, we demonstrate that suitable penalty parameters can be computed by a procedure in GNU Octave based on GLPK software.

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Shen, Rui, Zhiqing Meng, and Min Jiang. "Smoothing Partially Exact Penalty Function of Biconvex Programming." Asia-Pacific Journal of Operational Research 37, no. 04 (July 24, 2020): 2040018. http://dx.doi.org/10.1142/s0217595920400187.

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In this paper, a smoothing partial exact penalty function of biconvex programming is studied. First, concepts of partial KKT point, partial optimum point, partial KKT condition, partial Slater constraint qualification and partial exactness are defined for biconvex programming. It is proved that the partial KKT point is equal to the partial optimum point under the condition of partial Slater constraint qualification and the penalty function of biconvex programming is partially exact if partial KKT condition holds. We prove the error bounds properties between smoothing penalty function and penalty function of biconvex programming when the partial KKT condition holds, as well as the error bounds between objective value of a partial optimum point of smoothing penalty function problem and its [Formula: see text]-feasible solution. So, a partial optimum point of the smoothing penalty function optimization problem is an approximately partial optimum point of biconvex programming. Second, based on the smoothing penalty function, two algorithms are presented for finding a partial optimum or approximate [Formula: see text]-feasible solution to an inequality constrained biconvex optimization and their convergence is proved under some conditions. Finally, numerical experiments show that a satisfactory approximate solution can be obtained by the proposed algorithm.

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Burachik, Regina S., and C. Yalçın Kaya. "An augmented penalty function method with penalty parameter updates for nonconvex optimization." Nonlinear Analysis: Theory, Methods & Applications 75, no. 3 (February 2012): 1158–67. http://dx.doi.org/10.1016/j.na.2011.03.013.

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Ruan, Jiechang, Wenguang Yu, Ke Song, Yihan Sun, Yujuan Huang, and Xinliang Yu. "A Note on a Generalized Gerber–Shiu Discounted Penalty Function for a Compound Poisson Risk Model." Mathematics 7, no. 10 (September 24, 2019): 891. http://dx.doi.org/10.3390/math7100891.

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In this paper, we propose a new generalized Gerber–Shiu discounted penalty function for a compound Poisson risk model, which can be used to study the moments of the ruin time. First, by taking derivatives with respect to the original Gerber–Shiu discounted penalty function, we construct a relation between the original Gerber–Shiu discounted penalty function and our new generalized Gerber–Shiu discounted penalty function. Next, we use Laplace transform to derive a defective renewal equation for the generalized Gerber–Shiu discounted penalty function, and give a recursive method for solving the equation. Finally, when the claim amounts obey the exponential distribution, we give some explicit expressions for the generalized Gerber–Shiu discounted penalty function. Numerical illustrations are also given to study the effect of the parameters on the generalized Gerber–Shiu discounted penalty function.

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Duan, Yaqiong, and Shujun Lian. "Smoothing Approximation to the Square-Root Exact Penalty Function." Journal of Systems Science and Information 4, no. 1 (February 25, 2016): 87–96. http://dx.doi.org/10.1515/jssi-2016-0087.

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AbstractIn this paper, smoothing approximation to the square-root exact penalty functions is devised for inequality constrained optimization. It is shown that an approximately optimal solution of the smoothed penalty problem is an approximately optimal solution of the original problem. An algorithm based on the new smoothed penalty functions is proposed and shown to be convergent under mild conditions. Three numerical examples show that the algorithm is efficient.

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Pan, Cheng, Xiaolin Wang, Yingwei Luo, and Zhenlin Wang. "Penalty- and Locality-aware Memory Allocation in Redis Using Enhanced AET." ACM Transactions on Storage 17, no. 2 (May 28, 2021): 1–45. http://dx.doi.org/10.1145/3447573.

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Due to large data volume and low latency requirements of modern web services, the use of an in-memory key-value (KV) cache often becomes an inevitable choice (e.g., Redis and Memcached). The in-memory cache holds hot data, reduces request latency, and alleviates the load on background databases. Inheriting from the traditional hardware cache design, many existing KV cache systems still use recency-based cache replacement algorithms, e.g., least recently used or its approximations. However, the diversity of miss penalty distinguishes a KV cache from a hardware cache. Inadequate consideration of penalty can substantially compromise space utilization and request service time. KV accesses also demonstrate locality, which needs to be coordinated with miss penalty to guide cache management. In this article, we first discuss how to enhance the existing cache model, the Average Eviction Time model, so that it can adapt to modeling a KV cache. After that, we apply the model to Redis and propose pRedis, Penalty- and Locality-aware Memory Allocation in Redis, which synthesizes data locality and miss penalty, in a quantitative manner, to guide memory allocation and replacement in Redis. At the same time, we also explore the diurnal behavior of a KV store and exploit long-term reuse. We replace the original passive eviction mechanism with an automatic dump/load mechanism, to smooth the transition between access peaks and valleys. Our evaluation shows that pRedis effectively reduces the average and tail access latency with minimal time and space overhead. For both real-world and synthetic workloads, our approach delivers an average of 14.0%∼52.3% latency reduction over a state-of-the-art penalty-aware cache management scheme, Hyperbolic Caching (HC), and shows more quantitative predictability of performance. Moreover, we can obtain even lower average latency (1.1%∼5.5%) when dynamically switching policies between pRedis and HC.

