scholarly journals Using Collective Behavior of Coupled Oscillators for Solving DCOP

2019 ◽  
Vol 64 ◽  
pp. 987-1023
Author(s):  
Allan R. Leite ◽  
Fabricio Enembreck
Keyword(s):  
Coupled Oscillators ◽  
Large Scale ◽  
Approximate Solutions ◽  
Scalable Algorithms ◽  
Solution Quality ◽  
Distributed Constraint Optimization ◽  
Constraint Networks ◽  
Novel Approach ◽  
Convergence Process ◽  
Distributed Constraint Optimization Problem

The distributed constraint optimization problem (DCOP) has emerged as one of the most promising coordination techniques in multiagent systems. However, because DCOP is known to be NP-hard, the existing DCOP techniques are often unsuitable for large-scale applications, which require distributed and scalable algorithms to deal with severely limited computing and communication. In this paper, we present a novel approach to provide approximate solutions for large-scale, complex DCOPs. This approach introduces concepts of synchronization of coupled oscillators for speeding up the convergence process towards high-quality solutions. We propose a new anytime local search DCOP algorithm, called Coupled Oscillator OPTimization (COOPT), which amounts to iteratively solving a DCOP by agents exchanging local information that brings them to a consensus. We empirically evaluate COOPT on constraint networks involving hundreds of variables with different topologies, domains, and densities. Our experimental results demonstrate that COOPT outperforms other incomplete state-of-the-art DCOP algorithms, especially in terms of the agents' communication cost and solution quality.

scholarly journals A Generic Approach to Accelerating Belief Propagation Based Incomplete Algorithms for DCOPs via a Branch-and-Bound Technique

2019 ◽  
Vol 33 ◽  
pp. 6038-6045
Author(s):  
Ziyu Chen ◽  
Xingqiong Jiang ◽  
Yanchen Deng ◽  
Dingding Chen ◽  
Zhongshi He
Keyword(s):  
Branch And Bound ◽  
Large Scale ◽  
Belief Propagation ◽  
Optimization Problems ◽  
Search Space ◽  
Solution Quality ◽  
Distributed Constraint Optimization ◽  
Speed Up ◽  
Branch And Bound Technique ◽  
Constraint Optimization Problems

Belief propagation approaches, such as Max-Sum and its variants, are important methods to solve large-scale Distributed Constraint Optimization Problems (DCOPs). However, for problems with n-ary constraints, these algorithms face a huge challenge since their computational complexity scales exponentially with the number of variables a function holds. In this paper, we present a generic and easy-touse method based on a branch-and-bound technique to solve the issue, called Function Decomposing and State Pruning (FDSP). We theoretically prove that FDSP can provide monotonically non-increasing upper bounds and speed up belief propagation based incomplete DCOP algorithms without an effect on solution quality. Also, our empirically evaluation indicates that FDSP can reduce 97% of the search space at least and effectively accelerate Max-Sum, compared with the state-of-the-art.

scholarly journals A Local Stability Supported Parallel Distributed Constraint Optimization Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-9
Author(s):  
Duan Peibo ◽  
Zhang Changsheng ◽  
Zhang Bin
Keyword(s):  
Optimization Algorithm ◽  
Local Stability ◽  
Large Scale ◽  
Research Direction ◽  
Initial Solution ◽  
Constraint Optimization ◽  
Distributed Constraint Optimization ◽  
Distributed Constraint Optimization Problem ◽  
New Research ◽  
New Criterion

This paper presents a new distributed constraint optimization algorithm called LSPA, which can be used to solve large scale distributed constraint optimization problem (DCOP). Different from the access of local information in the existing algorithms, a new criterion called local stability is defined and used to evaluate which is the next agent whose value needs to be changed. The propose of local stability opens a new research direction of refining initial solution by finding key agents which can seriously effect global solution once they modify assignments. In addition, the construction of initial solution could be received more quickly without repeated assignment and conflict. In order to execute parallel search, LSPA finds final solution by constantly computing local stability of compatible agents. Experimental evaluation shows that LSPA outperforms some of the state-of-the-art incomplete distributed constraint optimization algorithms, guaranteeing better solutions received within ideal time.

scholarly journals Solving Multiagent Networks using Distributed Constraint Optimization

AI Magazine ◽  
2008 ◽  
Vol 29 (3) ◽  
pp. 47 ◽  
Author(s):  
Jonathan P. Pearce ◽  
Milind Tambe ◽  
Rajiv Maheswaran
Keyword(s):  
Optimal Algorithm ◽  
Local Optimum ◽  
Constraint Optimization ◽  
Solution Quality ◽  
Worst Case ◽  
Distributed Constraint Optimization ◽  
Constraint Graph ◽  
Distributed Constraint Optimization Problem ◽  
The Cost

