求解非线性方程组的一种新的全局收敛的Levenberg-Marquardt算法

本文提出了求解非线性方程组的一种新的全局收敛的Levenberg-Marquardt算法,即μk=ακ(θ||F_k|| (1-θ)||J_k~TF_k||),θ∈[0,1],其中ακ利用信赖域技巧来修正.在不必假设雅可比矩阵非奇异的局部误差界条件下,证明了该算法是全局收敛和局部二次收敛的.数值试验表明该算法能有效地求解奇异非线性方程组问题.     

关于ε不敏感损失函数推广误差的界

本文研究了学习理论中推广误差的界的问题.利用ε不敏感损失函数的性质,分别获得r逼近误差和估计(样本)误差的界,并在特定的假设空间上得到了学习算法推广误差的界.     

S-Sparse Ostrowski-Brauer矩阵线性互补问题的误差界及其应用（英文）

本文研究S-Sparse Ostrowski-Brauer (S-SOB)矩阵线性互补问题误差界的估计问题.利用矩阵不等式放缩技术及S-SOB矩阵逆矩阵无穷大范数,获得S-SOB矩阵线性互补问题的误差界,该界仅依赖于S-SOB矩阵的元素.在此基础上,给出S-SOB-B矩阵线性互补问题的误差界,并从理论上证明所给误差界在一定条件下优于García-Esnaola等(2009)和LIU等(2021)所给的结果.最后,通过数值算例进一步阐明了结果的有效性.     

未知死区输入高阶关联大系统的全局稳定分散控制

针对一类具有未知非线性死区输入的高阶关联大系统,设计了一种新的分散控制方法。该方法基于模糊滑模控制原理,确保所设计的分散控制器能使各个子系统仅根据自己的信息就能确定相应的控制量,真正实现分散控制。Lyapunov稳定理论分析证明了闭环系统的全局稳定性,跟踪误差收敛到零,并且给出了全局一致终结有界的相关界。仿真结果表明了所设计方法的有效性。     

伪凸集值映射的误差界

考虑了伪凸集值映射的误差界.证明了对于伪凸集值映射,局部误差界成立意味着整体误差界成立.通过相依导数,给出了伪凸集值映射存在误差界的一些等价叙述.     

关于B-Nekrasov矩阵线性互补问题最优误差界的注记

研究了B-Nekrasov矩阵线性互补问题的含有参数误差界的最优值问题,利用函数的单调性,在_0_(i_1)···_n···_(i_(n-1))≥0且0_n1的情况下,得到了该误差界的最优值.     

一类非最小相位非线性系统的鲁棒输出调节

研究了一类具有非最小相位和非线性外部系统的非线性系统的全局鲁棒输出调节问题.首先,利用浸入系统设计了一个非线性内模.其次,把原系统的全局鲁棒输出调节问题转化为增广系统的全局鲁棒镇定问题.然后,利用改变能量函数和动态增益技巧设计了一个状态反馈控制器,使得闭环系统的解有界并且跟踪误差渐近趋于零.最后,利用仿真结果验证了所设计的控制器的有效性.     

不确定量测联合信号重构的稳定性

研究了分布式压缩感知(Distributed Compressed Sensing,DCS)理论对联合稀疏信号进行联合重构的稳定性问题.文中讨论的联合稀疏信号模型中含有两个近似稀疏信号,且信号的量测过程中带有噪声.证明利用分布式压缩感知思想对近似稀疏的联合稀疏信号的联合稀疏重构具有稳定性,刻画了重构信号的误差,并与单个信号的稀疏重构导致的误差进行了比较,证明了在一定条件下,利用分布式压缩感知思想对信号进行联合重构的误差界小于单个信号重构的误差界.     

全局最优信息融合估计算法(Ⅰ)

研究了全局最优融合计算法,研究结果表明：全局最优估计的误差要小于局部估计的误差,即全局估计优于每一个局部估计。     

关于平行排序问题公平度的一个注记

利用经典的SPTgreedy算法分析了不同类机排序问题的全局公平度,证明了该算法所生成排序的公平度不超过m,并且该界为紧的.     

Error bounds for spatial discretization and waveform relaxation applied to parabolic functional differential equations

The process of semi-discretization and waveform relaxation are applied to general nonlinear parabolic functional differential equations. Two new theorems are presented, which extend and improve some of the classical results. The first of these theorems gives an upper bound for the norm of the error of finite difference semi-discretization. This upper bound is sharper than the classical error bound. The second of these theorems gives an upper bound for the norm of the error, which is caused by both semi-discretization and waveform relaxation. The focus in the paper is on estimating this error directly without using the upper bound for the error, which is caused by the process of semi-discretization and the upper bound for the error, which is caused by the waveform relaxation method. Such estimating gives sharper error bound than the bound, which is obtained by estimating both errors separately.     

