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Journal articles on the topic 'Global and local optimizations'

Author: Grafiati

Published: 25 May 2024

Last updated: 31 July 2025

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1

Ravi Kiran, B., and Jean Serra. "Global–local optimizations by hierarchical cuts and climbing energies." Pattern Recognition 47, no. 1 (2014): 12–24. http://dx.doi.org/10.1016/j.patcog.2013.05.012.

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2

Wang, Wei, Xiaoshan Zhang, and Min Li. "A Filled Function Method Dominated by Filter for Nonlinearly Global Optimization." Journal of Applied Mathematics 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/245427.

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This work presents a filled function method based on the filter technique for global optimization. Filled function method is one of the effective methods for nonlinear global optimization, since it can effectively find a better minimizer. Filter technique is applied to local optimization methods for its excellent numerical results. In order to optimize the filled function method, the filter method is employed for global optimizations in this method. A new filled function is proposed first, and then the algorithm and its properties are proved. The numerical results are listed at the end.

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3

Li, Shuang, and Qiuwei Li. "Local and Global Convergence of General Burer-Monteiro Tensor Optimizations." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 9 (2022): 10266–74. http://dx.doi.org/10.1609/aaai.v36i9.21267.

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Tensor optimization is crucial to massive machine learning and signal processing tasks. In this paper, we consider tensor optimization with a convex and well-conditioned objective function and reformulate it into a nonconvex optimization using the Burer-Monteiro type parameterization. We analyze the local convergence of applying vanilla gradient descent to the factored formulation and establish a local regularity condition under mild assumptions. We also provide a linear convergence analysis of the gradient descent algorithm started in a neighborhood of the true tensor factors. Complementary t

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Zhang, Laiyong, Chunming Tu, Fan Xiao, Bei Liu, and Peiqiang Li. "Intrinsic Regularity Analysis and Optimization Control of Current Stress and RMS for Dual-Active Bridge Converter." Electronics 13, no. 23 (2024): 4802. https://doi.org/10.3390/electronics13234802.

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Currently, current stress optimization control and current effective value optimization control have become the mainstream methods for real-time optimization control of dual-active bridge converters. However, there is still a lack of systematic research on the internal optimization regularities between the two and their respective internal optimization regularities, as well as problems such as the complex derivation of the effective value optimization control variable function. To address these issues, this paper analyzes the optimization results of stress optimization, effective value optimiz

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5

Lazzaretto, Andrea, Andrea Toffolo, Matteo Morandin, and Michael R. von Spakovsky. "Criteria for the decomposition of energy systems in local/global optimizations." Energy 35, no. 2 (2010): 1157–63. http://dx.doi.org/10.1016/j.energy.2009.06.009.

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6

Yang, Dezhi, Xintong He, Jun Wang, Guoxian Yu, Carlotta Domeniconi, and Jinglin Zhang. "Federated Causality Learning with Explainable Adaptive Optimization." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 15 (2024): 16308–15. http://dx.doi.org/10.1609/aaai.v38i15.29566.

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Discovering the causality from observational data is a crucial task in various scientific domains. With increasing awareness of privacy, data are not allowed to be exposed, and it is very hard to learn causal graphs from dispersed data, since these data may have different distributions. In this paper, we propose a federated causal discovery strategy (FedCausal) to learn the unified global causal graph from decentralized heterogeneous data. We design a global optimization formula to naturally aggregate the causal graphs from client data and constrain the acyclicity of the global graph without e

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7

Zatolokin, Y. A., E. I. Vatutin, and V. S. Titov. "ALGORITHMIC OPTIMIZATION OF SOFTWARE IMPLEMENTATION OF ALGORITHMS FOR MULTIPLYING DENSE REAL MATRICES ON GRAPHICS PROCESSORS WITH OPENGL TECHNOLOGY SUPPORT." Proceedings of the Southwest State University 21, no. 5 (2017): 6–15. http://dx.doi.org/10.21869/2223-1560-2017-21-5-06-15.

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In the article was given statement of a problem of matrix multiplication. Is is show that desired problem can be simpl formulated but for its solving may be required both heuristic methods and set of algorithmic modifications relating to algorithmic and high-level software optimization taking into account the particular problem and allow to increase the multiplication performance. These include: a comparative analysis of the performance of the actions performed without GPU-specific optimizations and with optimizations, which showed that computations without optimizing the work with global GPU

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8

Bao, Rong, Yongdong Li, Hongguang Wang, and Chunliang Liu. "A Multi-Constrained Optimization Method for THz Backward Wave Oscillators." Applied Sciences 12, no. 20 (2022): 10583. http://dx.doi.org/10.3390/app122010583.

