An alternative problem for backtracking and bounding

Rolfe, Timothy J.; Purdom, Paul W.

doi:10.1145/1041624.1041664

SIGCSE Bull.

2004

DOI: 10.1145/1041624.1041664

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An alternative problem for backtracking and bounding

Timothy J. Rolfe

Paul W. Purdom

Abstract: One of the programming problems in the 2002 Pacific Northwest regional ACM ICPC contest provides a new way to teach backtracking and also provides a very powerful example of a forward-looking bounding function. This article presents the problem, the bounding function, and timing information of implementations with and without the bounding function. It also provides the URL for access to the programs themselves.

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“…When cn + n − i<bestn, a larger clique cannot be found in the right subtree. The right node of Z can be cut off by the Pruning function [21].…”

mentioning

confidence: 99%

Intelligent Tennis Robot Based on a Deep Neural Network

Zeng

Jia

et al. 2019

Applied Sciences

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In this paper, an improved you only look once (YOLOv3) algorithm is proposed to make the detection effect better and improve the performance of a tennis ball detection robot. The depth-separable convolution network is combined with the original YOLOv3 and the residual block is added to extract the features of the object. The feature map output by the residual block is merged with the target detection layer through the shortcut layer to improve the network structure of YOLOv3. Both the original model and the improved model are trained by the same tennis ball data set. The results show that the recall is improved from 67.70% to 75.41% and the precision is 88.33%, which outperforms the original 77.18%. The recognition speed of the model is increased by half and the weight is reduced by half after training. All these features provide a great convenience for the application of the deep neural network in embedded devices. Our goal is that the robot is capable of picking up more tennis balls as soon as possible. Inspired by the maximum clique problem (MCP), the pointer network (Ptr-Net) and backtracking algorithm (BA) are utilized to make the robot find the place with the highest concentration of tennis balls. According to the training results, when the number of tennis balls is less than 45, the accuracy of determining the concentration of tennis balls can be as high as 80%.

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“…When cn + n − i<bestn, a larger clique cannot be found in the right subtree. The right node of Z can be cut off by the Pruning function [21].…”

mentioning

confidence: 99%

Intelligent Tennis Robot Based on a Deep Neural Network

Zeng

Jia

et al. 2019

Applied Sciences

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show abstract

Further thoughts on backtracking and bounding

González-Morris

2006

SIGCSE Bull.

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This report contains furthers analysis finding more accurate boundaries (pruning) to get a better performance for a specific backtracking algorithm based on the pattern problem. The problem statement is modest-sized to understand the combining criteria implemented reducing substantially the amount of time invested processing, and grows exponentially to the size of the problem making it expensive without applying any constraint.

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An alternative problem for backtracking and bounding

Cited by 2 publications

References 2 publications

Intelligent Tennis Robot Based on a Deep Neural Network

Intelligent Tennis Robot Based on a Deep Neural Network

Further thoughts on backtracking and bounding

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