Global error bound for convex inclusion problems

The existence of global error bound for convex inclusion problems is discussed in this paper, including pointwise global error bound and uniform global error bound. The existence of uniform global error bound has been carefully studied in Burke and Tseng (SIAM J. Optim. 6(2), 265–282, 1996) which unifies and extends many existing results. Our results on the uniform global error bound (see Theorem 3.2) generalize Theorem 9 in Burke and Tseng (1996) by weakening the constraint qualification and by widening the varying range of the parameter. As an application, the existence of global error bound for convex multifunctions is also discussed.     

One Approach to the Comparison of Error Bounds at a Point and on a Set in the Solution of Ill-Posed Problems

The approximate solution of ill-posed problems by the regularization method always involves the issue of estimating the error. It is a common practice to use uniform bounds on the whole class of well-posedness in terms of the modulus of continuity of the inverse operator on this class. Local error bounds, which are also called error bounds at a point, have been studied much less. Since the solution of a real-life ill-posed problem is unique, an error bound obtained on the whole class of well-posedness roughens to a great extent the true error bound. In the present paper, we study the difference between error bounds on the class of well-posedness and error bounds at a point for a special class of ill-posed problems. Assuming that the exact solution is a piecewise smooth function, we prove that an error bound at a point is infinitely smaller than the exact bound on the class of well-posedness.     

Computation of Error Bounds for P-matrix Linear Complementarity Problems

We give new error bounds for the linear complementarity problem where the involved matrix is a P-matrix. Computation of rigorous error bounds can be turned into a P-matrix linear interval system. Moreover, for the involved matrix being an H-matrix with positive diagonals, an error bound can be found by solving a linear system of equations, which is sharper than the Mathias-Pang error bound. Preliminary numerical results show that the proposed error bound is efficient for verifying accuracy of approximate solutions. This work is partly supported by a Grant-in-Aid from Japan Society for the Promotion of Science.     

The aliasing error in recovery of nonbandlimited signals by prefiltering and sampling

In this paper, we study the aliasing error when a nonbandlimited function is reconstructed by means of prefiltering and sampling. We give the optimal upper bound for the mean aliasing error and show that the error bound can be minimized by a suitable filter.     

Error bounds for convex differentiable inequality systems in Banach spaces

The paper is devoted to studying the Hoffman global error bound for convex quadratic/affine inequality/equality systems in the context of Banach spaces. We prove that the global error bound holds if the Hoffman local error bound is satisfied for each subsystem at some point of the solution set of the system under consideration. This result is applied to establishing the equivalence between the Hoffman error bound and the Abadie qualification condition, as well as a general version of Wang &; Pang's result [30], on error bound of Hölderian type. The results in the present paper generalize and unify recent works by Luo &; Luo in [17], Li in [16] and Wang &; Pang in [30].     

Error Control in Polytope Computations

This paper presents solutions for numerical computation on convex hulls; computational algorithms that ensure logical consistency and accuracy are proposed. A complete numerical error analysis is presented. It is shown that a global error bound for vertex-facet adjacency does not exist under logically consistent procedures. To cope with practical requirements, vertex preconditioned polytope computations are introduced using point and hyperplane adjustments. A global bound on vertex-facet adjacency error is affected by the global bound on vertices; formulas are given for a conservative choice of global error bounds.     

Convergence of discrete and penalized least squares spherical splines

We study the convergence of discrete and penalized least squares spherical splines in spaces with stable local bases. We derive a bound for error in the approximation of a sufficiently smooth function by the discrete and penalized least squares splines. The error bound for the discrete least squares splines is explicitly dependent on the mesh size of the underlying triangulation. The error bound for the penalized least squares splines additionally depends on the penalty parameter.     

Multiprocessor scheduling: combining LPT and MULTIFIT

We consider the problem of scheduling a set of n independent jobs on m identical machines with the objective of minimizing the total finishing time. We combine the two most popular algorithms, LTP and MULTIFIT, to form a new one. MULTIFIT is well known to have better error bound than LPT with the price paid in running time. Although MULTIFIT provides a better error bound, in many cases LPT still can yield better results. This motivates the development of this new combined algorithm, which uses the result of LPT as the incumbent and then applies MULTIFIT with fewer iterations. The performance of this combined algorithm is better than that of LPT because it uses LPT as an incumbent. The error bound of this combined algorithm is never worse than that of MULTIFIT. For example, the error bound of implementing this combined algorithm to the two-processor problem is , compared to the error bound of in MULTIFIT. Empirical results of the comparison for schedules obtained by the combined algorithm, MULTIFIT and LPT are also provided.     

Global Error Bound for the Generalized Linear Complementarity Problem over a Polyhedral Cone

In this paper, the global error bound estimation for the generalized linear complementarity problem over a polyhedral cone (GLCP) is considered. To obtain a global error bound for the GLCP, we first develop some equivalent reformulations of the problem under milder conditions and then characterize the solution set of the GLCP. Based on this, an easily computable global error bound for the GLCP is established. The results obtained in this paper can be taken as an extension of the existing global error bound for the classical linear complementarity problems. This work was supported by the Research Grant Council of Hong Kong, a Chair Professor Fund of The Hong Kong Polytechnic University, the Natural Science Foundation of China (Grant No. 10771120) and the Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry.