Reoptimizing the Strengthened Metric TSP on Multiple Edge Weight Modifications

2012 ◽  
pp. 111-122 ◽  
Author(s):  
Annalisa D’Andrea ◽  
Guido Proietti
Keyword(s):  
Multiple Edge ◽  
Edge Weight

scholarly journals Improving the Smoothed Complexity of FLIP for Max Cut Problems

2021 ◽  
Vol 17 (3) ◽  
pp. 1-38
Author(s):  
Ali Bibak ◽  
Charles Carlson ◽  
Karthekeyan Chandrasekaran
Keyword(s):  
General Framework ◽  
Edge Weight ◽  
Complete Graphs ◽  
Worst Case ◽  
Weight Density ◽  
Complete Problems ◽  
Substantial Progress ◽  
Run Time ◽  
Optimum Solution ◽  
Arbitrary Graphs

Finding locally optimal solutions for MAX-CUT and MAX- k -CUT are well-known PLS-complete problems. An instinctive approach to finding such a locally optimum solution is the FLIP method. Even though FLIP requires exponential time in worst-case instances, it tends to terminate quickly in practical instances. To explain this discrepancy, the run-time of FLIP has been studied in the smoothed complexity framework. Etscheid and Röglin (ACM Transactions on Algorithms, 2017) showed that the smoothed complexity of FLIP for max-cut in arbitrary graphs is quasi-polynomial. Angel, Bubeck, Peres, and Wei (STOC, 2017) showed that the smoothed complexity of FLIP for max-cut in complete graphs is ( O Φ 5 n 15.1 ), where Φ is an upper bound on the random edge-weight density and Φ is the number of vertices in the input graph. While Angel, Bubeck, Peres, and Wei’s result showed the first polynomial smoothed complexity, they also conjectured that their run-time bound is far from optimal. In this work, we make substantial progress toward improving the run-time bound. We prove that the smoothed complexity of FLIP for max-cut in complete graphs is O (Φ n 7.83 ). Our results are based on a carefully chosen matrix whose rank captures the run-time of the method along with improved rank bounds for this matrix and an improved union bound based on this matrix. In addition, our techniques provide a general framework for analyzing FLIP in the smoothed framework. We illustrate this general framework by showing that the smoothed complexity of FLIP for MAX-3-CUT in complete graphs is polynomial and for MAX - k - CUT in arbitrary graphs is quasi-polynomial. We believe that our techniques should also be of interest toward showing smoothed polynomial complexity of FLIP for MAX - k - CUT in complete graphs for larger constants k .

scholarly journals Stochastic MMSIF of multiple edge cracks FGMs plates subjected to combined loading using XFEM

2020 ◽  
Vol 7 (1) ◽  
pp. 35-47
Author(s):  
Achchhe Lal ◽  
Kundan Mishra
Keyword(s):  
Perturbation Technique ◽  
Uncertain System ◽  
Second Order ◽  
Functionally Graded ◽  
Combined Loading ◽  
Multiple Edge ◽  
Extended Finite Element ◽  
Graded Materials ◽  
Second Order Statistics ◽  
Mode Stress

AbstractThe second order statistics of multiple edge crack functionally graded materials (FGMs) under tensile, shear and combined loading assuming uncertain system parameters is presented in this paper. The uncertain parameters used under the present study are the material properties, and crack parameters such as crack length and crack angle. In this present analysis extended finite element method (XFEM) is used. The stochastic analysis is carried out using second order perturbation technique (SOPT) for the evaluation of mean and coefficient of variance (COV) of mixed mode stress intensity factor (MMSIF).

scholarly journals MULTI-SCALE SEGMENTATION OF HIGH RESOLUTION REMOTE SENSING IMAGES BY INTEGRATING MULTIPLE FEATURES

2017 ◽  
Vol XLII-1/W1 ◽  
pp. 247-255 ◽  
Author(s):  
Y. Di ◽  
G. Jiang ◽  
L. Yan ◽  
H. Liu ◽  
S. Zheng
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Minimum Spanning Tree ◽  
Network Evolution ◽  
Edge Weight ◽  
Remote Sensing Images ◽  
Canny Operator ◽  
Multiple Features ◽  
Multi Scale ◽  
Initial Segmentation

Most of multi-scale segmentation algorithms are not aiming at high resolution remote sensing images and have difficulty to communicate and use layers’ information. In view of them, we proposes a method of multi-scale segmentation of high resolution remote sensing images by integrating multiple features. First, Canny operator is used to extract edge information, and then band weighted distance function is built to obtain the edge weight. According to the criterion, the initial segmentation objects of color images can be gained by Kruskal minimum spanning tree algorithm. Finally segmentation images are got by the adaptive rule of Mumford–Shah region merging combination with spectral and texture information. The proposed method is evaluated precisely using analog images and ZY-3 satellite images through quantitative and qualitative analysis. The experimental results show that the multi-scale segmentation of high resolution remote sensing images by integrating multiple features outperformed the software eCognition fractal network evolution algorithm (highest-resolution network evolution that FNEA) on the accuracy and slightly inferior to FNEA on the efficiency.

scholarly journals A MINIMUM SPANNING TREE BASED METHOD FOR UAV IMAGE SEGMENTATION

2016 ◽  
Vol III-7 ◽  
pp. 111-117
Author(s):  
Ping Wang ◽  
Zheng Wei ◽  
Weihong Cui ◽  
Zhiyong Lin
Keyword(s):  
Image Segmentation ◽  
Coastal Area ◽  
Minimum Spanning Tree ◽  
Edge Weight ◽  
Segmentation Method ◽  
Optimal Criterion ◽  
The Rich ◽  
Uav Images ◽  
Uav Image ◽  
Scale Control

This paper proposes a Minimum Span Tree (MST) based image segmentation method for UAV images in coastal area. An edge weight based optimal criterion (merging predicate) is defined, which based on statistical learning theory (SLT). And we used a scale control parameter to control the segmentation scale. Experiments based on the high resolution UAV images in coastal area show that the proposed merging predicate can keep the integrity of the objects and prevent results from over segmentation. The segmentation results proves its efficiency in segmenting the rich texture images with good boundary of objects.

scholarly journals Research on the Extraction of Image Edge Information in Convolutional Neural Networks

2021 ◽  
Vol 2083 (3) ◽  
pp. 032015
Author(s):  
Guanru Zou ◽  
Yulin Luo ◽  
Zefeng Feng
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Input Image ◽  
Edge Weight ◽  
Edge Information ◽  
Image Dimension ◽  
Original Dataset ◽  
Image Edge ◽  
Accuracy Rates ◽  
Object Move

Abstract Convolutional neural network is an important neural network model in deep learning and a common algorithm in computer vision problems. From the perspective of practical application scenarios, this paper studies whether padding in convolutional neural network convolution layer weakens the image edge information. In order to eliminate the background factor, this paper select MNIST dataset as the research object, move the 0-9 digital image to the specified image edge by clearing the white area pixels in the specified direction, and use OpenCV to realize bilinear interpolation to scale the image to ensure that the image dimension is 28×28. The convolution neural network is built to train the original dataset and the processed dataset, and the accuracy rates are 0.9892 and 0.1082 respectively. In the comparative experiment, padding cannot solve the problem of weakening the image edge weight well. In the actual digital recognition scene, it is necessary to consider whether the core recognition area in the input image is at the edge of the image.

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