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Статті в журналах з теми "Adaptive Penalty"

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Автор: Grafiati

Опубліковано: 14 березня 2023

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1

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

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

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2

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

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3

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

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

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4

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

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

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5

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

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6

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

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7

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

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8

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

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Анотація:

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

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9

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

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

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10

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

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

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11

Wang, Lei, Juntao Li, Juanfang Liu, and Mingming Chang. "RAMRSGL: A Robust Adaptive Multinomial Regression Model for Multicancer Classification." Computational and Mathematical Methods in Medicine 2021 (May 25, 2021): 1–13. http://dx.doi.org/10.1155/2021/5584684.

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In view of the challenges of the group Lasso penalty methods for multicancer microarray data analysis, e.g., dividing genes into groups in advance and biological interpretability, we propose a robust adaptive multinomial regression with sparse group Lasso penalty (RAMRSGL) model. By adopting the overlapping clustering strategy, affinity propagation clustering is employed to obtain each cancer gene subtype, which explores the group structure of each cancer subtype and merges the groups of all subtypes. In addition, the data-driven weights based on noise are added to the sparse group Lasso penalty, combining with the multinomial log-likelihood function to perform multiclassification and adaptive group gene selection simultaneously. The experimental results on acute leukemia data verify the effectiveness of the proposed method.

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12

Wang, Yadi, Wenbo Zhang, Minghu Fan, Qiang Ge, Baojun Qiao, Xianyu Zuo, and Bingbing Jiang. "Regression with adaptive lasso and correlation based penalty." Applied Mathematical Modelling 105 (May 2022): 179–96. http://dx.doi.org/10.1016/j.apm.2021.12.016.

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13

Stevens, Kay B., and J. Randall Price. "Adaptive Behavior, Mental Retardation, and the Death Penalty." Journal of Forensic Psychology Practice 6, no. 3 (October 25, 2006): 1–29. http://dx.doi.org/10.1300/j158v06n03_01.

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14

Tessema, B., and G. G. Yen. "An Adaptive Penalty Formulation for Constrained Evolutionary Optimization." IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans 39, no. 3 (May 2009): 565–78. http://dx.doi.org/10.1109/tsmca.2009.2013333.

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15

Zhou, Hongbing, Xianlin Zeng, and Yiguang Hong. "Adaptive Exact Penalty Design for Constrained Distributed Optimization." IEEE Transactions on Automatic Control 64, no. 11 (November 2019): 4661–67. http://dx.doi.org/10.1109/tac.2019.2902612.

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16

Barbosa, Helio J. C., and Afonso C. C. Lemonge. "A new adaptive penalty scheme for genetic algorithms." Information Sciences 156, no. 3-4 (November 2003): 215–51. http://dx.doi.org/10.1016/s0020-0255(03)00177-4.

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17

Emerson, Denise, Wei Zhou, and Selwyn Piramuthu. "Goodwill, inventory penalty, and adaptive supply chain management." European Journal of Operational Research 199, no. 1 (November 2009): 130–38. http://dx.doi.org/10.1016/j.ejor.2008.11.007.

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18

Lemonge, Afonso C. C., Helio J. C. Barbosa, and Heder S. Bernardino. "Variants of an adaptive penalty scheme for steady-state genetic algorithms in engineering optimization." Engineering Computations 32, no. 8 (November 2, 2015): 2182–215. http://dx.doi.org/10.1108/ec-07-2014-0158.

