Some preconditioners for elliptic PDE-constrained optimization problems

For the structured systems of linear equations arising from the Galerkin finite element discretizations of elliptic PDE-constrained optimization problems, some preconditioners are proposed to accelerate the convergence rate of Krylov subspace methods such as GMRES for both cases of the Tikhonov parameter $\beta$ not very small (equal or greater than 1e?6) and sufficiently small (less than 1e?6), respectively. We derive the explicit expressions for the eigenvalues and eigenvectors of the corresponding preconditioned matrices. Numerical results show that the corresponding preconditioned GMRES methods perform and match well with the theoretical results.     

Parallelizing implicit algorithms for time-dependent problems by parabolic domain decomposition

Applications of the multidomain Local Fourier Basis method [1], for the solution of PDEs on parallel computers are described. The present approach utilizes, in an explicit way, the rapid (exponential) decay of the fundamental solutions of elliptic operators resulting from semi-implicit discretizations of parabolic time-dependent problems. As a result, the global matching relations for the elemental solutions are decoupled into local interactions between pairs of solutions in neighboring domains. Such interactions require only local communications between processors with short communication links. Thus the present algorithm overcomes the global coupling, inherent both in the use of the spectral Fourier method and implicit time discretization scheme.This research is supported partly by a grant from the French-Israeli Binational Foundation for 1991–1992.     

A finite difference non-matching domain decomposition algorithm for the parabolic equation

A finite difference domain decomposition algorithm on a non-overlapping non-matching grid for the parabolic equation is discussed. The basic procedure is to define the explicit scheme at the interface points with a larger mesh spacing H, then the implicit schemes with different mesh spacings are applied on the non-matching subdomains, respectively. The stability bound is released both for the one-dimensional and two-dimensional parabolic problem. Finally, numerical experiments are also presented.     

Optimized Schwarz methods without overlap for highly heterogeneous media

In this paper an original variant of the Schwarz domain decomposition method is introduced for heterogeneous media. This method uses new optimized interface conditions specially designed to take into account the heterogeneity between the sub-domains on each sides of the interfaces. Numerical experiments illustrate the dependency of the proposed method with respect to several parameters, and confirm the robustness and efficiency of this method based on such optimized interface conditions. Several mesh partitions taking into account multiple cross points are considered in these experiments.     

Restricted overlapping balancing domain decomposition methods and restricted coarse problems for the Helmholtz problem

Overlapping balancing domain decomposition methods and their combination with restricted additive Schwarz methods are proposed for the Helmholtz equation. These new methods also extend previous work on non-overlapping balancing domain decomposition methods toward simplifying their coarse problems and local solvers. They also extend restricted Schwarz methods, originally designed to overlapping domain decomposition and Dirichlet local solvers, to the case of non-overlapping domain decomposition and/or Neumann and Sommerfeld local solvers. Finally, we introduce coarse spaces based on partitions of unity and planes waves, and show how oblique projection coarse problems can be designed from restricted additive Schwarz methods. Numerical tests are presented.     

多频段时域分解的行为识别特征优选方法

为了降低外界环境对移动用户行为识别的影响,保留行为敏感特征、提高行为识别的准确率,提出了一种多频段时域分解的人体行为识别特征优选方法.该方法对行为样本数据进行多频段分解,计算样本数据在不同频段信号的特征,利用遗传算法以决策树作为分类器进行特征优选,在多组特征中搜索出近似最优的特征组合.实验结果表明,该方法优选出的特征组合能有效提高行为识别的准确率.     

Approximation algorithms for combinatorial multicriteria optimization problems

The computational complexity of combinatorial multiple objective programming problems is investigated. NP-completeness and # P -completeness results are presented. Using two definitions of approximability, general results are presented, which outline limits for approximation algorithms. The performance of the well-known tree and Christofides' heuristics for the traveling salesman problem is investigated in the multicriteria case with respect to the two definitions of approximability.     

