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2017 2nd International Conference on Advances in Management Engineering and Information Technology(AMEIT 2017)

作者： Fan YANG Peng ZHANG School of Economics Wuhan University of Technology

Transaction cost was an important factor in the financial *** to the situation of the underdeveloped market,we proposed a mean-variance portfolio selection model with nonsmooth concave transaction *** is nonsmooth programming *** paper proposed the subsection method and pivoting algorithm to solve it,and proved these algorithms ***,according to annual yields of six kinds of securities in eight years,we solved the mean-variance portfolio optimal strategy with nonsmooth concave transaction *** example was given to illustrate the efficiency of the algorithms,which offered a new way to solve the problems of nonsmooth programming.

关键词： Mean-variance portfolio selection Nonsmooth concave transaction costs pivoting algorithm Subsection method

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pivoting algorithm for Optimization Model of Portfolio Rebalancing with Transaction Costs

Pivoting Algorithm for Optimization Model of Portfolio Rebal...

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International Conference on Computational Intelligence and Natural Computing

作者： Liu, Yanwu Zhang, Zhongzhen Wuhan Univ Technol Sch Management Wuhan 430070 Peoples R China

ISBN: (纸本)9780769536453

Portfolio optimization is an important branch to which optimization methods are applied. Transaction costs have significant influence on portfolio rebalancing in practice. The introduction of transaction costs into optimal rebalancing model significantly enlarges the size of optimization problem and need more efficient algorithms. We deduce the relationships between the Karush-Kuhn-Tucker (KKT) conditions of the original model and those of its sub-problem. pivoting algorithm can make full use of these relationships to obtain the optimal solution for the original model efficiently from that for its subproblem.

关键词： transaction costs pivoting algorithm portfolio optimization optimal rebalancing

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pivoting algorithm for Optimization Model of Portfolio Rebalancing with Transaction Costs

Pivoting Algorithm for Optimization Model of Portfolio Rebal...

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2009 International Conference on Computational Intelligence and Natural Computing(CINC 2009)

作者： Yanwu LIU School of Management, Wuhan University of Technology, Wuhan, P. R. China,430070,Zhongzhen ZHANG School of Management, Wuhan University of Technology, Wuhan, P. R. China,430070

关键词： transaction costs pivoting algorithm portfolio optimization optimal rebalancing

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Higher-order Nonlinear Discrete Approximate Iteration On the Continuing Dynamic Programming

Higher-order Nonlinear Discrete Approximate Iteration On the...

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2nd International Conference on Industry, Information System and Material Engineering (IISME 2012)

作者： Zhang, Peng Zhou, Jingyi Wuhan Univ Sci & Technol Sch Management Wuhan 430081 Peoples R China

ISBN: (纸本)9783037853566

In order to solve the "dimension curse" and higher-order nonlinear, the paper proposes the discrete approximate iteration, and uses it to solve the continuum dynamic programming. Firstly, according to the network method, discretizes the state variables and transforms the model into multiperiod weighted digraph. Secondly, uses the max-plus algebra to solve the minimal path that is the admissible solution. At last, based on the admissible solution, we continues iterating until the two admissible solution is near. The paper also proves the convergence of the method.

关键词： Dynamic programming Discrete approximate iteration The max-plus algebra pivoting algorithm

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Optimization of the Expected Utility Portfolio Selection Model with Box Constraints

Optimization of the Expected Utility Portfolio Selection Mod...

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International Conference on Advanced Research on Computer Science and Information Engineering

作者： Zhang, Peng Yu, Lang Wuhan Univ Sci & Technol Sch Management Wuhan 430081 Peoples R China

ISBN: (纸本)9783642214103

A new expected utility (EU) portfolio selection model is proposed under the assumption that the trading volume has the box constraints. In the model, the expected utility function is quadratic. The model is solved by a pivoting algorithm which needn't be added any slack, surplus and artificial variable, and is easy to operate and works efficiently. A numerical example of a portfolio selection problem is given to compare the new model and the EU portfolio selection model not considering upper bounds. The comparison shows that when the risk preference coefficient is greater than a critical value, the risk and expected return don't increase as the coefficient increases;The relationship between the risk preference coefficient and the expected return (or risk) is nonlinear while that is linear in the case when short sales are allowed;the efficient portfolio selection considering the box constraints is subset of that not considering the constraints.