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The current design period for various backward wave oscillators (BWOs) is still at least several months. How to find the best structure parameters with an efficient and stable optimization method is a problem facing researchers in both scientific research and engineering work. In this paper, a non-randomized iterative optimization method is proposed. It applies orthogonal design methods to find local solutions that can provide optimal ‘gradient direction’ for several successive next iteration steps. An evaluation function is designed to distinguish the better ones from the local solutions in t

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9

Tohka, Jussi. "GLOBAL DEFORMABLE SURFACE OPTIMIZATION USING ADAPTIVE CONSTRAINTS AND PENALTIES." Image Analysis & Stereology 24, no. 1 (2011): 9. http://dx.doi.org/10.5566/ias.v24.p9-19.

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Deformable models are able to solve surface extraction problems challenged by image noise because imageindependent constraints are used to regularize the shape of the extracted surface. However, this ability of deformable models is shadowed by their application specificity, initialization sensitivity and the difficulty of the selection of proper values for user definable parameters. To overcome these problems restricting the automation of surface extraction, we present a new algorithm, named AdaCoP, for the global minimization of the energy of deformable surfaces. It iteratively performs const

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10

Jia, Jia, and Dejun Mu. "Low-Energy-Orientated Resource Scheduling in Cloud Computing by Particle Swarm Optimization." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 36, no. 2 (2018): 339–44. http://dx.doi.org/10.1051/jnwpu/20183620339.

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In order to reduce the energy cost in cloud computing, this paper represents a novel energy-orientated resource scheduling method based on particle swarm optimization. The energy cost model in cloud computing environment is studied first. The optimization of energy cost is then considered as a multiobjective optimization problem, which generates the Pareto optimization set. To solve this multiobjective optimization problem, the particle swarm optimization is involved. The states of one particle consist of both the allocation plan for servers and the frequency plans on servers. Each particle in

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11

Jianqi, Shi, Huang Yanhong, Li Ang, and Cai Fangda. "An optimal solution for software testing case generation based on particle swarm optimization." Open Physics 16, no. 1 (2018): 355–63. http://dx.doi.org/10.1515/phys-2018-0048.

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AbstractSearching based testing case generation technology converts the problem of testing case generation to function optimizations, through a fitness function, which is usually optimized using heuristic search algorithms. The particle swarm optimization (PSO) optimized testing case generation algorithm tends to lose population diversity of locally optimal solutions with low accuracy of local search. To overcome the above defects, a self-adaptive PSO based software testing case optimization algorithm is proposed. It adjusts the inertia weight dynamically according to the current iteration and

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12

Grunbacher, Sophie, Ramin Hasani, Mathias Lechner, Jacek Cyranka, Scott A. Smolka, and Radu Grosu. "On the Verification of Neural ODEs with Stochastic Guarantees." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 13 (2021): 11525–35. http://dx.doi.org/10.1609/aaai.v35i13.17372.

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We show that Neural ODEs, an emerging class of time-continuous neural networks, can be verified by solving a set of global-optimization problems. For this purpose, we introduce Stochastic Lagrangian Reachability (SLR), an abstraction-based technique for constructing a tight Reachtube (an over-approximation of the set of reachable states over a given time-horizon), and provide stochastic guarantees in the form of confidence intervals for the Reachtube bounds. SLR inherently avoids the infamous wrapping effect (accumulation of over-approximation errors) by performing local optimization steps to

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13

Meng, Yufeng, Jianhua He, Shichu Luo, Siqi Tao, and Jiancheng Xu. "An improved predator-prey particle swarm optimization algorithm for Nash equilibrium solution." PLOS ONE 16, no. 11 (2021): e0260231. http://dx.doi.org/10.1371/journal.pone.0260231.

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Focusing on the problem incurred during particle swarm optimization (PSO) that tends to fall into local optimization when solving Nash equilibrium solutions of games, as well as the problem of slow convergence when solving higher order game pay off matrices, this paper proposes an improved Predator-Prey particle swarm optimization (IPP-PSO) algorithm based on a Predator-Prey particle swarm optimization (PP-PSO) algorithm. First, the convergence of the algorithm is advanced by improving the distribution of the initial predator and prey. By improving the inertia weight of both predator and prey,

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14

Li, Li, Liu, and Ruan. "An Improved Bat Algorithm Based on Lévy Flights and Adjustment Factors." Symmetry 11, no. 7 (2019): 925. http://dx.doi.org/10.3390/sym11070925.

