发布于:2018-12-14 星期五 09:25:27 点击数:498





2019年2月至今 湖南大学工商管理学院副教授

2017年10月至今 湖南大学工商管理学院信息管理与电子商务系系主任












网络营销学 网络经济学 网络金融学 计量经济学(双语和全英文) 多元统计分析和SPSS应用(双语和全英文) 运筹学  高频金融计量学 凸优化理论与应用



1:Rui Xie,L Huang, Boshi Tian(田博士)*, J Fang, Differences in Changes in Carbon Dioxide Emissions among China's Transportation Subsectors: A Structural Decomposition Analysis,Emerging Markets Finance and Trade, 2019,DOI:10.1080/1540496X.2018.1526076. (SSCI,ESI Economics and Business(Management))IF:0.828

Abstract:In recent years, one of the largest and most rapidly growing emitters of carbon dioxide (CO2) in China is the transportation industry. This article applies the structural decomposition analysis (SDA) method to identify the driving forces of CO2 emissions in four subsectors of the transportation industry in China and distinguishes the main final demand patterns that increase its emissions. Our results show that, first, during the study period, the expansion in demand was the largest contributor to the increase in CO2 emissions in the transportation industry, whereas the energy intensity effect played a dominant role in reducing emissions. In addition, CO2 emissions among four subsectors differed significantly not only in terms of changes in quantity but also the impacts of influencing factors. Moreover, most of the recent growth in CO2 emissions in China’s transportation industry has been driven by investment and exports. Because of the wide heterogeneity of changes in CO2 emissions among different transportation sectors, the particularities of each subsector should be taken into account in formulating pollution abatement policies in the transportation industry.

2:Tian Bo-Shi(田博士)*,Li Dong-Hui and Yang Xiao-Qi, An unconstrained differentiable penalty method for implicit complementarity problems, Optimization Methods and Software, 2016,31(4):775-790,(SCI)影响因子:1.624, 运筹学与管理科学(JCR)

Abstract: In this paper, we introduce an unconstrained differentiable penalty method for solving implicit complementarity problems, which has an exponential convergence rate under the assumption of a uniform $\xi$-$P$-function. Instead of solving the unconstrained penalized equations directly, we consider a corresponding unconstrained optimization problem and apply the trust-region Gauss-Newton method to solve it. We prove that the local solution of the unconstrained optimization problem identifies that of the complementarity problems under monotone assumptions. We carry out numerical experiments on the test problems from MCPLIB, and show that the proposed method is efficient and robust. 

3:Tian Bo-Shi(田博士)* and Yang Xiao-Qi, Smoothing power penalty method for nonlinear complementarity problems,Pacific Journal of Optimization,2016,12(2):461-484,(SCI)影响因子:1.079,运筹学与管理科学(JCR) 

Abstract: In this paper, we introduce a new penalty method for solving nonlinear complementarity problems, which unifies the existing $\ell_1$-penalty method and the natural residual equation-based method. We establish the exponential convergence rate between a solution of the penalized equations and that of the complementarity problem under a uniform $\xi$-$P$-function and study a perturbed $b$-regularity condition. Two kinds of numerical algorithms with global and fast local convergence are designed by virtue of the proposed penalty method. Preliminary numerical experiments conducted on test problems from MCPLIB show that the proposed method is efficient and robust. 

4:Tian, Bo-Shi(田博士)*, Yang, Xiao-Qi and Meng Kai-Wen, An interior-point$\ell_{\frac12}$-penalty method for the inequality constrained nonlinear optimization. Journal of Industrial and Management Optimization,2016,12(3):949-973, (SCI)影响因子:0.843,运筹学与管理科学(JCR) 

Abstract: In this paper, we study inequality constrained nonlinear programming problems by virtue of an $\ell_{\frac12}$-penalty function and a quadratic relaxation. Combining with an interior-point method, we propose an interior-point $\ell_{\frac12}$-penalty method. We introduce different kinds of constraint qualifications to establish the first-order necessary conditions for the quadratically relaxed problem. We apply the modified Newton method to a sequence of logarithmic barrier problems, and design some reliable algorithms. Moreover, we establish the global convergence results of the proposed method. We carry out numerical experiments on 266 inequality constrained optimization problems. Our numerical results show that the proposed method is competitive with some existing interior-point $\ell_1$-penalty methods in term of iteration numbers and better when comparing the values of the penalty parameter.