In many cooperative multiagent domains, the effect of local interactions between agents can be compactly represented as a network structure. Given that agents are spread across such a network, agents directly interact only with a small group of neighbors. A distributed constraint optimization problem (DCOP) is a useful framework to reason about such networks of agents. Given agents’ inability to communicate and collaborate in large groups in such networks, we focus on an approach called k-optimality for solving DCOPs. In this approach, agents form groups of one or more agents until no group of k or fewer agents can possibly improve the DCOP solution; we define this type of local optimum, and any algorithm guaranteed to reach such a local optimum, as k-optimal. The article provides an overview of three key results related to koptimality. The first set of results gives worst-case guarantees on the solution quality of k-optima in a DCOP. These guarantees can help determine an appropriate k-optimal algorithm, or possibly an appropriate constraint graph structure, for agents to use in situations where the cost of coordination between agents must be weighed against the quality of the solution reached. The second set of results gives upper bounds on the number of k-optima that can exist in a DCOP. These results are useful in domains where a DCOP must generate a set of solutions rather than a single solution. Finally, we sketch algorithms for k-optimality and provide some experimental results for 1-, 2- and 3-optimal algorithms for several types of DCOPs.

A Novel Approach to Publishing Tasks for Collaboratively Crowdsourcing Workflows

2019 ◽  
Vol 29 (06) ◽  
pp. 763-790
Author(s):  
Wenjun Tang ◽  
Rong Chen ◽  
Shikai Guo
Keyword(s):  
Execution Time ◽  
Large Scale ◽  
Approximate Solutions ◽  
Constraint Solving ◽  
Total Cost ◽  
Large Scale Problems ◽  
Novel Approach ◽  
Heuristic Strategies ◽  
Time Requirements ◽  
Reduce Execution Time

In recent years, crowdsourcing has gradually become a promising way of using netizens to accomplish tiny tasks on, or even complex works through crowdsourcing workflows that decompose them into tiny ones to publish sequentially on the crowdsourcing platforms. One of the significant challenges in this process is how to determine the parameters for task publishing. Still some technique applied constraint solving to select the optimal tasks parameters so that the total cost of completing all tasks is minimized. However, experimental results show that computational complexity makes these tools unsuitable for solving large-scale problems because of its excessive execution time. Taking into account the real-time requirements of crowdsourcing, this study uses a heuristic algorithm with four heuristic strategies to solve the problem in order to reduce execution time. The experiment results also show that the proposed heuristic strategies produce good quality approximate solutions in an acceptable timeframe.

Green synthesis, characterization of silver sulfide nanoparticles and antibacterial activity evaluation

2019 ◽  
Author(s):  
Chem Int
Keyword(s):  
Antibacterial Activity ◽  
Large Scale ◽  
Research Work ◽  
Silver Sulfide ◽  
Morphological Properties ◽  
Effective Diameter ◽  
Novel Approach ◽  
Transmission Electron ◽  
Green Route ◽  
Silver Sulfide Nanoparticles

This research work presents a facile and green route for synthesis silver sulfide (Ag2SNPs) nanoparticles from silver nitrate (AgNO3) and sodium sulfide nonahydrate (Na2S.9H2O) in the presence of rosemary leaves aqueous extract at ambient temperature (27 oC). Structural and morphological properties of Ag2SNPs nanoparticles were analyzed by X-ray diffraction (XRD) and transmission electron microscopy (TEM). The surface Plasmon resonance for Ag2SNPs was obtained around 355 nm. Ag2SNPs was spherical in shape with an effective diameter size of 14 nm. Our novel approach represents a promising and effective method to large scale synthesis of eco-friendly antibacterial activity silver sulfide nanoparticles.

scholarly journals Fractional ridge regression: a fast, interpretable reparameterization of ridge regression

GigaScience ◽  
2020 ◽  
Vol 9 (12) ◽  
Author(s):  
Ariel Rokem ◽  
Kendrick Kay
Keyword(s):  
Ridge Regression ◽  
Large Scale ◽  
Imaging Data ◽  
Regularization Technique ◽  
Large Scale Data ◽  
Novel Approach ◽  
Manual Exploration ◽  
L2 Norm ◽  
Software Implementations ◽  
Brain Imaging Data

Abstract Background Ridge regression is a regularization technique that penalizes the L2-norm of the coefficients in linear regression. One of the challenges of using ridge regression is the need to set a hyperparameter (α) that controls the amount of regularization. Cross-validation is typically used to select the best α from a set of candidates. However, efficient and appropriate selection of α can be challenging. This becomes prohibitive when large amounts of data are analyzed. Because the selected α depends on the scale of the data and correlations across predictors, it is also not straightforwardly interpretable. Results The present work addresses these challenges through a novel approach to ridge regression. We propose to reparameterize ridge regression in terms of the ratio γ between the L2-norms of the regularized and unregularized coefficients. We provide an algorithm that efficiently implements this approach, called fractional ridge regression, as well as open-source software implementations in Python and matlab (https://github.com/nrdg/fracridge). We show that the proposed method is fast and scalable for large-scale data problems. In brain imaging data, we demonstrate that this approach delivers results that are straightforward to interpret and compare across models and datasets. Conclusion Fractional ridge regression has several benefits: the solutions obtained for different γ are guaranteed to vary, guarding against wasted calculations; and automatically span the relevant range of regularization, avoiding the need for arduous manual exploration. These properties make fractional ridge regression particularly suitable for analysis of large complex datasets.

