In this paper,we explore sparsity and homogeneity of regression coefficients incorporating prior constraint information.The sparsity means that a small fraction of regression coefficients is nonzero,and the homogeneit...In this paper,we explore sparsity and homogeneity of regression coefficients incorporating prior constraint information.The sparsity means that a small fraction of regression coefficients is nonzero,and the homogeneity means that regression coefficients are grouped and have exactly the same value in each group.A general pairwise fusion approach is proposed to deal with the sparsity and homogeneity detection when combining prior convex constraints.We develop a modified alternating direction method of multipliers algorithm to obtain the estimators and demonstrate its convergence.The efficiency of both sparsity and homogeneity detection can be improved by combining the prior information.Our proposed method is further illustrated by simulation studies and analysis of an ozone dataset.展开更多
Ordering based search methods have advantages over graph based search methods for structure learning of Bayesian networks in terms on the efficiency. With the aim of further increasing the accuracy of ordering based s...Ordering based search methods have advantages over graph based search methods for structure learning of Bayesian networks in terms on the efficiency. With the aim of further increasing the accuracy of ordering based search methods, we first propose to increase the search space, which can facilitate escaping from the local optima. We present our search operators with majorizations, which are easy to implement. Experiments show that the proposed algorithm can obtain significantly more accurate results. With regard to the problem of the decrease on efficiency due to the increase of the search space, we then propose to add path priors as constraints into the swap process. We analyze the coefficient which may influence the performance of the proposed algorithm, the experiments show that the constraints can enhance the efficiency greatly, while has little effect on the accuracy. The final experiments show that, compared to other competitive methods, the proposed algorithm can find better solutions while holding high efficiency at the same time on both synthetic and real data sets.展开更多
The three parameters of P-wave velocity, S-wave velocity, and density have remarkable differences in conventional prestack inversion accuracy, so study of the consistency inversion of the ＂three parameters＂ is very ...The three parameters of P-wave velocity, S-wave velocity, and density have remarkable differences in conventional prestack inversion accuracy, so study of the consistency inversion of the ＂three parameters＂ is very important. In this paper, we present a new inversion algorithm and approach based on the in-depth analysis of the causes in their accuracy differences. With this new method, the inversion accuracy of the three parameters is improved synchronously by reasonable approximations and mutual constraint among the parameters. Theoretical model calculations and actual data applications with this method indicate that the three elastic parameters all have high inversion accuracy and maintain consistency, which also coincides with the theoretical model and actual data. This method has good application prospects.展开更多
文摘In this paper,we explore sparsity and homogeneity of regression coefficients incorporating prior constraint information.The sparsity means that a small fraction of regression coefficients is nonzero,and the homogeneity means that regression coefficients are grouped and have exactly the same value in each group.A general pairwise fusion approach is proposed to deal with the sparsity and homogeneity detection when combining prior convex constraints.We develop a modified alternating direction method of multipliers algorithm to obtain the estimators and demonstrate its convergence.The efficiency of both sparsity and homogeneity detection can be improved by combining the prior information.Our proposed method is further illustrated by simulation studies and analysis of an ozone dataset.
基金supported by the National Natural Science Fundation of China(61573285)the Doctoral Fundation of China(2013ZC53037)
文摘Ordering based search methods have advantages over graph based search methods for structure learning of Bayesian networks in terms on the efficiency. With the aim of further increasing the accuracy of ordering based search methods, we first propose to increase the search space, which can facilitate escaping from the local optima. We present our search operators with majorizations, which are easy to implement. Experiments show that the proposed algorithm can obtain significantly more accurate results. With regard to the problem of the decrease on efficiency due to the increase of the search space, we then propose to add path priors as constraints into the swap process. We analyze the coefficient which may influence the performance of the proposed algorithm, the experiments show that the constraints can enhance the efficiency greatly, while has little effect on the accuracy. The final experiments show that, compared to other competitive methods, the proposed algorithm can find better solutions while holding high efficiency at the same time on both synthetic and real data sets.
基金sponsored by the National Major Program (No. 2011ZX05006-006)the 973 Program of China (No. 2011CB201104)Technical Research of Elastic Flooding Boundary and Well Network Optimization at the Development Late Stage of Low Permeable Oil Field (No. 2011ZX05009)
文摘The three parameters of P-wave velocity, S-wave velocity, and density have remarkable differences in conventional prestack inversion accuracy, so study of the consistency inversion of the ＂three parameters＂ is very important. In this paper, we present a new inversion algorithm and approach based on the in-depth analysis of the causes in their accuracy differences. With this new method, the inversion accuracy of the three parameters is improved synchronously by reasonable approximations and mutual constraint among the parameters. Theoretical model calculations and actual data applications with this method indicate that the three elastic parameters all have high inversion accuracy and maintain consistency, which also coincides with the theoretical model and actual data. This method has good application prospects.
基金Supported by the National Natural Science Foundation of China under Grant Nos.60372050 60372045 (国家自然科学基金)the National Basic Research Program of China under Grant No.2001CB309403 (国家重点基础研究发展计划(973))
基金Supported by the National Natural Science Foundation of China under Grant No.60875031(国家自然科学基金)the National Basic Research Program of China under Grant No.2007CB311002(国家重点基础研究发展计划(973))+2 种基金the Program for New Century Excellent Talents in University of china under Grant No.NECT-06-0078(新世纪优秀人才支持计划)the Research Fund for the Doctoral Program of Higher Education of the Ministry of Education of China under Grant No.20050004008(教育部高等学校博士学科点专项科研基金)the Fok Ying-Tbng Education Foundation for Young Teachers in the Higher Education Instirutions of China under Grant No.101068(霍英东教育基金会高等院校青年教师基金)
