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About 10,000 papers for “Local optimum”

Local Optimum

Weili Wu, Zhao Zhang, Ding-Zhu Du · Springer optimization and its applications · 2026

Cited by 0

Optimum fusion of correlated local decisions

E. Drakopoulos, C.C. Lee · 2003

A distributed detection system consisting of a number of local detectors and a fusion center is considered. Each detector makes a decision for the underlying binary hypothesis testing problem based on its own observations and transmits its decision to the fusion center where the global decision is derived. The local decision rules are assumed to be given, but the local decisions are correlated. The correlation model is expressed by a finite number of conditional probabilities. The optimum decision fusion rule in the Neyman-Pearson sense is derived and analyzed. The performance of the distributed detection system versus the degree of correlation between the local decisions is studied for a special correlation structure that can be indexed by a single parameter. It is shown that system performance degrades as the degree of correlation between local decisions increases.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Cited by 13

Automatic Lane-Changing Trajectory Planning: From Self-Optimum to Local-Optimum

Yang Li, Linbo Li, Daiheng Ni et al. · IEEE Transactions on Intelligent Transportation Systems · 2022

Existing lane-changing (LC) algorithms in general prioritize self-interest above the benefits of others. We argue that an autonomous vehicle should be socially responsible during lane change without causing excessive impact on its surroundings. Thus, in this paper, we propose an LC algorithm that could generate a trajectory to optimize the overall benefits of the subject vehicle and others in its vicinity. This is mainly achieved by introducing Longitudinal Control Model to characterize the driving behaviors of neighboring vehicles, and drivers’ needs such as comfort, efficiency, and safety are simultaneously satisfied. We combine macro and micro comparative analysis to evaluate the advantage of our approach against existing algorithms. Our findings reveal that the vehicle controlled by our algorithm is capable of safely performing the LC maneuver while causing the least amount of impact. This is reflected in the decrease in total cost of all vehicles and the increase in traffic speed and throughput in its vicinity. In addition, we use HighD dataset for further validation and demonstrate that our algorithm is practically sound. Application of this research can lead to improved autonomous driving technology.

Cited by 24

A model for optimum prioritisation of energy storage use. I: Optimum local use of local generation

Wolf‐Gerrit Früh, C.J. Trigg, Riko Jacob · Renewable Energy and Power Quality Journal · 2022

Modelling the operation of energy storage systems such as batteries in an energy model is challenging as it requires estimation of current and future imbalances between supply and demand and the value of energy stored and later returned to the system. Here, an approach is developed which optimally prioritises the provision of stored energy to the system according to a specified criterion such as time-of-use tariff. At this stage, the model assumes perfect knowledge of future supply and demand as well as a hierarchy of utility to the system operator. Such an operator would specify a ranked list of times of need from the energy storage. The model will then allocate available energy from earlier times while reserving necessary space in the available storage. By progressing through the times of need in ranked order, the model ensures best optimum use of the stored energy. The results show that the proposed method is very robust and can calculate reliably the potential of an energy storage system in any energy model based on time steps of balancing generation and demand with the mediation of storage.

Cited by 1Open access

Hierarchical automatic lane-changing motion planning: from self-optimum to local-optimum

Yang Li, Linbo Li, Daiheng Ni et al. · arXiv (Cornell University) · 2021

In order to minimize the impact of lane change (LC) maneuver on surrounding traffic environment, a hierarchical automatic LC algorithm that could realize local optimum has been proposed. This algorithm consists of a tactical layer planner and an operational layer controller. The former generates a local-optimum trajectory. The comfort, efficiency, and safety of the LC vehicle and its surrounding vehicles are simultaneously satisfied in the optimization objective function. The later is designed based on vehicle kinematics model and the Model Predictive Control (MPC), which aims to minimize the tracking error and control increment. Combining macro-level and micro-level analysis, we verify the effectiveness of the proposed algorithm. Our results demonstrate that our proposed algorithm could greatly reduce the impact of LC maneuver on traffic flow. This is reflected in the decrease of total loss for nearby vehicles (such as discomfort and speed reduction), and the increase of traffic speed and throughput within the LC area. In addition, in order to guide the practical application of our algorithm, we employ the HighD dataset to validate the algorithm. This research could also be regarded as a preliminary foundational work to develop locally-optimal automatic LC algorithm. We anticipate that this research could provide valuable insights into autonomous driving technology.