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Анотація:

Purpose – The purpose of this paper is to propose variants of an adaptive penalty scheme for steady-state genetic algorithms applied to constrained engineering optimization problems. Design/methodology/approach – For each constraint a penalty parameter is adaptively computed along the evolution according to information extracted from the current population such as the existence of feasible individuals and the level of violation of each constraint. The adaptive penalty method (APM), as originally proposed, computes the constraint violations of the initial population, and updates the penalty coefficient of each constraint after a given number of new individuals are inserted in the population. A second variant, called sporadic APM with constraint violation accumulation, works by accumulating the constraint violations during a given insertion of new offspring into the population, updating the penalty coefficients, and fixing the penalty coefficients for the next generations. The APM with monotonic penalty coefficients is the third variation, where the penalty coefficients are calculated as in the original method, but no penalty coefficient is allowed to have its value reduced along the evolutionary process. Finally, the penalty coefficients are defined by using a weighted average between the current value of a coefficient and the new value predicted by the method. This variant is called the APM with damping. Findings – The paper checks new variants of an APM for evolutionary algorithms; variants of an APM, for a steady-state genetic algorithm based on an APM for a generational genetic algorithm, largely used in the literature previously proposed by two co-authors of this manuscript; good performance of the proposed APM in comparison with other techniques found in the literature; innovative and general strategies to handle constraints in the field of evolutionary computation. Research limitations/implications – The proposed algorithm has no limitations and can be applied in a large number of evolutionary algorithms used to solve constrained optimization problems. Practical implications – The proposed algorithm can be used to solve real world problems in engineering as can be viewed in the references, presented in this manuscript, that use the original (APM) strategy. The performance of these variants is examined using benchmark problems of mechanical and structural engineering frequently discussed in the literature. Originality/value – It is the first extended analysis of the variants of the APM submitted for possible publication in the literature, applied to real world engineering optimization problems.

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19

Ma, Yecheng Jason, Andrew Shen, Osbert Bastani, and Jayaraman Dinesh. "Conservative and Adaptive Penalty for Model-Based Safe Reinforcement Learning." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 5 (June 28, 2022): 5404–12. http://dx.doi.org/10.1609/aaai.v36i5.20478.

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Reinforcement Learning (RL) agents in the real world must satisfy safety constraints in addition to maximizing a reward objective. Model-based RL algorithms hold promise for reducing unsafe real-world actions: they may synthesize policies that obey all constraints using simulated samples from a learned model. However, imperfect models can result in real-world constraint violations even for actions that are predicted to satisfy all constraints. We propose Conservative and Adaptive Penalty (CAP), a model-based safe RL framework that accounts for potential modeling errors by capturing model uncertainty and adaptively exploiting it to balance the reward and the cost objectives. First, CAP inflates predicted costs using an uncertainty-based penalty. Theoretically, we show that policies that satisfy this conservative cost constraint are guaranteed to also be feasible in the true environment. We further show that this guarantees the safety of all intermediate solutions during RL training. Further, CAP adaptively tunes this penalty during training using true cost feedback from the environment. We evaluate this conservative and adaptive penalty-based approach for model-based safe RL extensively on state and image-based environments. Our results demonstrate substantial gains in sample-efficiency while incurring fewer violations than prior safe RL algorithms. Code is available at: https://github.com/Redrew/CAP

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20

Liu, Qiegen, Biao Xiong, and Minghui Zhang. "Adaptive Sparse Norm and Nonlocal Total Variation Methods for Image Smoothing." Mathematical Problems in Engineering 2014 (2014): 1–18. http://dx.doi.org/10.1155/2014/426125.

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In computer vision and graphics, it is challenging to decompose various texture/structure patterns from input images. It is well recognized that how edges are defined and how this prior information guides smoothing are two keys in determining the quality of image smoothing. While many different approaches have been reported in the literature, sparse norm and nonlocal schemes are two promising tools. In this study, by integrating a texture measure as the spatially varying data-fidelity/smooth-penalty weight into the sparse norm and nonlocal total variation models, two new methods are presented for feature/structure-preserving filtering. The first one is a generalized relative total variation (i.e., GRTV) method, which improves the contrast-preserving and edge stiffness-enhancing capabilities of the RTV by extending the range of the penalty function’s norm from 1 to [0, 1]. The other one is a nonlocal version of generalized RTV (i.e., NLGRTV) for which the key idea is to use a modified texture-measure as spatially varying penalty weight and to replace the local candidate pixels with the nonlocal set in the smooth-penalty term. It is shown that NLGRTV substantially improves the performance of decomposition for regions with faint pixel-boundary.