scholarly journals Novel approach for large‐scale monitoring of submerged aquatic vegetation – a nationwide example from Sweden

2021 ◽  
Author(s):  
Silvia Huber ◽  
Lars B. Hansen ◽  
Lisbeth T. Nielsen ◽  
Mikkel L. Rasmussen ◽  
Jonas Sølvsteen ◽  
...  
Keyword(s):  
Large Scale ◽  
Submerged Aquatic Vegetation ◽  
Aquatic Vegetation ◽  
Novel Approach

scholarly journals Joint regression and learning from pairwise rankings for personalized image aesthetic assessment

2021 ◽  
Author(s):  
Jin Zhou ◽  
Qing Zhang ◽  
Jian-Hao Fan ◽  
Wei Sun ◽  
Wei-Shi Zheng
Keyword(s):  
Large Scale ◽  
Assessment Model ◽  
Generic Model ◽  
Small Subset ◽  
Deep Convolutional Neural Networks ◽  
Personal Taste ◽  
Hinge Loss ◽  
Novel Approach ◽  
Large Scale Dataset ◽  
Image Pairs

AbstractRecent image aesthetic assessment methods have achieved remarkable progress due to the emergence of deep convolutional neural networks (CNNs). However, these methods focus primarily on predicting generally perceived preference of an image, making them usually have limited practicability, since each user may have completely different preferences for the same image. To address this problem, this paper presents a novel approach for predicting personalized image aesthetics that fit an individual user’s personal taste. We achieve this in a coarse to fine manner, by joint regression and learning from pairwise rankings. Specifically, we first collect a small subset of personal images from a user and invite him/her to rank the preference of some randomly sampled image pairs. We then search for the K-nearest neighbors of the personal images within a large-scale dataset labeled with average human aesthetic scores, and use these images as well as the associated scores to train a generic aesthetic assessment model by CNN-based regression. Next, we fine-tune the generic model to accommodate the personal preference by training over the rankings with a pairwise hinge loss. Experiments demonstrate that our method can effectively learn personalized image aesthetic preferences, clearly outperforming state-of-the-art methods. Moreover, we show that the learned personalized image aesthetic benefits a wide variety of applications.

scholarly journals Reservoir-Induced Land Deformation: Case Study from the Grand Ethiopian Renaissance Dam

2021 ◽  
Vol 13 (5) ◽  
pp. 874
Author(s):  
Yu Chen ◽  
Mohamed Ahmed ◽  
Natthachet Tangdamrongsub ◽  
Dorina Murgulet
Keyword(s):  
Land Surface ◽  
Large Scale ◽  
Surface Deformation ◽  
Vertical Displacement ◽  
Nile River ◽  
Reservoir Volume ◽  
Novel Approach ◽  
Land Deformation ◽  
Large Scale Land ◽  
The Impact

The Nile River stretches from south to north throughout the Nile River Basin (NRB) in Northeast Africa. Ethiopia, where the Blue Nile originates, has begun the construction of the Grand Ethiopian Renaissance Dam (GERD), which will be used to generate electricity. However, the impact of the GERD on land deformation caused by significant water relocation has not been rigorously considered in the scientific research. In this study, we develop a novel approach for predicting large-scale land deformation induced by the construction of the GERD reservoir. We also investigate the limitations of using the Gravity Recovery and Climate Experiment Follow On (GRACE-FO) mission to detect GERD-induced land deformation. We simulated three land deformation scenarios related to filling the expected reservoir volume, 70 km3, using 5-, 10-, and 15-year filling scenarios. The results indicated: (i) trends in downward vertical displacement estimated at −17.79 ± 0.02, −8.90 ± 0.09, and −5.94 ± 0.05 mm/year, for the 5-, 10-, and 15-year filling scenarios, respectively; (ii) the western (eastern) parts of the GERD reservoir are estimated to move toward the reservoir’s center by +0.98 ± 0.01 (−0.98 ± 0.01), +0.48 ± 0.00 (−0.48 ± 0.00), and +0.33 ± 0.00 (−0.33 ± 0.00) mm/year, under the 5-, 10- and 15-year filling strategies, respectively; (iii) the northern part of the GERD reservoir is moving southward by +1.28 ± 0.02, +0.64 ± 0.01, and +0.43 ± 0.00 mm/year, while the southern part is moving northward by −3.75 ± 0.04, −1.87 ± 0.02, and −1.25 ± 0.01 mm/year, during the three examined scenarios, respectively; and (iv) the GRACE-FO mission can only detect 15% of the large-scale land deformation produced by the GERD reservoir. Methods and results demonstrated in this study provide insights into possible impacts of reservoir impoundment on land surface deformation, which can be adopted into the GERD project or similar future dam construction plans.

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