Cited by 1Open access

Optimum regular local spline interpolation of signals

V. N. Isakov · Journal of «Almaz – Antey» Air and Defence Corporation · 2016

The study deals with an optimum approach to regular local signal interpolation by means of generalised splines. For the special case of local regular polynomial spline interpolation we derive quasi-optimal interpolation bases and provide corresponding recommendations dealing with selecting interpolation order and order of smoothness.

Cited by 1Open access

Local Optimum Triangulation for Unorganized Points Cloud

Nan Wang, Qiang Zhang, Donghent Zhou et al. · Research Journal of Applied Sciences Engineering and Technology · 2013

The triangulation of the unorganized data from the 3D scanner is always an important research subject of the CAD, computer geometry, reverse engineering and other areas. This paper introduces the current mainstream methods of triangulation that based on 3D points cloud data: plane projection method and direct method of division. This paper put forward a local optimization of direct triangulations method.

Cited by 0Open access

Handle local optimum traps in CBIR systems

Danzhou Liu, Kien A. Hua, Hao Cheng · Journal of International Crisis and Risk Communication Research · 2008

Existing CBIR systems, designed around query refinement based on relevance feedback, suffer from local optimum traps. That is, when the user is examining a relevant cluster surrounded by less relevant images, essentially the same set of images will be returned for the user to provide relevance feedback. Since the user would select the same query images again, the relevance feedback process gets trapped in a local optimum. This local-optimum trap problem may severely impair the overall retrieval performance of today's CBIR systems. In this paper, we therefore propose a simulated annealing-based approach to address this important issue. When a stuck-at-a-local-optimum occurs, we employ a neighborhood search technique (i.e., simulated annealing) to escape from the local optimum. We also propose an index structure to speed up such neighborhood search. Our experimental study confirms that our approach can efficiently address the local-optimum trap problem, and therefore can improve the effectiveness of existing CBIR systems.

Cited by 0Open access

Near optimum local image watermarking using Krawtchouk moments

George A. Papakostas, Efstratios Tsougenis, Dimitrios E. Koulouriotis · 2010

A novel methodology that deals with the optimum watermarking of image media by using Krawtchouk moments, in terms of locality, is presented in this paper. The proposed technique uses a simple genetic algorithm as optimization procedure, in order to find the best parameters set (p <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> , p <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ) defining the local behaviour of the Krawtchouk polynomials. The resulted near optimum parameters are used to construct the corresponding image's moments where the watermark information is inserted. These parameters are selected regarding the PSNR (Peak Signal to Noise Ratio) of the resulted watermarked image and the NC (Normalized Correlation) between the original and the reconstructed watermark, under several attacking conditions. Appropriate experiments have taken place and the results are promising, in the sense that the selection of the region, where the watermark is inserted can improve the quality of the watermarked image, but most of all it is robust to signal processing attacks.

Cited by 22

GABoost: Graph Alignment Boosting via Local Optimum Escape

Wei Liu, Wei Zhang, Hantao Zhao et al. · Proceedings of the ACM on Management of Data · 2024

Heterogeneous graphs provide a universal data structure for representing various kinds of structured data in numerous domains. The graph alignment problem aims to find the correspondences of vertices in different graphs, playing a fundamental role in many downstream tasks of heterogeneous graph mining. In recent years, many graph alignment methods have been proposed, ranging from classical optimization methods , spectral methods , to embedding learning based-methods . Due to the problem's complexity, the result found by most existing methods is either a heuristic solution or a critical point in the solution space. In this paper, we propose GABoost, a graph alignment boosting algorithm that takes as input an initial alignment between two heterogeneous graphs and outputs a boosted alignment via an iterative local-optimum-escape process. One of the distinctive features of GABoost is that it can be sequentially composed with any graph alignment methods to improve the output of upstream methods. To examine the effectiveness of GABoost, we select 7 upstream methods of graph alignment as well as 6 real-world datasets, and quantitatively investigate the degree to which GABoost boosts these methods. The results show that GABoost improves the alignment accuracy of the 7 upstream methods by 25.25% on average with acceptable time overhead.