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Jiang, Min, Zhiqing Meng, and Rui Shen. "Partial Exactness for the Penalty Function of Biconvex Programming." Entropy 23, no. 2 (January 21, 2021): 132. http://dx.doi.org/10.3390/e23020132.

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Biconvex programming (or inequality constrained biconvex optimization) is an important model in solving many engineering optimization problems in areas like machine learning and signal and information processing. In this paper, the partial exactness of the partial optimum for the penalty function of biconvex programming is studied. The penalty function is partially exact if the partial Karush–Kuhn–Tucker (KKT) condition is true. The sufficient and necessary partially local stability condition used to determine whether the penalty function is partially exact for a partial optimum solution is also proven. Based on the penalty function, an algorithm is presented for finding a partial optimum solution to an inequality constrained biconvex optimization, and its convergence is proven under some conditions.

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35

Prajapati, Raju, and Om Prakash Dubey. "ANALYSING THE IMPACT OF PENALTY CONSTANT ON PENALTY FUNCTION THROUGH PARTICE SWARM OPTIMIZATION." International Journal of Students' Research in Technology & Management 6, no. 2 (March 1, 2018): 01–06. http://dx.doi.org/10.18510/ijsrtm.2018.621.

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Non Linear Programming Problems (NLPP) are tedious to solve as compared to Linear Programming Problem (LPP). The present paper is an attempt to analyze the impact of penalty constant over the penalty function, which is used to solve the NLPP with inequality constraint(s). The improved version of famous meta heuristic Particle Swarm Optimization (PSO) is used for this purpose. The scilab programming language is used for computational purpose. The impact of penalty constant is studied by considering five test problems. Different values of penalty constant are taken to prepare the unconstraint NLPP from the given constraint NLPP with inequality constraint. These different unconstraint NLPP is then solved by improved PSO, and the superior one is noted. It has been shown that, In all the five cases, the superior one is due to the higher penalty constant. The computational results for performance are shown in the respective sections.

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Meng, Zhiqing, Chuangyin Dang, Rui Shen, and Ming Jiang. "An Objective Penalty Function of Bilevel Programming." Journal of Optimization Theory and Applications 153, no. 2 (November 5, 2011): 377–87. http://dx.doi.org/10.1007/s10957-011-9945-9.

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White, D. J. "Penalty Function Approach to Linear Trilevel Programming." Journal of Optimization Theory and Applications 93, no. 1 (April 1997): 183–97. http://dx.doi.org/10.1023/a:1022610103712.

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Bai, F. S., Z. Y. Wu, and D. L. Zhu. "Lower order calmness and exact penalty function." Optimization Methods and Software 21, no. 4 (August 2006): 515–25. http://dx.doi.org/10.1080/10556780600627693.

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Iyengar, Garud, and Karl Sigman. "Exponential penalty function control of loss networks." Annals of Applied Probability 14, no. 4 (November 2004): 1698–740. http://dx.doi.org/10.1214/105051604000000936.

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Danilin, Yu M., and V. N. Kovnir. "Exact penalty function for nonlinear programming problems." Cybernetics 22, no. 5 (1987): 585–90. http://dx.doi.org/10.1007/bf01068354.

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Pinar, Mustafa �. "Linear programming via a quadratic penalty function." Mathematical Methods of Operations Research 44, no. 3 (October 1996): 345–70. http://dx.doi.org/10.1007/bf01193936.

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Qin, J., and D. T. Nguyen. "Generalized exponential penalty function for nonlinear programming." Computers & Structures 50, no. 4 (February 1994): 509–13. http://dx.doi.org/10.1016/0045-7949(94)90021-3.

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Christianson, B. "Geometric approach to Fletcher's ideal penalty function." Journal of Optimization Theory and Applications 84, no. 2 (February 1995): 433–41. http://dx.doi.org/10.1007/bf02192124.

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Knypiński, Łukasz. "Adaptation of the penalty function method to genetic algorithm in electromagnetic devices designing." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 38, no. 4 (July 1, 2019): 1285–94. http://dx.doi.org/10.1108/compel-01-2019-0010.

