两同型部件温贮备可修系统半离散化的研究

利用半离散的方法对两同型部件温贮备可修系统中的函数μ(x)进行离散,得到两个离散的方程,再利用算子半群的理论证明离散后方程的解收敛于原方程的解.     

线性森林定常发展系统半离散化的研究

利用半离散的方法将线性森林发展方程中的μ(r)进行离散,得到两个偏微分方程,进一步利用算子半群的理论证明离散后的解是收敛于原方程的解.     

两相同部件冷贮备可修系统半离散化的研究

利用半离散的方法将两相同部件冷贮备可修系统中的μ(x)进行离散,得到两个偏微分方程,进一步利用算子半群的理论证明离散后的解是收敛于原方程的解.     

求解二维波动方程正演反演问题的半离散方法

本文用半离散方法将二维波动方程离散为一维耦合波动方程组．给出了离散的收敛性及波动方程组的适定性．利用这种方法可以求解波动方程系数及演问题．     

广义神经传播方程的一种修正混合有限元方法的误差分析

利用修正的H~1-Galerkin混合有限元方法研究了广义神经传播方程,论证了其半离散解的存在唯一性,得到了半离散解的最优阶误差估计,该方法的优点是不需验证LBB相容性条件.     

Sobolev方程各向异性矩形非协调有限元分析

研究了Sobolev方程的各向异性矩形非协调有限元方法。在半离散和全离散格式下，得到了与传统协调有限元方法相同的最优误差估计和超逼近性质。进一步地利用插值后处理技术得到了整体超收敛结果。最后的数值结果表明了理论分析的正确性。     

半线性Sobolev方程的H~1-Galerkin混合有限元方法

利用H~1-Galerkin混合有限元方法研究了一维半线性Sobolev方程,得到了半离散解的最优阶误差估计,优点是不需验证LBB相容性条件.     

阻尼Sine-Gordon方程的H1-Galerkin混合元方法数值解

利用H1-Galerkin混合有限元方法讨论阻尼Sine-Gordon方程,得到一维情况下半离散和全离散格式的最优阶误差估计,并且推广应用到二维和三维情况,而且不用验证LBB相容性条件.     

抛物方程初边值问题连续有限元的超收敛性

研究了一类一维抛物方程初边值问题的连续有限元方法.在空间上进行任意m次有限元半离散,在时间方向上进行二次连续有限元后,获得了一个稳定的全离散计算格式.利用单元分析法校正技术的新思想进行理论分析,连续有限元解在剖分网格节点上具有超收敛性.     

四阶强阻尼非线性波动方程的Hermite型矩形混合有限元分析

讨论了四阶强阻尼非线性波动方程的Hermite型混合有限元方法,并证明了半离散格式下解的存在唯一性.基于该元积分恒等式结果,利用插值与Ritz投影之间的误差估计,可得到半离散格式下O(h~3)阶的超逼近性质,再借助于插值后处理技术导出整体超收敛.进而,通过构造一个新的金离散格式,得到了O(h~3+τ~2)的超逼近和超收敛结果.     

蚁群遗传混合算法

将蚁群遗传混合算法分别求解离散空间的和连续空间优化问题.求解旅行商问题的混合算法是以遗传算法为整个算法的框架,利用了蚁群算法中的信息素特性的进行交叉操作;根据旅行商问题的特点,给出了4种变异策略;针对遗传算法存在的过早收敛问题,加入2-0pt方法对问题求解进行了局部优化.与模拟退火算法、标准遗传算法和标准蚁群算法进行比较,4种混合算法效果都比较好,策略D的混合算法效果最好.求解连续空间优化问题是以蚁群算法为整个算法的框架,加入遗传算法的交叉操作和变异操作,用测试函数验证了混合蚁群算法的正确性.     

遗传算法的物理解释

提出了一种理想化的模拟仿生搜索算法——扰动算法 ,以此方法为基础 ,分析了遗传算法的搜索过程和效率问题 ,阐明了遗传算法作为一种次优算法的有效性 .相对于遗传算法的生物解释 ,本文给出了相应的物理解释 .同时 ,本文为遗传算法、进化策略和模拟退火算法找到了一种统一的物理解释 ,揭示了这些重要的仿生类算法实质上的相似性 .     

一种新的双模式盲均衡算法研究

针对恒模算法(CMA)收敛速度较慢、收敛后均方误差较大的缺点,提出一种新的双模式盲均衡算法.在算法初期,利用能快速收敛的归一化恒模算法(NCMA)进行冷启动,在算法收敛后切换到判决引导(DD-LMS)算法,减少误码率.计算机仿真表明,提出的新算法有较快的收敛速度和较低的误码率.     

