鲁棒稀疏重构问题的凝聚同伦算法

鲁棒稀疏重构问题是信号处理领域的重要问题,该问题的数学本质是一个NP难的数学优化问题.同伦算法是一类典型的路径跟踪算法,该算法是解非线性问题的一类成熟算法,具有全局收敛性,且易于并行实现.本文考虑同伦算法在鲁棒稀疏重构问题中的数值求解.基于l_∞范数及罚函数策略,我们首先将原始的基于l_0范数的最优化模型,转化为含参数的无约束极大极小值问题,进而构造凝聚函数光滑化模型中的极大值函数,并构造凝聚同伦算法数值求解.数值仿真实验验证了新方法的有效性,为大规模鲁棒重构问题的并行化数值求解奠定基础.     

含非线性项的不确定离散时滞系统鲁棒稳定性分析

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.     

集装箱码头泊位计划的鲁棒优化模型

针对集装箱码头作业中的不确定性因素,构建泊位计划的鲁棒优化模型与算法,目的是降低不确定性因素对集装箱码头作业系统的影响。首先,提出泊位计划鲁棒性度量指标,利用算例对各指标的效果进行分析。在此基础上,设计泊位计划鲁棒优化的两阶段优化算法。算法的第一阶段不考虑泊位计划的鲁棒性,以船舶总延误时间最小为目标;算法的第二阶段以所选择的鲁棒性指标最大为目标,以第一阶段获得的船舶总延误时间为约束条件,获得鲁棒调度方案。最后,研究作业资源(装卸桥数量)的变化对泊位计划鲁棒性的影响。算例分析表明,权重松弛量是有效的度量泊位计划鲁棒性的指标,两阶段算法可以有效解决泊位计划鲁棒优化问题。     

关于高阶不确定非线性系统的鲁棒适应干扰衰减问题

研究一类高阶不确定非线性系统的鲁棒适应H∞干扰衰减问题。本质改进了加幂积分器技巧及backstepping算法,并设计出一个光滑鲁棒适应动态反馈控制律,使得闭环系统既具有内稳定性,又使系统达到干扰衰减．所得结果改进了相关文献中的结论．     

不确定离散时间马尔可夫跳变模糊系统鲁棒H∞控制

讨论不确定离散时间马尔可夫跳变模糊系统(MJFS)的鲁棒H∞控制。首先,本文给出了能够保证系统鲁棒稳定且具有H∞鲁棒度的一个充分条件。然后采用并行分布补偿算法,将系统鲁棒H∞控制控制器的设计转化成为了一组线性矩阵不等式的求解问题,方便使用Matlab求解。最后的仿真结果表明,本文所提出的方法是有效的。     

一种基于能量检测的鲁棒频谱感知方法

能量检测是一种简单又有效的频谱感知技术,但是易于受到噪声不确定的困扰而影响其性能.文章针对能量检测中的噪声不确定性问题,提出了利用Huber混合模型的鲁棒能量感知方法,具体地推导了该方法的判决规则,分析了算法性能,并给出了仿真结果.仿真结果表明,在受到噪声不确定影响的情况下,鲁棒能量感知方法的性能优于传统能量感知方法的性能.     

不确定多目标优化鲁棒真有效解的最优性与对偶

本文研究一类不确定性多目标优化问题鲁棒真有效解的最优性条件和对偶理论.首先,借助鲁棒真有效解的标量化定理,在鲁棒型闭凸锥约束品性下,建立了不确定多目标优化问题真有效解的最优性条件;其次,针对原不确定多目优化的Wolfe型对偶问题,得到关于鲁棒真有效解的强、弱对偶定理.     

多目标应急物流中心选址的鲁棒优化模型

针对重大突发事件的应急物资救援,研究了应急物流中心的选址及应急物资的调运问题。利用离散的情景集合描述受灾点应急物资需求的不确定性以及应急物资运输成本和运输时间的不确定性,同时考虑应急救援成本和应急救援时间两个目标,建立了多目标应急物流中心选址的确定型模型和鲁棒优化模型。为将多目标问题转化为单目标问题,利用成本单目标和时间单目标的最优结果将多目标转化为相对值再加权处理,该方法既可消除多个目标之间的单位及数量级差异,还可以根据问题的数据变化进行动态调整。以提供应急物资救援服务的设施作为编码,设计了一种通用的混合蛙跳算法。为检验模型和算法的有效性,设计了一个多情景的算例,结果表明两个模型和算法具备良好的可行性和有效性,且鲁棒优化模型能较好地保持对各种不确定性的抗干扰能力;最后,讨论分析了成本偏好权重和鲁棒约束系数的影响,结果表明可根据成本偏好权重的取值范围来区分各种应急救援阶段,体现不同救援阶段的救援要求及特征,并给出了成本偏好权重和鲁棒约束系数的取值建议。     

不确定信息下分式半无限优化问题的近似最优性刻画

该文研究了一类带不确定参数的多目标分式半无限优化问题。首先借助鲁棒优化方法,引入该不确定多目标分式优化问题的鲁棒对应优化模型,并借助Dinkelbach方法,将该鲁棒对应优化模型转化为一般的多目标优化问题。随后借助一种标量化方法,建立了该优化问题的标量化问题,并刻画了它们的解之间的关系。最后借助一类鲁棒型次微分约束规格,建立了该不确定多目标分式优化问题拟近似有效解的鲁棒最优性条件。     

分布时滞系统的动态输出反馈鲁棒稳定

主要研究了一类具分布时变时滞不确定系统的输出反馈鲁棒稳定问题.基于动态输出反馈和Lyapunov-Krasovskii泛函,给出了闭环系统与时滞相关的鲁棒稳定的条件.所得条件为线性矩阵不等式形式,便于运用内点算法进行求解.     