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Purpose The purpose of this paper is to elaborate the effective method of adaptation of the external penalty function to the genetic algorithm. Design/methodology/approach In the case of solving the optimization tasks with constraints using the external penalty function, the penalty term has a larger value than the primary objective function. The sigmoidal transformation is introduced to solve this problem. A new method of determining the value of the penalty coefficient in subsequent iterations associated with the changing penalty has been proposed. The proposed approach has been applied to the optimization of an electromagnetic linear actuator, and the mathematical model of the devices contains equations of the magnetic field, by taking into account the nonlinearity of ferromagnetic material. Findings The proposed new approach of the penalty function method consists in the reduction of the external penalty function in successive penalty iterations instead of its increase as it is in the classical method. In addition, the method of normalization of constraints during the formulation of optimization problem has a significant impact on the obtained results of optimization calculations. Originality/value The proposed approach can be applied to solve constrained optimization tasks in designing of electromagnetic devices.

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Nguyen, Binh Thanh, Yanqin Bai, Xin Yan, and Touna Yang. "Perturbed smoothing approach to the lower order exact penalty functions for nonlinear inequality constrained optimization." Tamkang Journal of Mathematics 50, no. 1 (March 30, 2018): 37–60. http://dx.doi.org/10.5556/j.tkjm.50.2019.2625.

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In this paper, we propose two new smoothing approximation to the lower order exact penalty functions for nonlinear optimization problems with inequality constraints. Error estimations between smoothed penalty function and nonsmooth penalty function are investigated. By using these new smooth penalty functions, a nonlinear optimization problem with inequality constraints is converted into a sequence of minimizations of continuously differentiable function. Then based on each of the smoothed penalty functions, we develop an algorithm respectively to finding an approximate optimal solution of the original constrained optimization problem and prove the convergence of the proposed algorithms. The effectiveness of the smoothed penalty functions is illustrated through three examples, which show that the algorithm seems efficient.

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Jiang, Yue, Afshin Mashadi-Hossein, Rachel Yost, Jeffrey Teoh, Ryan P. Larson, and Ronald J. Hause. "Statistical Learning Approaches for Predicting Lisocabtagene Maraleucel (liso-cel) Drug Product Composition from Donor-Selected Material Composition." Blood 134, Supplement_1 (November 13, 2019): 591. http://dx.doi.org/10.1182/blood-2019-125801.

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Introduction: Liso-cel is an investigational, anti-CD19, defined composition (4-1BB) chimeric antigen receptor (CAR) T cell product administered at a target dose of CD4+ and CD8+ CAR T cells. Liso-cel manufacturing process design includes controls that minimize between-lot variability, enabling robust CAR T cell generation across heterogeneous patient populations and disease indications. Characterization of liso-cel includes measurements of cell health, phenotype, and function. To demonstrate the robustness of the manufacturing process for which a contributor of variation is variability in incoming patient material, we developed a statistical method leveraging canonical correlation analysis (CCA) and lasso regression for predicting CAR T cell composition from measurements of cell health and phenotype in incoming patient T cells. These methods may also improve our understanding of donor variability effects on CAR T cell quality. Methods: CAR T cells were manufactured from autologous leukapheresis material in the TRANSCEND NHL 001 (NCT02631044) clinical trial. CCA and lasso models were constructed from 34 starting material attributes and 101 CD4 and CD8 clinical drug product attributes from 119 patients. CCA was implemented using prospective meta-analysis and telefit packages, and lasso regression was implemented using the glmnet package, both in R v3.5. Predictive accuracy was assessed for both methods using ten-fold cross validation. Results: CCA simultaneously found linear combinations of incoming patient T cell attributes and linear combinations of drug product attributes such that their correlation was maximized with an option of evoking a sparsity "penalty" to reduce model complexity by down-weighting (regularizing) attributes with small, independent effects. This approach enabled us to identify "meta-features" of primary components of incoming T cells strongly correlated with those of CAR T cells. Meta-feature 1 indicated that proportions of naïve CD4 T cells in starting T cell material were highly correlated with frequencies of naïve-like CD4 and CD8 CAR T cells post manufacturing (Figure 1). Meta-feature 2 revealed that naïve and central memory CD4 and CD8 T cell proportions in starting materials were correlated with naïve and central memory CD8 CAR T cells. Meta-feature 3 indicated that effector CD4 T cell proportions measured phenotypically in starting material were correlated with CD4 and CD8 CAR T cell effector functions, including antigen-specific cytokine production. Lastly, meta-feature 4 suggested that effector CD8 T cell proportions in starting material were correlated with CD8 CAR T cell effector functions. Because penalized CCA identified primary components of features correlated between incoming patient T cell material and manufactured CAR T cells, it can predict multiple attributes simultaneously, but with reduced capacity to most effectively predict a single attribute of interest. Hence, we implemented the lasso regression method that performs both variable selection and regularization to enhance the predictive accuracy of single attributes one at a time. Lasso regression models predict subsets of CAR T cell attributes more accurately than CCA and identify which starting T cell attributes are most important for prediction at the expense of having less power for predicting drug product attributes with limited relevant individual features in starting material. CCA achieved prediction accuracies up to an R2 of 42% for predicting CD4+ CAR+ naïve-like T cells (P=0.008), whereas lasso regression achieved up to an R2 of 67% for the same CAR T cell attribute (P=6×10-275). Both methods perform best at predicting classically naïve and TEMRA T cell compositions. Using CCA and lasso, we achieved nominally significant predictions for 53 of the 101 CAR T cell attributes using only 34 starting material attributes as input; the residual variation in the CAR T cell attributes independent of starting material composition was likely due to other patient or process variables. Conclusion: The application of statistical learning approaches to CAR T cell characterization data can enable us to predict CAR T cell characteristics that are directly related to donor-to-donor variability in incoming T cell material. These methods may allow us to develop adaptive manufacturing processes to improve treatment outcomes of autologous cellular therapies. Disclosures Jiang: Juno Therapeutics, a Celgene Company: Employment, Equity Ownership. Mashadi-Hossein:Celgene Corporation: Employment, Equity Ownership. Yost:Juno Therapeutics, a Celgene Company: Employment, Equity Ownership. Teoh:Juno Therapeutics, a Celgene Company: Employment, Equity Ownership. Larson:Juno Therapeutics, a Celgene Company: Employment, Equity Ownership. Hause:Juno Therapeutics, a Celgene Company: Employment, Equity Ownership.

