共查询到20条相似文献,搜索用时 281 毫秒
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随机参数和随机资金流环境下基于二次效用函数的投资组合优化 总被引:1,自引:0,他引:1
研究完全市场下基于二次效用最大化的带有随机资金流的动态投资组合选择问题,其中假设无风险利率、股票收益率和波动率矩阵都是一致有界随机过程.通过应用线性二次控制方法和向后随机微分方程理论得到了最优投资组合的解析表达式. 相似文献
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现实的金融市场上,当有重大信息出现时,会对股价产生冲击,使得股价产生跳跃,同时投资过程会有随机资金流的介入,考虑股价出现跳跃与随机资金流介入的投资组合优化问题,通过构造倒向-前向随机微分方程并结合随机最优控制理论研究了一般效用函数下的投资组合选择问题,获得最优投资组合策略,然后针对二次效用函数,给出显式表示的最优投资组合策略. 相似文献
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本文研究了具有再保险和投资的随机微分博弈.应用线性-二次控制的理论,在指数效用和幂效用下,求得了最优再保险策略、最优投资策略、最优市场策略和值函数的显示解,推广了文[8]的结果.通过本文的研究,当市场出现最坏的情况时,可以指导保险公司选择恰当的再保险和投资策略使自身所获得的财富最大化. 相似文献
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二次损失下随机回归系数和参数的线性Minimax估计 总被引:3,自引:0,他引:3
对带有随机效应的一般线性模型,本文提出了随机回归系数和参数线性组合的Minimax估计问题.在二次损失下,研究了线性估计的极小极大性.关于适当的假设,得到了可估函数的唯一线性Minimax估计. 相似文献
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对带有随机效应的一般线性模型,本文提出了随机回归系数和参数线性组合的Minimax估计问题. 在二次损失下,研究了线性估计的极小极大性.关于适当的假设,得到了可估函数的唯一线性Mjnimax 估计. 相似文献
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《数学的实践与认识》2013,(21)
本文借用线性模型系数的Minimax估计方法,在二次损失函数下运用随机优化理论对Gua.ss-Markov非线性模型的系数进行了研究,建立了非线性模型系数Minimax估计的随机优化模型. 相似文献
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Markowitz的均值-方差模型在投资组合优化中得到了广泛的运用和拓展,其中多数拓展模型仅局限于对随机投资组合或模糊投资组合的研究,而忽略了实际问题同时包含了随机信息和模糊信息两个方面。本文首先定义随机模糊变量的方差用以度量投资组合的风险,提出具有阀值约束的最小方差随机模糊投资组合模型,基于随机模糊理论,将该模型转化为具有线性等式和不等式约束的凸二次规划问题。为了提高上述模型的有效性,本文以投资者期望效用最大化为压缩目标对投资组合权重进行压缩,构建等比例-最小方差混合的随机模糊投资组合模型,并求解该模型的最优解。最后,运用滚动实际数据的方法,比较上述两个模型的夏普比率以验证其有效性。 相似文献
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Global optimization of a class of nonconvex quadratically constrained quadratic programming problems
Yong Xia 《数学学报(英文版)》2011,27(9):1803-1812
In this paper we study a class of nonconvex quadratically constrained quadratic programming problems generalized from relaxations
of quadratic assignment problems. We show that each problem is polynomially solved. Strong duality holds if a redundant constraint
is introduced. As an application, a new lower bound is proposed for the quadratic assignment problem. 相似文献
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In this paper, a new variable reduction technique is presented for general integer quadratic programming problem (GP), under which some variables of (GP) can be fixed at zero without sacrificing optimality. A sufficient condition and a necessary condition for the identification of dominated terms are provided. By comparing the given data of the problem and the upper bound of the variables, if they meet certain conditions, some variables can be fixed at zero. We report a computational study to demonstrate the efficacy of the proposed technique in solving general integer quadratic programming problems. Furthermore, we discuss separable integer quadratic programming problems in a simpler and clearer form. 相似文献
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本文主要讨论了二次整数规划问题的线性化方法.在目标函数为二次函数的情况下,我们讨论了带有二次约束的整数规划问题的线性化方法,并将文献中对二次0-1问题的研究拓展为对带有盒约束的二次整数规划问题的研究.最终将带有盒约束的二次整数规划问题转化为线性混合本文主要讨论了二次整数规划问题的线性化方法.在目标函数为二次函数的情况下,我们讨论了带有二次约束的整数规划问题的线性化方法,并将文献中对二次0-1问题的研究拓展为对带有盒约束的二次整数规划问题的研究.最终将带有盒约束的二次整数规划问题转化为线性混合0-1整数规划问题,然后利用Ilog-cplex或Excel软件中的规划求解工具进行求解,从而解决原二次整数规划. 相似文献
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The uncapacitated plant location problem under uncertainty is formulated in a mean-variance framework with prices in various markets correlated via their response to a common random factor. This formulation results in a mixed-integer quadratic programming problem. However, for a given integer solution, the resulting quadratic programming problem is amenable to a very simple solution procedure. The simplicity of this algorithm means that reasonably large problems should be solvable using existing branch-and-bound techniques. 相似文献
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H. Bernau 《Journal of Optimization Theory and Applications》1990,65(2):209-222