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21

Gao, Cuixia, Naiyan Wang, Qi Yu, and Zhihua Zhang. "A Feasible Nonconvex Relaxation Approach to Feature Selection." Proceedings of the AAAI Conference on Artificial Intelligence 25, no. 1 (August 4, 2011): 356–61. http://dx.doi.org/10.1609/aaai.v25i1.7921.

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Variable selection problems are typically addressed under apenalized optimization framework. Nonconvex penalties such as the minimax concave plus (MCP) and smoothly clipped absolute deviation(SCAD), have been demonstrated to have the properties of sparsity practically and theoretically. In this paper we propose a new nonconvex penalty that we call exponential-type penalty. The exponential-type penalty is characterized by a positive parameter,which establishes a connection with the ell0 and ell1 penalties.We apply this new penalty to sparse supervised learning problems. To solve to resulting optimization problem, we resort to a reweighted ell1 minimization method. Moreover, we devise an efficient method for the adaptive update of the tuning parameter. Our experimental results are encouraging. They show that the exponential-type penalty is competitive with MCP and SCAD.

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22

Clevenhaus, Anna, Matthias Ehrhardt, Michael Günther, and Daniel Ševčovič. "Pricing American Options with a Non-Constant Penalty Parameter." Journal of Risk and Financial Management 13, no. 6 (June 13, 2020): 124. http://dx.doi.org/10.3390/jrfm13060124.

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As the American early exercise results in a free boundary problem, in this article we add a penalty term to obtain a partial differential equation, and we also focus on an improved definition of the penalty term for American options. We replace the constant penalty parameter with a time-dependent function. The novelty and advantage of our approach consists in introducing a bounded, time-dependent penalty function, enabling us to construct an efficient, stable, and adaptive numerical approximation scheme, while in contrast, the existing standard approach to the penalisation of the American put option-free boundary problem involves a constant penalty parameter. To gain insight into the accuracy of our proposed extension, we compare the solution of the extension to standard reference solutions from the literature. This illustrates the improvement of using a penalty function instead of a penalising constant.

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23

Wang, Bing-Chuan, Han-Xiong Li, Yun Feng, and Wen-Jing Shen. "An adaptive fuzzy penalty method for constrained evolutionary optimization." Information Sciences 571 (September 2021): 358–74. http://dx.doi.org/10.1016/j.ins.2021.03.055.

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24

McWhirter, J. G. "Data-domain penalty function algorithm for stabilised adaptive beamforming." IEE Proceedings - Radar, Sonar and Navigation 147, no. 6 (2000): 265. http://dx.doi.org/10.1049/ip-rsn:20000753.

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25

Monticelli, A., and Wen-Hsuing E. Liu. "Adaptive movement penalty method for the Newton optimal power." IEEE Power Engineering Review 11, no. 2 (February 1991): 57. http://dx.doi.org/10.1109/mper.1991.88735.

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26

Elvander, F., T. Kronvall, S. I. Adalbjörnsson, and A. Jakobsson. "An adaptive penalty multi-pitch estimator with self-regularization." Signal Processing 127 (October 2016): 56–70. http://dx.doi.org/10.1016/j.sigpro.2016.02.015.

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27

Shi, Kun, та Peng Shi. "Adaptive sparse Volterra system identification with ℓ0‐norm penalty". Signal Processing 91, № 10 (жовтень 2011): 2432–36. http://dx.doi.org/10.1016/j.sigpro.2011.04.028.

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28

Matias, João, Aldina Correia, Pedro Mestre, Carlos Serodio, Pedro Couto, Christophe Teixeira, and Pedro Melo-Pinto. "Adaptive Penalty and Barrier function based on Fuzzy Logic." Expert Systems with Applications 42, no. 19 (November 2015): 6777–83. http://dx.doi.org/10.1016/j.eswa.2015.04.070.