Total-FETI domain decomposition method for solution of elasto-plastic problems

In this paper we present an algorithm for the parallel solution of the rate-independent elasto-plastic problems with kinematic hardening. We assume the von Mises plastic criterion and the associated plastic flow rule. The time discretization is based on the implicit Euler method. The corresponding one-time-step problem is formulated in the incremental form with respect to the unknown displacement and discretized spatially by the finite element method. We use an ‘external’ algorithm based on a linearization of the elasto-plastic stress–strain relation by the corresponding tangential operator and we parallelize the arising linearized problem by the Total-FETI method. The numerical experiments were carried out using our novel C/C++ library FLLOP (FETI Light Layer On top of PETSc) at HECToR supercomputer located at EPCC, UK.     

A hybridized angle-encouragement-based decomposition approach for many-objective optimization problems

Due to the large objective space when handling many-objective optimization problems (MaOPs), it is a challenging work for multi-objective evolutionary algorithms (MOEAs) to balance convergence and diversity during the search process. Although a number of decomposition-based MOEAs have been designed for the above purpose, some difficulties are still encountered for tackling some difficult MaOPs. As inspired by the existing decomposition approaches, a new Hybridized Angle-Encouragement-based (HAE) decomposition approach is proposed in this paper, which is embedded into a general framework of decomposition-based MOEAs, called MOEA/D-HAE. Two classes of decomposition approaches, i.e., the angle-based decomposition and the proposed encouragement-based boundary intersection decomposition, are sequentially used in HAE. The first one selects appropriate solutions for association in the feasible region of each subproblem, which is expected to well maintain the diversity of associated solutions. The second one acts as a supplement for the angle-based one under the case that no solution is located in the feasible region of subproblem, which aims to ensure the convergence and explore the boundaries. By this way, HAE can effectively combine their advantages, which helps to appropriately balance convergence and diversity in evolutionary search. To study the effectiveness of HAE, two series of well-known test MaOPs (WFG and DTLZ) are used. The experimental results validate the advantages of HAE when compared to other classic decomposition approaches and also confirm the superiority of MOEA/D-HAE over seven recently proposed many-objective evolutionary algorithms.     

Explicit/implicit and Crank–Nicolson domain decomposition methods for parabolic partial differential equations

Two parallel non-overlapping domain decomposition algorithms for solving parabolic partial differential equations are proposed. The algorithms combine Crank–Nicolson scheme with implicit Galerkin finite element methods in sub-domains and explicit flux approximation along inner boundaries at each time step. Thus, parallelism can be easily achieved. $L^2$-norm error estimates for these explicit/implicit procedures are presented, in which time step constraints are proved to be less severe than that of fully explicit schemes. Numerical experiments are also performed to verify the theoretical analysis.     

马尔可夫跳变系统的鲁棒故障检测与时域优化

研究马尔可夫跳变系统(MJS) 鲁棒故障检测滤波器(FDF) 的设计与优化问题. 基于观测器构建残差发生器, 将相应的FDF 设计问题转化为H_∞ 滤波问题, 以LMI 的形式得到并证明了FDF 存在的充分条件及求解方法. 为进一步改善故障检测系统的性能, 采用一种时域优化方法对其进行优化, 并以矩阵Moore-Penrose 逆的形式给出了该优化问题的最优解. 数值仿真表明该方法具有较好的检测效果.     

整数规划问题智能求解算法综述*

为了对大规模整数规划问题的求解方法提供参考,对基于智能算法求解整数规划问题的研究进行了分析和评述。鉴于现有算法的缺陷与不足,讨论了应用智能算法求解整数规划问题未来可能的研究方向。     

Evolutionary algorithms for optimization problems with uncertainties and hybrid indices

Many optimization problems in real-world applications contain both explicit (quantitative) and implicit (qualitative) indices that usually contain uncertain information. How to effectively incorporate uncertain information in evolutionary algorithms is one of the most important topics in information science. In this paper, we study optimization problems with both interval parameters in explicit indices and interval uncertainties in implicit indices. To incorporate uncertainty in evolutionary algorithms, we construct a mathematical uncertain model of the optimization problem considering the uncertainties of interval objectives; and then we transform the model into a precise one by employing the method of interval analysis; finally, we develop an effective and novel evolutionary optimization algorithm to solve the converted problem by combining traditional genetic algorithms and interactive genetic algorithms. The proposed algorithm consists of clustering of a large population according to the distribution of the individuals and estimation of the implicit indices of an individual based on the similarity among individuals. In our experiments, we apply the proposed algorithm to an interior layout problem, a typical optimization problem with both interval parameters in the explicit index and interval uncertainty in the implicit index. Our experimental results confirm the feasibility and efficiency of the proposed algorithm.     