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This paper proposed an improved bat algorithm based on Lévy flights and adjustment factors (LAFBA). Dynamically decreasing inertia weight is added to the velocity update, which effectively balances the global and local search of the algorithm; the search strategy of Lévy flight is added to the position update, so that the algorithm maintains a good population diversity and the global search ability is improved; and the speed adjustment factor is added, which effectively improves the speed and accuracy of the algorithm. The proposed algorithm was then tested using 10 benchmark functions and 2 c

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15

Tang, Jianing, Shufeng Gong, Yanfeng Zhang, Chong Fu, and Ge Yu. "Distributed Single-Source Shortest Path with Only Local Relaxation." Electronics 13, no. 13 (2024): 2502. http://dx.doi.org/10.3390/electronics13132502.

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Finding the shortest path from a source vertex to any other vertices on a graph (single-source shortest path, SSSP) is used in a wide range of applications. With the rapid expansion of graph data volume, graphs are too large to be stored and processed in a standalone machine. Therefore, performing SSSP distributively in the computer clusters becomes an inevitable way. We found that the performance of existing distributed SSSP algorithms is limited by the communication cost between workers, which is caused by global relaxation. To eliminate the expensive communication cost, we propose an effici

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16

Beheshti, Zahra, Siti Mariyam Shamsuddin, and Sarina Sulaiman. "Fusion Global-Local-Topology Particle Swarm Optimization for Global Optimization Problems." Mathematical Problems in Engineering 2014 (2014): 1–19. http://dx.doi.org/10.1155/2014/907386.

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In recent years, particle swarm optimization (PSO) has been extensively applied in various optimization problems because of its structural and implementation simplicity. However, the PSO can sometimes find local optima or exhibit slow convergence speed when solving complex multimodal problems. To address these issues, an improved PSO scheme called fusion global-local-topology particle swarm optimization (FGLT-PSO) is proposed in this study. The algorithm employs both global and local topologies in PSO to jump out of the local optima. FGLT-PSO is evaluated using twenty (20) unimodal and multimo

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17

HONG, Liang, Sensen CHU, Shuangyun PENG, and Quanli XU. "Multiscale segmentation-optimized algorithm for high-spatial remote sensing imagery considering global and local optimizations." National Remote Sensing Bulletin 24, no. 12 (2020): 1464–75. http://dx.doi.org/10.11834/jrs.20208496.

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18

Addis, Bernardetta, Marco Locatelli, and Fabio Schoen. "Local optima smoothing for global optimization." Optimization Methods and Software 20, no. 4-5 (2005): 417–37. http://dx.doi.org/10.1080/10556780500140029.

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19

Locatelli, Marco, and Fabio Schoen. "Global optimization based on local searches." Annals of Operations Research 240, no. 1 (2015): 251–70. http://dx.doi.org/10.1007/s10479-015-2014-2.

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20

Locatelli, Marco, and Fabio Schoen. "Global optimization based on local searches." 4OR 11, no. 4 (2013): 301–21. http://dx.doi.org/10.1007/s10288-013-0251-2.

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21

Rebello, Carine M., Márcio A. F. Martins, José M. Loureiro, Alírio E. Rodrigues, Ana M. Ribeiro, and Idelfonso B. R. Nogueira. "From an Optimal Point to an Optimal Region: A Novel Methodology for Optimization of Multimodal Constrained Problems and a Novel Constrained Sliding Particle Swarm Optimization Strategy." Mathematics 9, no. 15 (2021): 1808. http://dx.doi.org/10.3390/math9151808.

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The present work proposes a novel methodology for an optimization procedure extending the optimal point to an optimal area based on an uncertainty map of deterministic optimization. To do so, this work proposes the deductions of a likelihood-based test to draw confidence regions of population-based optimizations. A novel Constrained Sliding Particle Swarm Optimization algorithm is also proposed that can cope with the optimization procedures characterized by multi-local minima. There are two open issues in the optimization literature, uncertainty analysis of the deterministic optimization and a

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22

Bračevac, Oliver, Guannan Wei, Songlin Jia, et al. "Graph IRs for Impure Higher-Order Languages: Making Aggressive Optimizations Affordable with Precise Effect Dependencies." Proceedings of the ACM on Programming Languages 7, OOPSLA2 (2023): 400–430. http://dx.doi.org/10.1145/3622813.