CurveLP-A MATLAB implementation of an infeasible interior-point algorithm for linear programming

Mehrotra’s algorithm has been the most successful infeasible interior-point algorithm for linear programming since 1990. Most popular interior-point software packages for linear programming are based on Mehrotra’s algorithm. This paper describes a proposal and implementation of an alternative algorithm, an arc-search infeasible interior-point algorithm. We will demonstrate, by testing Netlib problems and comparing the test results obtained by the arc-search infeasible interior-point algorithm and Mehrotra’s algorithm, that the proposed arc-search infeasible interior-point algorithm is a more reliable and efficient algorithm than Mehrotra’s algorithm.     

A New Diagonal Quasi-Newton Updating Method With Scaled Forward Finite Differences Directional Derivative for Unconstrained Optimization

A new diagonal quasi-Newton updating algorithm for unconstrained optimization is presented. The elements of the diagonal matrix approximating the Hessian are determined as scaled forward finite differences directional derivatives of the components of the gradient. Under mild classical assumptions, the convergence of the algorithm is proved to be linear. Numerical experiments with 80 unconstrained optimization test problems, of different structures and complexities, as well as five applications from MINPACK-2 collection, prove that the suggested algorithm is more efficient and more robust than the quasi-Newton diagonal algorithm retaining only the diagonal elements of the BFGS update, than the weak quasi-Newton diagonal algorithm, than the quasi-Cauchy diagonal algorithm, than the diagonal approximation of the Hessian by the least-change secant updating strategy and minimizing the trace of the matrix, than the Cauchy with Oren and Luenberger scaling algorithm in its complementary form (i.e. the Barzilai-Borwein algorithm), than the steepest descent algorithm, and than the classical BFGS algorithm. However, our algorithm is inferior to the limited memory BFGS algorithm (L-BFGS).     

A rank-one algorithm for unconstrained function minimization

A rank-one algorithm is presented for unconstrained function minimization. The algorithm is a modified version of Davidon's variance algorithm and incorporates a limited line search. It is shown that the algorithm is a descent algorithm; for quadratic forms, it exhibits finite convergence, in certain cases. Numerical studies indicate that it is considerably superior to both the Davidon-Fletcher-Powell algorithm and the conjugate-gradient algorithm.     

含有等式约束非线性规划的全局优化算法

针对含有多个等式约束的非线性规划问题,提出一个全局优化算法.该方法基于可行集策略把改进的模拟退火方法与确定的局部算法方法相结合.对算法的收敛性进行了证明,数值结果表明算法的有效性及正确性.     

经过改进的求解TSP问题的蚁群算法

介绍了一种求解TSP问题的算法—改进的蚁群算法,算法通过模拟蚁群搜索食物的过程,可用于求解TSP问题,算法的主要特点是:正反馈、分布式计算、与某种启发式算法相结合.通过对传统蚁群算法的改进可以得到较好的结果.计算机仿真结果表明了该算法的有效性.     

A fraction free Matrix Berlekamp/Massey algorithm

We describe a fraction free version of the Matrix Berlekamp/Massey algorithm. The algorithm computes a minimal matrix generator of linearly generated square matrix sequences in an integral domain. The algorithm performs all operations in the integral domain, so all divisions performed are exact. For scalar sequences, the matrix algorithm specializes to a different algorithm than the algorithm currently in the literature. This new scalar algorithm has smaller intermediate values than the known fraction free Berlekamp/Massey algorithm.     

Nonlinear Smoothing and the EM Algorithm for Positive Integral Equations of the First Kind

We study a modification of the EMS algorithm in which each step of the EMS algorithm is preceded by a nonlinear smoothing step of the form , where S is the smoothing operator of the EMS algorithm. In the context of positive integral equations (à la positron emission tomography) the resulting algorithm is related to a convex minimization problem which always admits a unique smooth solution, in contrast to the unmodified maximum likelihood setup. The new algorithm has slightly stronger monotonicity properties than the original EM algorithm. This suggests that the modified EMS algorithm is actually an EM algorithm for the modified problem. The existence of a smooth solution to the modified maximum likelihood problem and the monotonicity together imply the strong convergence of the new algorithm. We also present some simulation results for the integral equation of stereology, which suggests that the new algorithm behaves roughly like the EMS algorithm. Accepted 1 April 1997