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29

Lin, C. Y., and W. H. Wu. "Self-organizing adaptive penalty strategy in constrained genetic search." Structural and Multidisciplinary Optimization 26, no. 6 (April 1, 2004): 417–28. http://dx.doi.org/10.1007/s00158-003-0373-9.

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30

Mahdavi-Amiri, N., and M. Shaeiri. "An adaptive competitive penalty method for nonsmooth constrained optimization." Numerical Algorithms 75, no. 1 (October 10, 2016): 305–36. http://dx.doi.org/10.1007/s11075-016-0208-6.

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31

Kusakci, Ali Osman, and Mehmet Can. "An adaptive penalty based covariance matrix adaptation–evolution strategy." Computers & Operations Research 40, no. 10 (October 2013): 2398–417. http://dx.doi.org/10.1016/j.cor.2013.03.013.

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32

Carvalho, Érica Da Costa Reis, José Pedro Gonçalves Carvalho, Heder Soares Bernardino, Patrícia Habib Hallak, and Afonso Celso de Castro Lemonge. "AN ADAPTIVE CONSTRAINT HANDLING TECHNIQUE FOR PARTICLE SWARM IN CONSTRAINED OPTIMIZATION PROBLEMS." Revista CIATEC-UPF 8, no. 1 (June 17, 2016): 39. http://dx.doi.org/10.5335/ciatec.v8i1.6023.

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Анотація:

Nature inspired meta-heuristics are largely used to solve optimization problems. However, these techniques should be adapted when solving constrained optimization problems, which are common in real world situations. Here an adaptive penalty approach (called Adaptive Penalty Method, APM) is combined with a particle swarm optimization (PSO) technique to solve constrained optimization problems. This approach is analyzed using a benchmark of test-problems and 5 mechanical engineering problems. Moreover, three variants of APM are considered in the computational experiments. Comparison results show that the proposed algorithm obtains a promising performance on the majority of the test problems

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33

French, Donald A., Stig Larsson, and Ricardo H. Nochetto. "Pointwise a Posteriori Error Analysis for an Adaptive Penalty Finite Element Method for the Obstacle Problem." Computational Methods in Applied Mathematics 1, no. 1 (2001): 18–38. http://dx.doi.org/10.2478/cmam-2001-0002.

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Abstract Finite element approximations based on a penalty formulation of the elliptic obstacle problem are analyzed in the maximum norm. A posteriori error estimates, which involve a residual of the approximation and a spatially variable penalty parameter, are derived in the cases of both smooth and rough obstacles. An adaptive algorithm is suggested and implemented in one dimension.

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34

Cao, Zhanglong, David Bryant, Timothy C. A. Molteno, Colin Fox, and Matthew Parry. "V-Spline: An Adaptive Smoothing Spline for Trajectory Reconstruction." Sensors 21, no. 9 (May 6, 2021): 3215. http://dx.doi.org/10.3390/s21093215.

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Trajectory reconstruction is the process of inferring the path of a moving object between successive observations. In this paper, we propose a smoothing spline—which we name the V-spline—that incorporates position and velocity information and a penalty term that controls acceleration. We introduce an adaptive V-spline designed to control the impact of irregularly sampled observations and noisy velocity measurements. A cross-validation scheme for estimating the V-spline parameters is proposed, and, in simulation studies, the V-spline shows superior performance to existing methods. Finally, an application of the V-spline to vehicle trajectory reconstruction in two dimensions is given, in which the penalty term is allowed to further depend on known operational characteristics of the vehicle.

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35

BERNARDI, C., V. GIRAULT, and F. HECHT. "A POSTERIORI ANALYSIS OF A PENALTY METHOD AND APPLICATION TO THE STOKES PROBLEM." Mathematical Models and Methods in Applied Sciences 13, no. 11 (November 2003): 1599–628. http://dx.doi.org/10.1142/s0218202503003057.

