Solving Large-Scale Least Squares Semidefinite Programming by Alternating Direction Methods

2011 ◽  
Vol 32 (1) ◽  
pp. 136-152 ◽  
Author(s):  
Bingsheng He ◽  
Minghua Xu ◽  
Xiaoming Yuan
Keyword(s):  
Semidefinite Programming ◽  
Least Squares ◽  
Large Scale ◽  
Alternating Direction Methods ◽  
Alternating Direction

scholarly journals How Tour Guides’ Professional Competencies Influence on Service Quality of Tour Guiding and Tourist Satisfaction: An Exploratory Research

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Yi-Chien Lin ◽  
Mei-Lan Lin ◽  
Yi-Cheng Chen
Keyword(s):  
Service Quality ◽  
Conceptual Framework ◽  
Least Squares ◽  
Partial Least Squares ◽  
Large Scale ◽  
Online Survey ◽  
Professional Competencies ◽  
Convenience Sample ◽  
Tour Guides ◽  
Tourist Satisfaction

Drawing upon the theoretical perspectives from activity competency model and prior tourism literature, this study propose a conceptual framework to explain the impacts of professional competencies on service quality and tourist satisfaction. Empirical data were gathered from a large-scale online survey with experienced GPT tourists to test the proposed hypotheses and research model. The proposed conceptual framework was validated using the partial least squares (PLS) technique. Data gathered from tourists was based on a convenience sample of 345 respondents to test the proposed plausible hypotheses. The conceptual model was validated using the partial least squares (PLS) technique. The empirical results indicate that tour guides’ professional competencies significantly impact on service quality and tourist satisfaction; and tour guides’ service quality positively influences tourist satisfaction.

scholarly journals Distributed Weighted Least-Squares and Gaussian Belief Propagation: An Integrated Approach

2021 ◽  
Author(s):  
Dino Zivojevic ◽  
Muhamed Delalic ◽  
Darijo Raca ◽  
Dejan Vukobratovic ◽  
Mirsad Cosovic
Keyword(s):  
Least Squares ◽  
Large Scale ◽  
Belief Propagation ◽  
Weighted Least Squares ◽  
Measurement Data ◽  
Integrated Approach ◽  
Factor Graph ◽  
State Variables ◽  
Large Scale Systems ◽  
Distributed Approach

The purpose of a state estimation (SE) algorithm is to estimate the values of the state variables considering the available set of measurements. The centralised SE becomes impractical for large-scale systems, particularly if the measurements are spatially distributed across wide geographical areas. Dividing the large-scale systems into clusters (\ie subsystems) and distributing the computation across clusters, solves the constraints of centralised based approach. In such scenarios, using distributed SE methods brings numerous advantages over the centralised ones. In this paper, we propose a novel distributed approach to solve the linear SE model by combining local solutions obtained by applying weighted least-squares (WLS) of the given subsystems with the Gaussian belief propagation (GBP) algorithm. The proposed algorithm is based on the factor graph operating without a central coordinator, where subsystems exchange only ``beliefs", thus preserving privacy of the measurement data and state variables. Further, we propose an approach to speed-up evaluation of the local solution upon arrival of a new information to the subsystem. Finally, the proposed algorithm provides results that reach accuracy of the centralised WLS solution in a few iterations, and outperforms vanilla GBP algorithm with respect to its convergence properties.

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