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Graph-based intermediate representations (IRs) are widely used for powerful compiler optimizations, either interprocedurally in pure functional languages, or intraprocedurally in imperative languages. Yet so far, no suitable graph IR exists for aggressive global optimizations in languages with both effects and higher-order functions: aliasing and indirect control transfers make it difficult to maintain sufficiently granular dependency information for optimizations to be effective. To close this long-standing gap, we propose a novel typed graph IR combining a notion of reachability types with a

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23

Roy, Jarrod A., David A. Papa, and Igor L. Markov. "Fine Control of Local Whitespace in Placement." VLSI Design 2008 (September 23, 2008): 1–10. http://dx.doi.org/10.1155/2008/517919.

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In modern design methodologies, a large fraction of chip area during placement is left unused by standard cells and allocated as “whitespace.” This is done for a variety of reasons including the need for subsequent buffer insertion, as a means to ensure routability, signal integrity, and low coupling capacitance between wires, and to improve yield through DFM optimizations. To this end, layout constraints often require a certain minimum fraction of whitespace in each region of the chip. Our work introduces several techniques for allocation of whitespace in global, detail, and incremental place

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24

LI, Wei, Wen-biao JIN, and Xian-qian XIAO. "Local-global algorithm for triangular mesh optimization." Journal of Computer Applications 31, no. 4 (2011): 1013–15. http://dx.doi.org/10.3724/sp.j.1087.2011.01013.

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25

Onodera, Tatsuhiro, Kenichi Tamura, and Keiichiro Yasuda. "Integrated Optimization Using Global and Local Modeling." Proceedings of the ISCIE International Symposium on Stochastic Systems Theory and its Applications 2013 (May 5, 2013): 137–43. http://dx.doi.org/10.5687/sss.2013.137.

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26

Cheng, Xuemin, Yongtian Wang, Qun Hao, and Masaki Isshiki. "Global and local optimization for optical systems." Optik 117, no. 3 (2006): 111–17. http://dx.doi.org/10.1016/j.ijleo.2005.06.007.

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27

Deissenberg, Christophe. "The global failure of local preference optimization." Computers & Mathematics with Applications 25, no. 10-11 (1993): 161–72. http://dx.doi.org/10.1016/0898-1221(93)90290-c.

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28

Floudas, C. A., and H. Th Jongen. "Global Optimization: Local Minima and Transition Points." Journal of Global Optimization 32, no. 3 (2005): 409–15. http://dx.doi.org/10.1007/s10898-004-0865-1.

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29

Bagirov, Adil M., Alexander M. Rubinov, and Jiapu Zhang. "Local Optimization Method with Global Multidimensional Search." Journal of Global Optimization 32, no. 2 (2005): 161–79. http://dx.doi.org/10.1007/s10898-004-2700-0.

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30

Ahandani, Morteza Alinia, Mohammad-Taghi Vakil-Baghmisheh, and Mohammad Talebi. "Hybridizing local search algorithms for global optimization." Computational Optimization and Applications 59, no. 3 (2014): 725–48. http://dx.doi.org/10.1007/s10589-014-9652-1.

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31

First, Z., S. T. Hackman, and U. Passy. "Local-global properties of bifunctions." Journal of Optimization Theory and Applications 73, no. 2 (1992): 279–97. http://dx.doi.org/10.1007/bf00940182.

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32

Bodyanskiy, Yevgeniy, Alina Shafronenko, and Iryna Pliss. "Credibilistic fuzzy clustering based on evolutionary method of crazy cats." System research and information technologies, no. 3 (November 18, 2021): 110–19. http://dx.doi.org/10.20535/srit.2308-8893.2021.3.09.

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The problem of fuzzy clustering of large datasets that are sent for processing in both batch and online modes, based on a credibilistic approach, is considered. To find the global extremum of the credibilistic fuzzy clustering goal function, the modification of the swarm algorithm of crazy cats swarms was introduced, that combined the advantages of evolutionary algorithms and a global random search. It is shown that different search modes are generated by a unified mathematical procedure, some cases of which are known algorithms for both local and global optimizations. The proposed approach is

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33

Kang, Chao, Jihui Xu, and Yuan Bian. "Affine Formation Maneuver Control for Multi-Agent Based on Optimal Flight System." Applied Sciences 14, no. 6 (2024): 2292. http://dx.doi.org/10.3390/app14062292.