Multi-operator based evolutionary algorithms for solving constrained optimization problems

Over the last two decades, many sophisticated evolutionary algorithms have been introduced for solving constrained optimization problems. Due to the variability of characteristics in different COPs, no single algorithm performs consistently over a range of problems. In this paper, for a better coverage of the problem characteristics, we introduce an algorithm framework that uses multiple search operators in each generation. The appropriate mix of the search operators, for any given problem, is determined adaptively. The framework is tested by implementing two different algorithms. The performance of the algorithms is judged by solving 60 test instances taken from two constrained optimization benchmark sets from specialized literature. The first algorithm, which is a multi-operator based genetic algorithm (GA), shows a significant improvement over different versions of GA (each with a single one of these operators). The second algorithm, using differential evolution (DE), also confirms the benefit of the multi-operator algorithm by providing better and consistent solutions. The overall results demonstrated that both GA and DE based algorithms show competitive, if not better, performance as compared to the state of the art algorithms.     

基于模型分解的多机带时间窗口任务规划算法

针对多机带时间窗口任务规划问题,提出了基于模型分解的规划求解算法。通过引入基于逻辑的Benders分解方法,将经典Benders分解算法应用扩展至带离散时间窗口的混合线性整数规划模型,实现模型分解。采用工艺级商业软件MOSEK与GECODE分别求解主、子问题,同时给出Benders剪枝函数生成方法,以迭代方式收敛解空间获得可行解。实现算法并设计测试案例,实验结果验证了算法的有效性。     

Gradient-free algorithms for distributed online convex optimization

In this paper, we consider the distributed bandit convex optimization of time-varying objective functions over a network. By introducing perturbations into the objective functions, we design a deterministic difference and a randomized difference to replace the gradient information of the objective functions and propose two classes of gradient-free distributed algorithms. We prove that both the two classes of algorithms achieve regrets of $O(T^{3/4})$ for convex objective functions and $O(T^{2/3})$ for strongly convex objective functions, with respect to the time index $T$ and consensus of the estimates established as well. Simulation examples are given justifying the theoretical results.     

多目标优化问题的蚁群算法研究

将离散空间问题求解的蚁群算法引入连续空间,针对多目标优化问题的特点,提出一种用于求解带有约束条件的多目标函数优化问题的蚁群算法.该方法定义了连续空间中信息量的留存方式和蚂蚁的行走策略,并将信息素交流和基于全局最优经验指导两种寻优方式相结合,用以加速算法收敛和维持群体的多样性.通过3组基准函数来测试算法性能,并与NSGAII算法进行了仿真比较.实验表明该方法搜索效率高,向真实Pareto前沿逼近的效果好,获得的解的散布范围广,是一种求解多目标优化问题的有效方法.     

基于网格的一种新的动态演化算法

近年来,越来越多的演化计算研究者对动态优化问题产生了很大的兴趣,并产生了很多解决动态优化问题的方法。提出一种新的动态演化算法,与传统的演化算法有所不同,它是建立在划分网格基础上的,故而称它为网格优化算法。通过测试典型的动态优化问题,并与经典的SOS算法进行比较,证明了算法的有效性。     

Efficient algorithms for computing two nearest-neighbor problems on a rap

This paper makes an improvement of computing two nearest-neighbor problems of images on a reconfigurable array of processors (RAP) by increasing the bus width between processors. Based on a base-n system, a constant time algorithm is first presented for computing the maximum/minimum of N log N-bit unsigned integers on a RAP using N processors each with N^1/c-bit bus width, where c is a constant and c ≥ 1. Then, two basic operations such as image component labeling and border following are also derived from it. Finally, these algorithms are used to design two constant time algorithms for the nearest neighbor black pixel and the nearest neighbor component problems on an N^1/2 × N^1/2 image using N^1/2 × N^1/2 processors each with N^1/c-bit bus width, where c is a constant and c ≥ 1. Another contribution of this paper is that the execution time of the proposed algorithms is tunable by the bus width.