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Lv, Yibing. "An exact penalty function approach for solving the linear bilevel multiobjective programming problem." Filomat 29, no. 4 (2015): 773–79. http://dx.doi.org/10.2298/fil1504773l.

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In this paper, a new penalty function approach is proposed for the linear bilevel multiobjective programming problem. Using the optimality conditions of the lower level problem, we transform the linear bilevel multiobjective programming problem into the corresponding linear multiobjective programming problem with complementary constraint. The complementary constraint is appended to the upper level objectives with a penalty. Then, we give via an exact penalty method an existence theorem of Pareto optimal solutions and propose an algorithm for the linear bilevel multiobjective programming problem. Numerical results showing viability of the penalty function approach are presented.

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Setiono, Rudy. "A Penalty-Function Approach for Pruning Feedforward Neural Networks." Neural Computation 9, no. 1 (January 1, 1997): 185–204. http://dx.doi.org/10.1162/neco.1997.9.1.185.

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This article proposes the use of a penalty function for pruning feedforward neural network by weight elimination. The penalty function proposed consists of two terms. The first term is to discourage the use of unnecessary connections, and the second term is to prevent the weights of the connections from taking excessively large values. Simple criteria for eliminating weights from the network are also given. The effectiveness of this penalty function is tested on three well-known problems: the contiguity problem, the parity problems, and the monks problems. The resulting pruned networks obtained for many of these problems have fewer connections than previously reported in the literature.

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Prajapati, Raju, Om Prakash Dubey, and Ranjit Pradhan. "ON NON-QUADRATIC PENALTY FUNCTION FOR NON-LINEAR PROGRAMMING PROBLEM WITH EQUALITY CONSTRAINTS." International Journal of Students' Research in Technology & Management 7, no. 1 (June 19, 2019): 23–28. http://dx.doi.org/10.18510/ijsrtm.2019.715.

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Purpose: The present paper focuses on the Non-Linear Programming Problem (NLPP) with equality constraints. NLPP with constraints could be solved by penalty or barrier methods. Methodology: We apply the penalty method to the NLPP with equality constraints only. The non-quadratic penalty method is considered for this purpose. We considered a transcendental i.e. exponential function for imposing the penalty due to the constraint violation. The unconstrained NLPP obtained in this way is then processed for further solution. An improved version of evolutionary and famous meta-heuristic Particle Swarm Optimization (PSO) is used for the same. The method is tested with the help of some test problems and mathematical software SCILAB. The solution is compared with the solution of the quadratic penalty method. Results: The results are also compared with some existing results in the literature.

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Lian, Shujun, Sitong Meng, and Yiju Wang. "An Objective Penalty Function-Based Method for Inequality Constrained Minimization Problem." Mathematical Problems in Engineering 2018 (June 6, 2018): 1–7. http://dx.doi.org/10.1155/2018/7484256.

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For inequality constrained minimization problem, we first propose a new exact nonsmooth objective penalty function and then apply a smooth technique to the penalty function to make it smooth. It is shown that any minimizer of the smoothing objective penalty function is an approximated solution of the original problem. Based on this, we develop a solution method for the inequality constrained minimization problem and prove its global convergence. Numerical experiments are provided to show the efficiency of the proposed method.

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