We focus on second order duality for a class of multiobjective programming problem subject to cone constraints. Four types of second order duality models are formulated. Weak and strong duality theorems are establishe...We focus on second order duality for a class of multiobjective programming problem subject to cone constraints. Four types of second order duality models are formulated. Weak and strong duality theorems are established in terms of the generalized convexity, respectively. Converse duality theorems, essential parts of duality theory, are presented under appropriate assumptions. Moreover, some deficiencies in the work of Ahmad and Agarwal(2010) are discussed.展开更多
This article proposes a method for fitting models subject to a convex and log-convex constraint on the probability vector of a product multinomial (binomial) distribution. We present an iterative algorithm for findi...This article proposes a method for fitting models subject to a convex and log-convex constraint on the probability vector of a product multinomial (binomial) distribution. We present an iterative algorithm for finding the restricted maximum likelihood estimates (MLEs) of the probability vector and show that the algorithm converges to the true solution. Some examples are discussed to illustrate the method.展开更多
基金supported by National Natural Science Foundation of China (Grant Nos. 11431004, 11271391 and 11201511)the Project of Chongqing Science and Technology Committee (Grant No. cstc2014pt-sy00001)Theoretical Foundation and Application Procedure of Environmental Data Envelopment Analysis Model (Grant No. B-Q22L)
摘要We focus on second order duality for a class of multiobjective programming problem subject to cone constraints. Four types of second order duality models are formulated. Weak and strong duality theorems are established in terms of the generalized convexity, respectively. Converse duality theorems, essential parts of duality theory, are presented under appropriate assumptions. Moreover, some deficiencies in the work of Ahmad and Agarwal(2010) are discussed.
基金Supported by the National Natural Science Foundation of China(No.11071008)Scientific Foundations of Beijing Jiaotong University(No.2012JBM105)
摘要This article proposes a method for fitting models subject to a convex and log-convex constraint on the probability vector of a product multinomial (binomial) distribution. We present an iterative algorithm for finding the restricted maximum likelihood estimates (MLEs) of the probability vector and show that the algorithm converges to the true solution. Some examples are discussed to illustrate the method.