Policy iteration for robust nonstationary Markov decision processes

Policy iteration is a well-studied algorithm for solving stationary Markov decision processes (MDPs). It has also been extended to robust stationary MDPs. For robust nonstationary MDPs, however, an “as is” execution of this algorithm is not possible because it would call for an infinite amount of computation in each iteration. We therefore present a policy iteration algorithm for robust nonstationary MDPs, which performs finitely implementable approximate variants of policy evaluation and policy improvement in each iteration. We prove that the sequence of cost-to-go functions produced by this algorithm monotonically converges pointwise to the optimal cost-to-go function; the policies generated converge subsequentially to an optimal policy.     

Robust optimization in countably infinite linear programs

A robust optimization framework for countably infinite linear programs (CILPs) is developed. It is shown that a particular robust counterpart of a nominal CILP can be reformulated as another CILP. A bound on the probability of constraint violation is derived. A convergent algorithm for solving this robust CILP is proposed.     

A unitary Hessenberg QR-based algorithm via semiseparable matrices

In this paper, we present a novel method for solving the unitary Hessenberg eigenvalue problem. In the first phase, an algorithm is designed to transform the unitary matrix into a diagonal-plus-semiseparable form. Then we rely on our earlier adaptation of the QR algorithm to solve the dpss eigenvalue problem in a fast and robust way. Exploiting the structure of the problem enables us to yield a quadratic time using a linear memory space. Nonetheless the algorithm remains robust and converges as fast as the customary QR algorithm. Numerical experiments confirm the effectiveness and the robustness of our approach.     

Robust analysis and global optimization

In this paper, the properties of robust sets and robust functions are studied. Also, we study minimization of a robust function over a robust set and extend the optimality conditions of [3] and the algorithm of [4,5] to our case. The algorithm is shown to be effective.This research was supported by the National Science Foundation of China.     

Proportion-based robust optimization and team orienteering problem with interval data

In this paper, a proportion-based robust optimization approach is developed to deal with uncertain combinatorial optimization problems. This approach assumes that a certain proportion of uncertain coefficients in each solution are allowed to change and optimizes a deterministic model so as to achieve a trade-off between optimality and feasibility when the coefficients change. We apply this approach on team orienteering problem with interval data (TOPID), a variant of vehicle routing problem, which has not yet been studied before. A branch and price algorithm is proposed to solve the robust counterpart by using two novel dominance relations. Finally, numerical study is performed. The results show the usefulness of the proposed robust optimization approach and the effectiveness of our algorithm.     

A branch and bound algorithm for the robust shortest path problem with interval data

Many real problems can be modelled as robust shortest path problems on interval digraphs, where intervals represent uncertainty about real costs and a robust path is not too far from the shortest path for each possible configuration of the arc costs.A branch and bound algorithm for this problem is presented.     

Contact interaction inside knot connections of cables

Knots as a method for the fastening of ropes and other linear materials are widely appearing in practical applications ­ in sailing, in surgery, in textile and rope structures etc. The mechanics of knots, however, appears to be not sufficiently covered neither by analytical methods, nor by computational methods. From a computational mechanics point of view a knot is a perfect example requiring both a robust smooth cable element and a robust curve-to-curve contact algorithm. The current contribution is aimed on the development this combination ­ the isogeometric approach for curvilinear beams and the robust curve-to-curve contact algorithm for curvilinear cables. The developed model is applied studying the mechanics of various knots. (© 2010 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Accurate and robust body position trajectory tracking of six-legged walking robots with nonsingular terminal sliding mode control method

Many tasks, such as walking in narrow environments, detecting land mines, coordinating with manipulators, and avoiding obstacles, demand multi-legged walking robots to accurately and robustly track predefined body trajectories. Tracking body position trajectory must be accurate and robust in these situations, but research on this topic is rarely carried out. In this study, we propose a nonsingular terminal sliding mode (NTSM) control algorithm to implement accurate and robust body position trajectory tracking of six-legged walking robots. The NTSM control algorithm is constructed on the basis of the body position trajectory tracking model with a new NTSM reaching law. The performance of the NTSM control method is evaluated through several verifications. Results demonstrate that the proposed algorithm is effective for accurate and robust body position trajectory tracking. The findings of this study can provide insights into improving multi-legged walking robots’ walking and operation abilities in special environments and expanding the application fields of these robots.     

A branch and bound algorithm for the robust spanning tree problem with interval data

The robust spanning tree problem is a variation, motivated by telecommunications applications, of the classic minimum spanning tree problem. In the robust spanning tree problem edge costs lie in an interval instead of having a fixed value.Interval numbers model uncertainty about the exact cost values. A robust spanning tree is a spanning tree whose total cost minimizes the maximum deviation from the optimal spanning tree over all realizations of the edge costs. This robustness concept is formalized in mathematical terms and is used to drive optimization.In this paper a branch and bound algorithm for the robust spanning tree problem is proposed. The method embeds the extension of some results previously presented in the literature and some new elements, such as a new lower bound and some new reduction rules, all based on the exploitation of some peculiarities of the branching strategy adopted.Computational results obtained by the algorithm are presented. The technique we propose is up to 210 faster than methods recently appeared in the literature.     

一种单位化的增量梯度算法

研究目标函数是若干光滑函数和的可分离优化问题,提出了一种单位化增量梯度算法。该算法每次子迭代只需要计算一个（或几个）分量函数的单位负梯度方向作为迭代方向。在一定条件下,证明了采用发散步长的单位化增量梯度算法的收敛性。作为应用,新算法和Bertsekas D P,Tsitsikils J N提出的（没有单位化）增量梯度算法分别用来求解稳健估计问题和源定位问题。数值例子表明,新算法优于（没有单位化）增量梯度算法。