This paper investigates the general quadratic programming problem, i.e., the problem of finding the minimum of a quadratic function subject to linear constraints. In the case where, over the set of feasible points, the objective function is bounded from below, this problem can be solved by the minimization of a linear function, subject to the solution set of a linear complementarity problem, representing the Kuhn-Tucker conditions of the quadratic problem.To detect in the quadratic problem the unboundedness from below of the objective function, necessary and sufficient conditions are derived. It is shown that, when these conditions are applied, the general quadratic programming problem becomes equivalent to the investigation of an appropriately formulated linear complementarity problem.This research was supported by the Hungarian Research Foundation, Grant No. OTKA/1044. 相似文献
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In this paper, we investigate a constrained optimization problem with a quadratic cost functional and two quadratic equality constraints. It is assumed that the cost functional is positive definite and that the constraints are both feasible and regular (but otherwise they are unrestricted quadratic functions). Thus, the existence of a global constrained minimum is assured. We develop a necessary and sufficient condition that completely characterizes the global minimum cost. Such a condition is of essential importance in iterative numerical methods for solving the constrained minimization problem, because it readily distinguishes between local minima and global minima and thus provides a stopping criterion for the computation. The result is similar to one obtained previously by the authors. In the previous result, we gave a characterization of the global minimum of a constrained quadratic minimization problem in which the cost functional was an arbitrary quadratic functional (as opposed to positive-definite here) and the constraints were at least positive-semidefinite quadratic functions (as opposed to essentially unrestricted here). 相似文献
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研究了二次剩余问题,利用整数分类的办法,给出了|Jn|和|Qn|的公式(这里n是奇合数,Jn是Zn*中有Jacobi符号为1的所有元素的集合,Qn是模n的所有二次剩余的集合) .基于这些结果,可以得出当a对模n的Jacobi符号等于1时,正确猜测a为模n的二次剩余的可能性,从而推广了[1]p .74中的结果. 相似文献
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In this paper we consider optimization problems defined by a quadratic objective function and a finite number of quadratic inequality constraints. Given that the objective function is bounded over the feasible set, we present a comprehensive study of the conditions under which the optimal solution set is nonempty, thus extending the so-called Frank-Wolfe theorem. In particular, we first prove a general continuity result for the solution set defined by a system of convex quadratic inequalities. This result implies immediately that the optimal solution set of the aforementioned problem is nonempty when all the quadratic functions involved are convex. In the absence of the convexity of the objective function, we give examples showing that the optimal solution set may be empty either when there are two or more convex quadratic constraints, or when the Hessian of the objective function has two or more negative eigenvalues. In the case when there exists only one convex quadratic inequality constraint (together with other linear constraints), or when the constraint functions are all convex quadratic and the objective function is quasi-convex (thus allowing one negative eigenvalue in its Hessian matrix), we prove that the optimal solution set is nonempty. 相似文献
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Convex integer quadratic programming involves minimization of a convex quadratic objective function with affine constraints and is a well-known NP-hard problem with a wide range of applications. We proposed a new variable reduction technique for convex integer quadratic programs (IQP). Based on the optimal values to the continuous relaxation of IQP and a feasible solution to IQP, the proposed technique can be applied to fix some decision variables of an IQP simultaneously at zero without sacrificing optimality. Using this technique, computational effort needed to solve IQP can be greatly reduced. Since a general convex bounded IQP (BIQP) can be transformed to a convex IQP, the proposed technique is also applicable for the convex BIQP. We report a computational study to demonstrate the efficacy of the proposed technique in solving quadratic knapsack problems. 相似文献