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The use of affine maneuver control to maintain the desired configuration of unmanned aerial vehicle (UAV) swarms has been widely practiced. Nevertheless, the lack of capability to interact with obstacles and navigate autonomously could potentially limit its extension. To address this problem, we present an innovative formation flight system featuring a virtual leader that seamlessly integrates global control and local control, effectively addressing the limitations of existing methods that rely on fixed configuration changes to accommodate real-world constraints. To enhance the elasticity of a

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34

Garcia-Palomares, U. M., F. J. Gonzalez-Castaño, and J. C. Burguillo-Rial. "A Combined Global & Local Search (CGLS) Approach to Global Optimization." Journal of Global Optimization 34, no. 3 (2006): 409–26. http://dx.doi.org/10.1007/s10898-005-3249-2.

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35

Zhan, Dawei, Jiachang Qian, and Yuansheng Cheng. "Balancing global and local search in parallel efficient global optimization algorithms." Journal of Global Optimization 67, no. 4 (2016): 873–92. http://dx.doi.org/10.1007/s10898-016-0449-x.

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36

KERDKAEW, JUTAMAS, RABIAN WANGKEEREE, and GUE MYUNG LEE. "On optimality conditions for robust weak sharp solution in uncertain optimizations." Carpathian Journal of Mathematics 36, no. 3 (2020): 443–52. http://dx.doi.org/10.37193/cjm.2020.03.12.

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In this paper, we investigate the robust optimization problem involving nonsmooth and nonconvex real-valued functions. We firstly establish a necessary condition for the local robust weak sharp solution of considered problem under a constraint qualification. These optimality conditions are presented in terms of multipliers and Mordukhovich subdifferentials of the related functions. Then, by employing the robust version of the (KKT) condition, and some appropriate generalized convexity conditions, we also obtain some sufficient conditions for the global robust weak sharp solutions of the proble

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37

Awotunde, Abeeb A. "GLOCAL: A global-local optimization template for multiple history-matched reservoir parameters." Journal of Petroleum Science and Engineering 154 (June 2017): 1–18. http://dx.doi.org/10.1016/j.petrol.2017.04.007.

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38

Jiang, Meirui, Zirui Wang, and Qi Dou. "HarmoFL: Harmonizing Local and Global Drifts in Federated Learning on Heterogeneous Medical Images." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 1 (2022): 1087–95. http://dx.doi.org/10.1609/aaai.v36i1.19993.

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Multiple medical institutions collaboratively training a model using federated learning (FL) has become a promising solution for maximizing the potential of data-driven models, yet the non-independent and identically distributed (non-iid) data in medical images is still an outstanding challenge in real-world practice. The feature heterogeneity caused by diverse scanners or protocols introduces a drift in the learning process, in both local (client) and global (server) optimizations, which harms the convergence as well as model performance. Many previous works have attempted to address the non-

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39

Mao, Hu Ping, Yi Zhong Wu, and Li Ping Chen. "Data Driven Multivariate Adaptive Regression Splines Based Simulation Optimization." Applied Mechanics and Materials 44-47 (December 2010): 3800–3806. http://dx.doi.org/10.4028/www.scientific.net/amm.44-47.3800.

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This paper proposes a data driven based optimization approach which combines augmented Lagrangian method, MARS with effective data processing. In the approach, an expensive simulation run is required if and only if a nearby data point does not exist in the cumulatively growing database. Over time the database matures and is enriched as more and more optimizations have been performed. MARS is a self-adaptive regression process, which fits in with the multidimensional problems, and uses a modified recursive partitioning strategy to simplify high-dimensional problems into smaller yet highly accur

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40

Alenezi, Fayadh, and K. C. Santosh. "Geometric Regularized Hopfield Neural Network for Medical Image Enhancement." International Journal of Biomedical Imaging 2021 (January 22, 2021): 1–12. http://dx.doi.org/10.1155/2021/6664569.

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One of the major shortcomings of Hopfield neural network (HNN) is that the network may not always converge to a fixed point. HNN, predominantly, is limited to local optimization during training to achieve network stability. In this paper, the convergence problem is addressed using two approaches: (a) by sequencing the activation of a continuous modified HNN (MHNN) based on the geometric correlation of features within various image hyperplanes via pixel gradient vectors and (b) by regulating geometric pixel gradient vectors. These are achieved by regularizing proposed MHNNs under cohomology, wh

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41

Fu, Shichen, Detao Yang, Zenghui Mei, and Weixiong Zheng. "Progress in Construction Robot Path-Planning Algorithms: Review." Applied Sciences 15, no. 3 (2025): 1165. https://doi.org/10.3390/app15031165.