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Анотація:

We derive an a posteriori error estimate for an abstract saddle-point problem when a penalty term is added to stabilize the variational formulation, the aim being to optimize the choice of the penalty parameter. As an application, we consider a discretization of the Stokes problem obtained by combining the penalty technique and the finite element method, we perform its a posteriori analysis in a detailed way and present some numerical experiments on adaptive meshes which are in good agreement with the results of the analysis and confirm its interest.

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36

Sun, Bingbing, and Tariq Alkhalifah. "Adaptive traveltime inversion." GEOPHYSICS 84, no. 4 (July 1, 2019): U13—U29. http://dx.doi.org/10.1190/geo2018-0595.1.

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Анотація:

We have developed a method to obtain a misfit function for robust waveform inversion. In this method, called adaptive traveltime inversion (ATI), a matching filter that matches predicted data to measured data is computed. If the velocity model is relatively accurate, the resulting matching filter is close to a Dirac delta function. Its traveltime shift, which characterizes the defocusing of the matching filter, is computed by minimization of the crosscorrelation between a penalty function such as [Formula: see text] and the matching filter. ATI is constructed by minimization of the least-squares errors of the calculated traveltime shift. Further analysis indicates that the resulting traveltime shift corresponds to a first-order moment, the mean value of the resulting matching filter distribution. We extend ATI to a more general misfit function formula by computing different order moment of the resulting matching filter distribution. Choosing the penalty function in adaptive waveform inversion (AWI) as [Formula: see text], the misfit function of AWI is the second-order moment, the variance of the resulting matching filter distribution with zero mean. Because our ATI method is based on a global comparison using deconvolution, such as AWI, it can resolve the “cycle skipping” issue. We evaluate our ATI misfit function and compare it with state-of-the-art options such as least-squares inversion (L2 norm), wave-equation traveltime inversion, and AWI using schematic examples before moving to more complex examples, such as the Marmousi model. For the Marmousi model, starting with a 1D [Formula: see text] model, with data without low frequencies (no energy below 3 Hz), a meaningful estimation of the P-wave velocity model is recovered. Our ATI misfit function (first-order moment) indicates comparable performance with the AWI misfit function (the second-order moment). We also include a real data example from the Gulf of Mexico to demonstrate the effectiveness of our method.

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37

Harnpornchai, Napat, Kittawit Autchariyapanitkul, Jirakom Sirisrisakulchai, and Songsak Sriboonchitta. "Optimal Outpatient Appointment System with Uncertain Parameters Using Adaptive-Penalty Genetic Algorithm." Journal of Advanced Computational Intelligence and Intelligent Informatics 19, no. 5 (September 20, 2015): 585–92. http://dx.doi.org/10.20965/jaciii.2015.p0585.

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Анотація:

The optimal number of doctors and appointment interval for an outpatient appointment system in a class of individual block/fixed interval are determined using an adaptive-penalty Genetic Algorithm. The length of service time for doctor consultation, the time required for the laboratory tests, and the time deviating from the appointment time are modelled by random variables. No-show patients are also included in the system. Using the adaptive penalty scheme, optimization constraints are automatically and numerically handled. The solution methodology is readily applicable to other appointment systems. The study has a significant implication from the viewpoint of economic and risk management of health care service.

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Liu, Tianxiang, Zhaosong Lu, Xiaojun Chen, and Yu-Hong Dai. "An exact penalty method for semidefinite-box-constrained low-rank matrix optimization problems." IMA Journal of Numerical Analysis 40, no. 1 (October 29, 2018): 563–86. http://dx.doi.org/10.1093/imanum/dry069.

