Parallel Partial Stabilizing Algorithms for Large Linear Control Systems

In this paper we present parallel algorithms for stabilizing large linear control systems on multicomputers. Our algorithms first separate the stable part of the linear control system and then compute a stabilizing feedback for the unstable part. Both stages are solved by means of the matrix sign function which presents a high degree of parallelism and scalability.The experimental results on an IBM SP2 platform show the performance of our approach.     

基于动力学方程求解复矩阵特征值问题的并行实现

该文提出了一种利用动力学方程求解复特征值及其特征向量的并行实现方法。方法的原理为：首先将特征值问题通过优化技术转化为一个非线性动力学系统的求解问题，然后利用电路模拟中的波形松弛法并行计算这组动力学方程的解。该方法能够有效地确定复矩阵的全部特征值和特征向量。这是首次将波形松弛法引入大型矩阵的计算中，其并行算法已在IBM RS／6000 SuperPOWER2系统中有效地实现。     

Guaranteed roll-off in a class of high-gain feedback design problems

A number of synthesis problems associated with (almost) disturbance decoupling by state or measurement feedback is considered. Starting from a mathematical definition of the notion of high-frequency roll-off, known results on the solvability of these problems are generalized to the situation in which we require their solvability together with a certain guaranteed roll-off between disturbance and control. The conditions are formulated in terms of the solvability and approximate solvability of certain matrix equations in rational functions.     

基于加权矩阵的多维广义特征值并行分解算法

针对串行广义特征值分解算法实时性差的缺点, 提出基于加权矩阵的多维广义特征值分解算法. 与串行算法不同, 所提算法能够在一次迭代过程中并行地估计出多维广义特征向量. 平稳点分析表明: 当且仅当算法中状态矩阵等于所需的广义特征向量时, 算法达到收敛状态. 通过对比相邻时刻的状态矩阵模值证明了所提算法的自稳定特性. 所提算法参数选取简单, 实际实施较为容易. 数值仿真和实例应用进一步验证了算法的并行性、自稳定性和实用性.     

Component-Based Derivation of a Parallel Stiff ODE Solver Implemented in a Cluster of Computers

A component-based methodological approach to derive distributed implementations of parallel ODE solvers is proposed. The proposal is based on the incorporation of explicit constructs for performance polymorphism into a methodology to derive group parallel programs of numerical methods from SPMD modules. These constructs enable the structuring of the derivation process into clearly defined steps, each one associated with a different type of optimization. The approach makes possible to obtain a flexible tuning of a parallel ODE solver for several execution contexts and applications. Following this methodological approach, a relevant parallel numerical scheme for solving stiff ODES has been optimized and implemented on a PC cluster. This numerical scheme is obtained from a Radau IIA Implicit Runge–Kutta method and exhibits a high degree of potential parallelism. Several numerical experiments have been performed by using several test problems with different structural characteristics. These experiments show satisfactory speedup results.