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Construction robots are increasingly becoming a significant force in the digital transformation and intelligent upgrading of the construction industry. Path planning is crucial for the advancement of building robot technology. Based on the understanding of construction site information, this paper categorizes path-planning algorithms into two types: global path-planning and local path-planning. Local path planning is further divided into classical algorithms, intelligent algorithms, and reinforcement learning algorithms. Using this classification framework, this paper summarizes the latest res

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42

Ibraheem, Kais I., and Hisham M. Khudhur. "Optimization algorithm based on the Euler method for solving fuzzy nonlinear equations." Eastern-European Journal of Enterprise Technologies 1, no. 4 (115) (2022): 13–19. http://dx.doi.org/10.15587/1729-4061.2022.252014.

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In a variety of engineering, scientific challenges, mathematics, chemistry, physics, biology, machine learning, deep learning, regression classification, computer science, programming, artificial intelligence, in the military, medical and engineering industries, robotics and smart cars, fuzzy nonlinear equations play a critical role. As a result, in this paper, an Optimization Algorithm based on the Euler Method approach for solving fuzzy nonlinear equations is proposed. In mathematics and computer science, the Euler approach (sometimes called the forward Euler method) is a first-order numeric

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43

Kais, I. Ibraheem, and M. Khudhur Hisham. "Optimization algorithm based on the Euler method for solving fuzzy nonlinear equations." Eastern-European Journal of Enterprise Technologies 1, no. 4 (115) (2022): 13–19. https://doi.org/10.15587/1729-4061.2022.252014.

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In a variety of engineering, scientific challenges, mathematics, chemistry, physics, biology, machine learning, deep learning, regression classification, computer science, programming, artificial intelligence, in the military, medical and engineering industries, robotics and smart cars, fuzzy nonlinear equations play a critical role. As a result, in this paper, an Optimization Algorithm based on the Euler Method approach for solving fuzzy nonlinear equations is proposed. In mathematics and computer science, the Euler approach (sometimes called the forward Euler method) is a first-order numeric

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44

Molero, J. M., E. M. Garzon, I. Garcia, and A. Plaza. "Analysis and Optimizations of Global and Local Versions of the RX Algorithm for Anomaly Detection in Hyperspectral Data." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 6, no. 2 (2013): 801–14. http://dx.doi.org/10.1109/jstars.2013.2238609.

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45

Li, Wei, Huajun Gong, and Ruigang Yang. "Fast Texture Mapping Adjustment via Local/Global Optimization." IEEE Transactions on Visualization and Computer Graphics 25, no. 6 (2019): 2296–303. http://dx.doi.org/10.1109/tvcg.2018.2831220.

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46

Xingping Dong, Jianbing Shen, Ling Shao, and Ming-Hsuan Yang. "Interactive Cosegmentation Using Global and Local Energy Optimization." IEEE Transactions on Image Processing 24, no. 11 (2015): 3966–77. http://dx.doi.org/10.1109/tip.2015.2456636.

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47

Pandini, D., L. T. Pileggi, and A. J. Strojwas. "Global and local congestion optimization in technology mapping." IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 22, no. 4 (2003): 498–506. http://dx.doi.org/10.1109/tcad.2003.809646.

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48

Gergel, Victor, Vladimir Grishagin, and Ruslan Israfilov. "Local Tuning in Nested Scheme of Global Optimization." Procedia Computer Science 51 (2015): 865–74. http://dx.doi.org/10.1016/j.procs.2015.05.216.

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49

Kuiper, Jan H., Rik Huiskes, and Harrie Weinans. "Bone remodeling: comparing local adaptation and global optimization." Journal of Biomechanics 25, no. 7 (1992): 807. http://dx.doi.org/10.1016/0021-9290(92)90569-m.

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de Oliveira, Leonardo Correia, Silvana M. B. Afonso, and Bernardo Horowitz. "Global/local optimization strategies combined for waterflooding problems." Journal of the Brazilian Society of Mechanical Sciences and Engineering 38, no. 7 (2015): 2051–62. http://dx.doi.org/10.1007/s40430-015-0461-y.

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