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Abstract This paper considers a matrix optimization problem where the objective function is continuously differentiable and the constraints involve a semidefinite-box constraint and a rank constraint. We first replace the rank constraint by adding a non-Lipschitz penalty function in the objective and prove that this penalty problem is exact with respect to the original problem. Next, for the penalty problem we present a nonmonotone proximal gradient (NPG) algorithm whose subproblem can be solved by Newton’s method with globally quadratic convergence. We also prove the convergence of the NPG algorithm to a first-order stationary point of the penalty problem. Furthermore, based on the NPG algorithm, we propose an adaptive penalty method (APM) for solving the original problem. Finally, the efficiency of an APM is shown via numerical experiments for the sensor network localization problem and the nearest low-rank correlation matrix problem.

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39

Liu, Xin, Bangxin Zhao, and Wenqing He. "Simultaneous Feature Selection and Classification for Data-Adaptive Kernel-Penalized SVM." Mathematics 8, no. 10 (October 20, 2020): 1846. http://dx.doi.org/10.3390/math8101846.

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Simultaneous feature selection and classification have been explored in the literature to extend the support vector machine (SVM) techniques by adding penalty terms to the loss function directly. However, it is the kernel function that controls the performance of the SVM, and an imbalance in the data will deteriorate the performance of an SVM. In this paper, we examine a new method of simultaneous feature selection and binary classification. Instead of incorporating the standard loss function of the SVM, a penalty is added to the data-adaptive kernel function directly to control the performance of the SVM, by firstly conformally transforming the kernel functions of the SVM, and then re-conducting an SVM classifier based on the sparse features selected. Both convex and non-convex penalties, such as least absolute shrinkage and selection (LASSO), moothly clipped absolute deviation (SCAD) and minimax concave penalty (MCP) are explored, and the oracle property of the estimator is established accordingly. An iterative optimization procedure is applied as there is no analytic form of the estimated coefficients available. Numerical comparisons show that the proposed method outperforms the competitors considered when data are imbalanced, and it performs similarly to the competitors when data are balanced. The method can be easily applied in medical images from different platforms.

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Awad, Fouad H., Ali Al-kubaisi, and Maha Mahmood. "Large-scale timetabling problems with adaptive tabu search." Journal of Intelligent Systems 31, no. 1 (January 1, 2022): 168–76. http://dx.doi.org/10.1515/jisys-2022-0003.

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Abstract Timetabling problems are specific types of scheduling problems that deal with assigning certain events to the timeslots. This assigning is subject to certain hard constraints that should be achieved to get a feasible timetable and soft constraints that must meet as many as possible during forming a feasible schedule. This paper introduces an adaptive tabu search. Eleven benchmark datasets of the year 2002 are applied to show the effectiveness of the introduced algorithm. These datasets consist of 5-small, 5-medium, and 1-large dataset. As compared to other methods from previous works, the proposed algorithm produces excellent timetables, in comparison with the algorithms as well as the current results, the mathematical results showed the high effectiveness of the suggested algorithm. It has a minor deficit on the medium or the small problem adaptive Tabu, and the tabu search relies on the tabu list and penalty cost when the change in the penalty cost is checked; if it is still unchanged for the period of iterations (1,000 iterations), the tabu list reduces automatically by (−2); furthermore, the tabu list remains constant.

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Zhu, Xiaodi, Yanfeng Li, Jia Sun, Houjin Chen, and Jinlei Zhu. "Unsupervised domain adaptive person re-identification via camera penalty learning." Multimedia Tools and Applications 80, no. 10 (February 2, 2021): 15215–32. http://dx.doi.org/10.1007/s11042-021-10589-6.

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42

Wang, Degen, Tong Wang, Weichen Cui, and Cheng Liu. "Adaptive Support-Driven Sparse Recovery STAP Method with Subspace Penalty." Remote Sensing 14, no. 18 (September 7, 2022): 4463. http://dx.doi.org/10.3390/rs14184463.