 5:Tian, Bo-Shi(田博士)*, Hu, Yao-Hua and Yang Xiao-Qi, A box-constrained differentiable penalty method for nonlinear complementarity problems. Journal of Global Optimization,2015,62(4):729-747,(SCI) 影响因子:1.355,运筹学与管理科学(JCR)

Abstract: In this paper, we propose a box-constrained differentiable penalty method for nonlinear complementarity problems, which not only inherits the same convergence rate as the existing $\ell_\frac1p$-penalty method but also overcomes its disadvantage of non-Lipschitzianness. We introduce the concept of a uniform $\xi$-$P$-function with $\xi\in(1,2]$, and apply it to prove that the solution of box-constrained penalized equations converges to that of the original problem at an exponential order. Instead of solving the box-constrained penalized equations directly, we solve a corresponding differentiable least squares problem by using a trust-region Gauss-Newton method. Furthermore, we establish the connection between the local solution of the least squares problem and that of the original problem under mild conditions. We carry out the numerical experiments on the test problems from MCPLIB, and show that the proposed method is efficient and robust. 

6:Li, Dong-Hui and Tian, Bo-Shi(田博士)*, n-step quadratic convergence of the MPRP method with a restart strategy, Journal of Computational and Applied Mathematics, 2011,235(17): 4978-4990. (通讯作者)(SCI)影响因子:1.112. 

Abstract: It is well-known that the PRP conjugate gradient method with exact line search is globally and linearly convergent. If a restart strategy is used, the convergence rate of the method can be an n-step superlinear/quadratic convergence. Recently, Zhang et al. [L. Zhang, W. Zhou, D.H. Li, A descent modified Polak–Ribière–Polyak conjugate gradient method and its global convergence, IMA J. Numer. Anal. 26 (2006) 629–640] developed a modified PRP (MPRP) method that is globally convergent if an inexact line search is used. In this paper, we investigate the convergence rate of the MPRP method with inexact line search. We first show that the MPRP method with Armijo line search or Wolfe line search is linearly
convergent. We then show that the MPRP method with a restart strategy still retains nstep superlinear/quadratic convergence if the initial steplength is appropriately chosen.
We also do some numerical experiments. The results show that the restart MPRP method does converge quadratically. Moreover, it is more efficient than the non-restart method. 

7:Dai, Zhi-Feng and Tian, Bo-Shi(田博士), Global convergence of some modified PRP nonlinear conjugate gradient methods, Optimization Letters,2011, 5 (4):615--630. (SCI)影响因子:0.952运筹学与管理科学(JCR) 

Abstract: Recently, similar to Hager and Zhang (SIAM J Optim 16:170–192, 2005), Yu (Nonlinear self-scaling conjugate gradient methods for large-scale optimization problems. Thesis of Doctors Degree, Sun Yat-Sen University, 2007) and Yuan
(Optim Lett 3:11–21, 2009) proposed modified PRP conjugate gradient methods which generate sufficient descent directions without any line searches. In order to obtain the global convergence of their algorithms, they need the assumption that the stepsize is bounded away from zero. In this paper, we take a little modification to these methods such that the modified methods retain sufficient descent property. Without requirement of the positive lower bound of the stepsize, we prove that the proposed methods are globally convergent. Some numerical results are also reported. 


    1:光滑化法方法求解互补问题以及在金融中的应用,湖南大学青年教师成长计划, 2015.01—2019.12,主持

2:均衡约束规划的新型松弛算法及其应用研究(11601142),国家自然科学基金委,2017.01-2019.12, 主持

3:高维高频金融数据的实证研究(2016M602412),博士后基金面上项目,2016.12-2019.12, 主持