关键词： Portfolio selection Utility pivoting algorithm The box constraints

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The Optimization on the Expected Utility Portfolio Selection Model Without Short Sales

The Optimization on the Expected Utility Portfolio Selection...

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International Conference on Automation, Communication, Architectonics and Materials

作者： Zhang, Peng Wang, Huili Wuhan Univ Sci & Technol Sch Management Wuhan 430081 Peoples R China

ISBN: (纸本)9783037851036

A new expected utility (EU) portfolio selection model without short sales is proposed. In the model, the expected utility function is quadratic. The model is solved by the pivoting algorithm. The paper showed in the EU portfolio selection model without the short sales, the relationship between the risk preference coefficient and the expected return is not linear but more complex. The risk preference coefficient could just reflect the investors' preference in some intervals. We wrote program to calculate the optimal portfolios with the different coefficient. Investors could choose the optimal investment strategy according to both their own risk preference and the expected return of the portfolio.

关键词： Portfolio selection Utility pivoting algorithm Without short sales

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An Efficient and Concise algorithm for Convex Quadratic Programming and Its Application to Markowitz’s Portfolio Selection Model

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Technology and Investment 2011年 2卷 229-239页

作者： Zhongzhen Zhang Huayu Zhang

This paper presents a pivoting-based method for solving convex quadratic programming and then shows how to use it together with a parameter technique to solve mean-variance portfolio selection problems.

关键词： Convex Quadratic Programming Mean-Variance Portfolio Selection Model pivoting algorithm

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pivoting in Linear Complementarity: Two Polynomial-Time Cases

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DISCRETE & COMPUTATIONAL GEOMETRY 2009年第2期42卷 187-205页

作者： Foniok, Jan Fukuda, Komei Gaertner, Bernd Luethi, Hans-Jakob ETH Inst Theoret Comp Sci CH-8092 Zurich Switzerland ETH Inst Operat Res CH-8092 Zurich Switzerland

We study the behavior of simple principal pivoting methods for the P-matrix linear complementarity problem (P-LCP). We solve an open problem of Morris by showing that Murty's least-index pivot rule (under any fixed index order) leads to a quadratic number of iterations on Morris's highly cyclic P-LCP examples. We then show that on K-matrix LCP instances, all pivot rules require only a linear number of iterations. As the main tool, we employ unique-sink orientations of cubes, a useful combinatorial abstraction of the P-LCP.

关键词： Linear complementarity pivoting algorithm P-matrix K-matrix Computational complexity Unique-sink orientation

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A Fast algorithm for Linearly Constrained Quadratic Programming Problems with Lower and Upper Bounds

A Fast Algorithm for Linearly Constrained Quadratic Programm...

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International Conference on MultiMedia and Information Technology

作者： Liu, Yanwu Mang, Zhongzhen Wuhan Univ Technol Sch Management Wuhan 430070 Peoples R China

ISBN: (纸本)9780769535562

There are many applications related to linearly constrained quadratic programs subjected to upper and lower bounds. Lower bounds and upper bounds are treated as different constraints by common quadratic programming algorithms. These traditional treatments significantly increase the computation of quadratic programming problems. We employ pivoting algorithm to solve quadratic programming models. The algorithm can convert the quadratic programming with upper and lower bounds into quadratic programming with upper or lower bounds equivalently by making full use of the Karush-Kuhn-Tucker (KKT) conditions of the problem and decrease the computation. The algorithm can further decrease calculation to obtain solution of quadratic programming problems by solving a smaller linear inequality system which is the linear part of KKT conditions for the quadratic programming problems and is equivalent to the KKT conditions while maintaining complementarity conditions of the KKT conditions to hold.

关键词： pivoting algorithm Karush-Kuhn-Tucker conditions quadratic programming lower and upper bounds

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A Parametric algorithm for Long-Short Portfolio Optimization

A Parametric Algorithm for Long-Short Portfolio Optimization

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First International Workshop on Knowledge Discovery and Data Mining

作者： Yanwu Liu~1 Zhongzhen Zhang~1 Feng Xiong~1 LIU Fang~2 1 School of Management,Wuhan University of Technology,Wuhan,P.R.China 430070 2 School of Economics,Huazhong University of Science and Technology,Wuhan P.R.China

A parametric algorithm is proposed to calculate efficient frontier of long-short *** key to the algorithm is to introduce parametric technique into the pivoting *** numerical results show that the algorithm has high c... 详细信息

关键词： long-short portfolio pivoting algorithm parametric technique

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