Cited by 1

Refining mimicry: phenotypic variation tracks the local optimum

Claire Mérot, Yann Le Poul, Marc Théry et al. · Journal of Animal Ecology · 2016

Müllerian mimicry between chemically defended preys is a textbook example of natural selection favouring phenotypic convergence onto a shared warning signal. Studies of mimicry have concentrated on deciphering the ecological and genetic underpinnings of dramatic switches in mimicry association, producing a well-known mosaic distribution of mimicry patterns across geography. However, little is known about the accuracy of resemblance between natural comimics when the local phenotypic optimum varies. In this study, using analyses of wing shape, pattern and hue, we quantify multimodal phenotypic similarity between butterfly comimics sharing the so-called postman pattern in different localities with varying species composition. We show that subtle but consistent variation between populations of the localized species, Heliconius timareta thelxinoe, enhance resemblance to the abundant comimics which drive the mimicry in each locality. Those results suggest that rarer comimics track the changes in the phenotypic optimum caused by gradual changes in the composition of the mimicry community, providing insights into the process by which intraspecific diversity of mimetic pattern may arise. Furthermore, our results suggest a multimodal evolution of similarity, with coordinated convergence in different features of the phenotype such as wing outline, pattern and hue. Finally, multilocus genotyping allows estimating local hybridization rates between H. timareta and comimic H. melpomene in different populations, raising the hypothesis that mimicry refinement between closely related comimics may be enhanced by adaptive introgression at loci modifying the accuracy of resemblance.

Cited by 17Open access

Testing that a Local Optimum of the Likelihood is Globally Optimum using\n Reparameterized Embeddings

J.W. LeBlanc, Brian J. Thelen, Alfred O. Hero · arXiv (Cornell University) · 2019

Many mathematical imaging problems are posed as non-convex optimization\nproblems. When numerically tractable global optimization procedures are not\navailable, one is often interested in testing ex post facto whether or not a\nlocally convergent algorithm has found the globally optimal solution. When the\nproblem is formulated in terms of maximizing the likelihood function under a\nstatistical model for the measurements, one can construct a statistical test\nthat a local maximum is in fact the global maximum. A one-sided test is\nproposed for the case that the statistical model is a member of the generalized\nlocation family of probability distributions, a condition often satisfied in\nimaging and other inverse problems. We propose a general method for improving\nthe accuracy of the test by reparameterizing the likelihood function to embed\nits domain into a higher dimensional parameter space. We show that the proposed\nglobal maximum testing method results in improved accuracy and reduced\ncomputation for a physically-motivated joint-inverse problem arising in\ncamera-blur estimation.\n

Cited by 0Open access

Multimodal Optimization with the Local Optimum Ranking 2 Algorithm

Francisco Daniel Filip Duarte · Research Square · 2021

Abstract In optimization tasks, it is interesting to achieve a set of efficient solutions instead of one single output, in the case the best solution is not suitable. Many niching methods offer a diversified response, yet some important problems are common: (1) The most interesting solutions of each local optimum are not identified. Thus, the output is the overall population of solutions, which increases the work of the designer in verifying which solution is the most interesting. (2) Existing niching algorithms tend to distribute the solutions on the most promising regions, over-populating some local optima and sub-populating others, which leads to poor optimization.To solve these challenges, a novel niching method is presented, named local optimum ranking 2 (LOR2). This sorting methodology favors the exploration of a defined number of local optima and ranks each local population by objective value within each local optimum. Thus, is performed a multi-focus exploration, with an equalized number of solutions on each local optimum, while identifying which solutions are the local apices. To exemplify its application, the LOR2 algorithm is applied in the design optimization of a metallic cantilever beam. It achieves a set of efficient and diverse design configurations, offering both performance and diversity for structural design challenges.In addition, a second experiment describes how the algorithm can be applied to segment the domain of any function, into a mesh of similar sized or custom-sized elements. Thus, it can significantly simplify metamodels and reduce their computation time.

Cited by 0Open access