共查询到20条相似文献,搜索用时 140 毫秒
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首先,证明了凸概率密度分布簇的单周期期望均值下单损失鲁棒优化等价模型定理,以及凸概率密度分布簇的单周期期望均值下多损失鲁棒优化等价模型。然后,提出了直营连锁企业的产品在凸概率密度分布簇下的期望均值的单周期生产分配供应问题,建立了直营连锁企业的单周期生产分配供应期望均值鲁棒模型,在获得近似周期概率分布簇情形下给出了单周期生产分配供应鲁棒模型,这种近似鲁棒模型等价于一个线性规划问题。最后,通过已知一个产品的4个周期构成的混合分布簇进行了数值实验,数值结果表明了期望均值准则下的生产分配供应鲁棒模型的生产分配供应策略更加稳健。 相似文献
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实际节目彩排调度中,节目的表演时长受内外因素影响,具有不确定性。为了合理调度所有节目,控制演员的空闲时间,使得演员的总等待成本最小,采用了鲁棒优化方法进行研究。首先,建立了节目彩排调度的确定型模型;进一步,考虑节目表演时长的不确定性,采用有界区间描述节目表演时长并考虑决策者风险偏好,在确定型模型的基础上构建区间型两阶段鲁棒优化模型;接着,将鲁棒优化模型转化为0-1混合线性规划模型;最后,采用Matlab进行数值实验,结果表明决策者越偏好规避风险,演员的总等待成本越大。 相似文献
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研究了一类具有脉冲效应和时变时滞的灰色随机系统的鲁棒稳定性问题。在给出了脉冲随机泛函微分系统随机稳定性的条件的基础上,首先利用Lyapunov-KrasoVskii泛函法和灰矩阵的连续矩阵覆盖的分解技术,得到了具有脉冲效应和时变时滞的灰色随机系统的随机鲁棒稳定性判据,进而基于所得的这个随机鲁棒稳定性判据和Dini导数,给出了该系统指数鲁棒稳定性的判据。实例表明,所得判据是有效的和实用的。 相似文献
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针对一类同时存在非线性项和不确定项的离散时滞系统,研究了系统的鲁棒稳定性问题.通过构造Lyapunov函数并利用Schur补引理以线性矩阵不等式(LMI)形式给出了系统鲁棒稳定的充分条件;利用离散时滞系统鲁棒稳定性的充分条件,采用LMI技术,设计出基于LMI的状态反馈鲁棒控制器;理论证明该方法设计的控制器保证闭环系统鲁棒渐近稳定. 相似文献
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区间动力系统的鲁棒稳定性分析 总被引:1,自引:0,他引:1
本文研究区间系统的鲁棒稳定性问题 ,把连续区间系统的鲁棒Hurwtiz稳定和离散区间系统的鲁棒Schur稳定的问题等价转换于一参数扰动矩阵集的鲁棒非奇异问题 ,然后给出鲁棒Hurwtiz稳定和鲁棒Schur稳定的基于 μ 分析的充分必要条件 . 相似文献
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通过使用灰色矩阵覆盖集的分解方法和矩阵范数的性质,构造李雅普诺夫函数,研究了灰色中立随机线性时滞系统的鲁棒稳定性和几乎指数鲁棒稳定性. 相似文献
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In this paper we present a robust duality theory for generalized convex programming problems in the face of data uncertainty within the framework of robust optimization. We establish robust strong duality for an uncertain nonlinear programming primal problem and its uncertain Lagrangian dual by showing strong duality between the deterministic counterparts: robust counterpart of the primal model and the optimistic counterpart of its dual problem. A robust strong duality theorem is given whenever the Lagrangian function is convex. We provide classes of uncertain non-convex programming problems for which robust strong duality holds under a constraint qualification. In particular, we show that robust strong duality is guaranteed for non-convex quadratic programming problems with a single quadratic constraint with the spectral norm uncertainty under a generalized Slater condition. Numerical examples are given to illustrate the nature of robust duality for uncertain nonlinear programming problems. We further show that robust duality continues to hold under a weakened convexity condition. 相似文献
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In this paper, we present a duality theory for fractional programming problems in the face of data uncertainty via robust optimization. By employing conjugate analysis, we establish robust strong duality for an uncertain fractional programming problem and its uncertain Wolfe dual programming problem by showing strong duality between the deterministic counterparts: robust counterpart of the primal model and the optimistic counterpart of its dual problem. We show that our results encompass as special cases some programming problems considered in the recent literature. Moreover, we also show that robust strong duality always holds for linear fractional programming problems under scenario data uncertainty or constraint-wise interval uncertainty, and that the optimistic counterpart of the dual is tractable computationally. 相似文献
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Y. Zhang 《Journal of Optimization Theory and Applications》2007,132(1):111-124
Most research in robust optimization has been focused so far on inequality-only, convex conic programming with simple linear
models for the uncertain parameters. Many practical optimization problems, however, are nonlinear and nonconvex. Even in linear
programming, the coefficients may still be nonlinear functions of the uncertain parameters. In this paper, we propose robust
formulations that extend the robust-optimization approach to a general nonlinear programming setting with parameter uncertainty
involving both equality and inequality constraints. The proposed robust formulations are valid in a neighborhood of a given
nominal parameter value and are robust to the first-order, thus suitable for applications where reasonable parameter estimations
are available and uncertain variations are moderate.