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Detecting a moving target is an attractive topic in many fields, such as remote sensing. Space-time adaptive processing (STAP) plays a key role in detecting moving targets in strong clutter backgrounds for airborne early warning radar systems. However, STAP suffers serious clutter suppression performance loss when the number of training samples is insufficient due to the inhomogeneous clutter environment. In this article, an efficient sparse recovery STAP algorithm is proposed. First, inspired by the relationship between multiple sparse Bayesian learning (M-SBL) and subspace-based hybrid greedy algorithms, a new optimization objective function based on a subspace penalty is established. Second, the closed-form solution of each minimization step is obtained through the alternating minimization algorithm, which can guarantee the convergence of the algorithm. Finally, a restart strategy is used to adaptively update the support, which reduces the computational complexity. Simulation results show that the proposed algorithm has excellent performance in clutter suppression, convergence speed and running time with insufficient training samples.

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43

Wang, Guodong. "Multiplicative Noise Removal Using TVL1 Norm and Adaptive Penalty Parameter." Journal of Information and Computational Science 11, no. 11 (July 20, 2014): 3993–4001. http://dx.doi.org/10.12733/jics20104240.

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44

Benner, Peter, and Hamdullah Yücel. "Adaptive Symmetric Interior Penalty Galerkin Method for Boundary Control Problems." SIAM Journal on Numerical Analysis 55, no. 2 (January 2017): 1101–33. http://dx.doi.org/10.1137/15m1034507.

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45

Zhu, Daan, Moe Razaz, and Mark Fisher. "An adaptive algorithm for image restoration using combined penalty functions." Pattern Recognition Letters 27, no. 12 (September 2006): 1336–41. http://dx.doi.org/10.1016/j.patrec.2006.01.009.

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46

Hoppe, R. H. W., G. Kanschat, and T. Warburton. "Convergence Analysis of an Adaptive Interior Penalty Discontinuous Galerkin Method." SIAM Journal on Numerical Analysis 47, no. 1 (January 2009): 534–50. http://dx.doi.org/10.1137/070704599.

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47

Zhu, Daan, Moe Razaz, and Richard Lee. "Adaptive penalty likelihood for reconstruction of multidimensional confocal microscopy images." Computerized Medical Imaging and Graphics 29, no. 5 (July 2005): 319–31. http://dx.doi.org/10.1016/j.compmedimag.2004.12.004.

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48

Monticelli, A., and W. H. E. Liu. "Adaptive movement penalty method for the Newton optimal power flow." IEEE Transactions on Power Systems 7, no. 1 (1992): 334–42. http://dx.doi.org/10.1109/59.141723.

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49

Haris, Asad, Ali Shojaie, and Noah Simon. "Nonparametric regression with adaptive truncation via a convex hierarchical penalty." Biometrika 106, no. 1 (December 13, 2018): 87–107. http://dx.doi.org/10.1093/biomet/asy056.

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SUMMARY We consider the problem of nonparametric regression with a potentially large number of covariates. We propose a convex, penalized estimation framework that is particularly well suited to high-dimensional sparse additive models and combines the appealing features of finite basis representation and smoothing penalties. In the case of additive models, a finite basis representation provides a parsimonious representation for fitted functions but is not adaptive when component functions possess different levels of complexity. In contrast, a smoothing spline-type penalty on the component functions is adaptive but does not provide a parsimonious representation. Our proposal simultaneously achieves parsimony and adaptivity in a computationally efficient way. We demonstrate these properties through empirical studies and show that our estimator converges at the minimax rate for functions within a hierarchical class. We further establish minimax rates for a large class of sparse additive models. We also develop an efficient algorithm that scales similarly to the lasso with the number of covariates and sample size.

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50

Coit, David W., Alice E. Smith, and David M. Tate. "Adaptive Penalty Methods for Genetic Optimization of Constrained Combinatorial Problems." INFORMS Journal on Computing 8, no. 2 (May 1996): 173–82. http://dx.doi.org/10.1287/ijoc.8.2.173.

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