求解广义BBM-Burgers方程的一个两层非线性守恒差分格式

对广义BBM-Burgers方程的初边值问题进行了数值研究,提出一个两层非线性Crank-Nicolson差分格式,格式合理地模拟方程本身的一个守恒量,得到差分解的先验估计和存在唯一性,并利用能量方法分析该格式的二阶收敛性与无条件稳定性。     

自适应惯性权重的改进粒子群算法

针对标准PSO算法求解高维非线性问题时存在的大量无效迭代(经过一轮迭代后全局最优位置保持不变),提出了一种自适应惯性权重的改进粒子群算法。基于单次迭代中单粒子运动状态的分析,提出并证明了论点:上一轮迭代适应度值变差的粒子,当前迭代中其惯性分量将引导粒子往适应度值变差的方向运动,导致粒子群体无效迭代次数增加。设计了标准PSO算法改进方案,将上一轮迭代中适应度值变差的全体粒子的惯性权重置为零,消除当前迭代中不利惯性分量对算法收敛的不良影响。采用6个标准测试函数,将该算法与标准PSO算法、固定惯性权重PSO算法和具有领袖的PSO算法进行性能对比分析。试验表明,该改进算法无效迭代次数更少,在收敛率、收敛速度和收敛稳定性上均具有明显的优势。     

模糊集收敛的等价性

基于前人在对特殊模糊集上关于确界收敛,L-收敛,Endograph-收敛,Sendograph-收敛之间的等价性的分析,证明了一般模糊数序列上确界度量收敛等价于同时层次收敛和强截集收敛,并对关于截集连续的模糊数集合上的收敛的等价性给出了一种简洁的几何证明方法。     

谈拉格朗日中值定理在高等数学课程教学中的应用

拉格朗日中值定理是微分学的基础定理之一,是连接函数及其导数之间关系的桥梁,有着广泛的应用。文章用七个例题,从三大方面总结了拉格朗日中值定理的灵活应用。这对于正确的理解和掌握拉格朗日中值定理,以及以后进一步学习数学具有重要的作用和深远的意义。     

MQPSO: 一种具有多群体与多阶段的QPSO算法*

提出了一种改进的QPSO(Quantum-behaved Particle Swarm Optimization)算法,即一种具有多群体与多阶段的具有量子行为的粒子群优化算法.在该算法中,粒子被分为多个群体,利用多个阶段进行全局搜索,这样可以有效地避免粒子群早熟,提高了算法的全局收敛性能.对几个重要测试函数的测试结果证明,MQPSO算法的收敛性能优于标准粒子群算法(Standard Particle Swarm Optimization, SPSO)以及QPSO算法.     

二辊矫直原理及精度分析

通过对二辊矫直过程的分析，深入的研究了矫直的机理。建立了二辊矫直的数学模型，并给出了辊形可矫直的条件和矫直精度的定量计算。     

Dual–primal proximal point algorithms for extended convex programming

This paper presents a class of dual–primal proximal point algorithms (PPAs) for extended convex programming with linear constraints. By choosing appropriate proximal regularization matrices, the application of the general PPA to the equivalent variational inequality of the extended convex programming with linear constraints can result in easy proximal subproblems. In theory, the sequence generated by the general PPA may fail to converge since the proximal regularization matrix is asymmetric sometimes. So we construct descent directions derived from the solution obtained by the general PPA. Different step lengths and descent directions are chosen with the negligible additional computational load. The global convergence of the new algorithms is proved easily based on the fact that the sequences generated are Fejér monotone. Furthermore, we provide a simple proof for the O(1/t) convergence rate of these algorithms.     

Compact difference method for solving the fractional reaction–subdiffusion equation with Neumann boundary value condition

In this paper, we derive a high-order compact finite difference scheme for solving the reaction–subdiffusion equation with Neumann boundary value condition. The L1 method is used to approximate the temporal Caputo derivative, and the compact difference operator is applied for spatial discretization. We prove that the compact finite difference method is unconditionally stable and convergent with order O(τ^2?α+h⁴) in L₂ norm, where τ, α, and h are the temporal step size, the order of time fractional derivative and the spatial step size, respectively. Finally, some numerical experiments are carried out to show the effectiveness of the proposed difference scheme.