This work was supported in part by NSF Grant DMS-0405831 相似文献
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Thai Doan Chuong 《Operations Research Letters》2019,47(2):105-109
We first show that the closedness of the characteristic cone of the constraint system of a parametric robust linear optimization problem is a necessary and sufficient condition for each robust linear program with the finite optimal value to admit exact semidefinite linear programming relaxations. We then provide the weakest regularity condition that guarantees exact second-order cone programming relaxations for parametric robust linear programs. 相似文献
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Robust conjugate duality for convex optimization under uncertainty with application to data classification 总被引:1,自引:0,他引:1
In this paper we present a robust conjugate duality theory for convex programming problems in the face of data uncertainty within the framework of robust optimization, extending the powerful conjugate duality technique. We first establish robust strong duality between an uncertain primal parameterized convex programming model problem and its uncertain conjugate dual by proving strong duality between the deterministic robust counterpart of the primal model and the optimistic counterpart of its dual problem under a regularity condition. This regularity condition is not only sufficient for robust duality but also necessary for it whenever robust duality holds for every linear perturbation of the objective function of the primal model problem. More importantly, we show that robust strong duality always holds for partially finite convex programming problems under scenario data uncertainty and that the optimistic counterpart of the dual is a tractable finite dimensional problem. As an application, we also derive a robust conjugate duality theorem for support vector machines which are a class of important convex optimization models for classifying two labelled data sets. The support vector machine has emerged as a powerful modelling tool for machine learning problems of data classification that arise in many areas of application in information and computer sciences. 相似文献
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In this paper, we study quasi approximate solutions for a convex semidefinite programming problem in the face of data uncertainty. Using the robust optimization approach (worst-case approach), approximate optimality conditions and approximate duality theorems for quasi approximate solutions in robust convex semidefinite programming problems are explored under the robust characteristic cone constraint qualification. Moreover, some examples are given to illustrate the obtained results. 相似文献
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In this study, a two-stage fuzzy robust integer programming (TFRIP) method has been developed for planning environmental management systems under uncertainty. This approach integrates techniques of robust programming and two-stage stochastic programming within a mixed integer linear programming framework. It can facilitate dynamic analysis of capacity-expansion planning for waste management facilities within a multi-stage context. In the modeling formulation, uncertainties can be presented in terms of both possibilistic and probabilistic distributions, such that robustness of the optimization process could be enhanced. In its solution process, the fuzzy decision space is delimited into a more robust one by specifying the uncertainties through dimensional enlargement of the original fuzzy constraints. The TFRIP method is applied to a case study of long-term waste-management planning under uncertainty. The generated solutions for continuous and binary variables can provide desired waste-flow-allocation and capacity-expansion plans with a minimized system cost and a maximized system feasibility. 相似文献
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In this paper, we examine duality for fractional programming problems in the face of data uncertainty within the framework
of robust optimization. We establish strong duality between the robust counterpart of an uncertain convex–concave fractional
program and the optimistic counterpart of its conventional Wolfe dual program with uncertain parameters. For linear fractional
programming problems with constraint-wise interval uncertainty, we show that the dual of the robust counterpart is the optimistic
counterpart in the sense that they are equivalent. Our results show that a worst-case solution of an uncertain fractional
program (i.e., a solution of its robust counterpart) can be obtained by solving a single deterministic dual program. In the
case of a linear fractional programming problem with interval uncertainty, such solutions can be found by solving a simple
linear program. 相似文献
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In this paper, we consider approximate solutions (\(\epsilon \)-solutions) for a convex semidefinite programming problem in the face of data uncertainty. Using robust optimization approach (worst-case approach), we prove an approximate optimality theorem and approximate duality theorems for \(\epsilon \)-solutions in robust convex semidefinite programming problem under the robust characteristic cone constraint qualification. Moreover, an example is given to illustrate the obtained results. 相似文献
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不确定信息多目标线性优化的鲁棒方法 总被引:1,自引:0,他引:1
研究不确定信息的多目标线性优化问题,其数据不能精确给出但是属于一个给定的集合.首先,采用鲁棒方法把该问题转化为一个确定的多目标优化问题.然后,给出此问题解存在的充分条件.最后,通过实例验证了用鲁棒方法解决不确定信息的多目标线性优化问题的有效性. 相似文献