A note about the complexity of minimizing Nesterov's smooth Chebyshev–Rosenbrock function

This short note considers and resolves the apparent contradiction between known worst-case complexity results for first- and second-order methods for solving unconstrained smooth nonconvex optimization problems and a recent note by Jarre [On Nesterov's smooth Chebyshev–Rosenbrock function, Optim. Methods Softw. (2011)] implying a very large lower bound on the number of iterations required to reach the solution's neighbourhood for a specific problem with variable dimension.     

Genetic algorithm‐based neural‐network modeling approach applied to AlGaN/GaN devices

An accurate equivalent circuit large‐signal model (ECLSM) for AlGaN‐GaN high electron mobility transistor (HEMT) is presented. The model is derived from a distributed small‐signal model that efficiently describes the physics of the device. A genetic neural‐network‐based model for the gate and drain currents and charges is presented along with its parameters extraction procedure. This model is embedded in the ECLSM, which is then implemented in CAD software and validated by pulsed and continuous large‐signal measurements of on‐wafer 8 × 125‐μm GaN on SiC substrate HEMT. Pulsed IV simulations show that the model can efficiently describe the bias dependency of trapping and self‐heating effects. Single‐ and two‐tone simulation results show that the model can accurately predict the output power and its harmonics and the associated intermodulation distortion (IMD) under different input‐power and bias conditions. © 2012 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2013.     

宽带钢厚度模型的研究与优化

介绍莱钢宽带钢厚度模型的优化,以改善板形,提高控制精度.     

非圆曲线的数控处理及程序实现

引用优化设计中一维搜索和黄金分割法,针对数控加工中非圆曲线的处理,给出实用、简洁和新颖的宏程序实现方法,解决在数控系统中求解方程组的问题,是数控用户快速解决相关应用问题的有效手段。     

Rigidity of a simple extended lower triangular matrix

For the all-ones lower triangular matrices, the upper and lower bounds on rigidity are known to match [P. Pudlak, Z. Vavrin, Computation of rigidity of order n²/r for one simple matrix, Comment Math. Univ. Carolin. 32 (2) (1991) 213-218]. In this short note, we apply these techniques to the all-ones extended lower triangular matrices, to obtain upper and lower bounds with a small gap between the two; we show that the rigidity is .     

Expected runtimes of evolutionary algorithms for the Eulerian cycle problem

Evolutionary algorithms are randomized search heuristics, which are applied to problems whose structure is not well understood, as well as to problems in combinatorial optimization. They have successfully been applied to different kinds of arc routing problems. To start the analysis of evolutionary algorithms with respect to the expected optimization time on these problems, we consider the Eulerian cycle problem. We show that a variant of the well-known (1+1)$(1+1)$ EA working on the important encoding of permutations is able to find an Eulerian tour of an Eulerian graph in expected polynomial time. Altering the operator used for mutation in the considered algorithm, the expected optimization time changes from polynomial to exponential.     

Rollout sampling approximate policy iteration

Several researchers have recently investigated the connection between reinforcement learning and classification. We are motivated by proposals of approximate policy iteration schemes without value functions, which focus on policy representation using classifiers and address policy learning as a supervised learning problem. This paper proposes variants of an improved policy iteration scheme which addresses the core sampling problem in evaluating a policy through simulation as a multi-armed bandit machine. The resulting algorithm offers comparable performance to the previous algorithm achieved, however, with significantly less computational effort. An order of magnitude improvement is demonstrated experimentally in two standard reinforcement learning domains: inverted pendulum and mountain-car.     

改进的猴王遗传算法求解组合拍卖最优竞胜标

应用遗传算法求解第一价格封闭组合拍卖问题,编码及约束条件复杂,在交叉和变异的过程中容易产生大量不可行解,并且在选择和精英保留中,一些局部最优点(早熟点)对应的个体容易被大量复制,使群体多样性下降。该文就以上的问题,改进了原有遗传算法求解组合拍卖最优竞胜标的方法,并采用了猴王遗传算法的精英保留策略。实际计算证明本算法编码方式自然并且直观表达了个体和标的的关系。在交叉和变异算子中,产生不可行解的数量下降,更积极的保存了上一代进化的部分成果。而改进猴王遗传算法的精英保留策略,使算法能够以高概率跳出早熟,达到算法速度与准确的平衡。     

一种基于GPU加速的细粒度并行蚁群算法

为改善蚁群算法对大规模旅行商问题的求解性能,提出一种基于图形处理器(GPU)加速的细粒度并行蚁群算法.将并行蚁群算法求解过程转化为统一计算设备架构的线程块并行执行过程,使得蚁群算法在GPU中加速执行.实验结果表明,该算法能提高全局搜索能力,增大细粒度并行蚁群算法的蚂蚁规模,从而提高了算法的运算速度.     

基于双极偏好控制的多目标粒子群优化算法

考虑双极偏好信息对粒子群的控制作用,提出一种使用双极偏好——正偏好和负偏好引导粒子群向 Pareto 前沿偏好区域进化的方法．根据TOPSIS 决策法思想,将外部种群粒子与正负偏好点的相对贴近度排序作为 外部种群管理和全局最优解更新策略;根据贴近度值确定解集的分布度;选取6 种不同类型的多目标测试函数进行 算法模拟,从世代距离、空间测度和超体积测度3 个指标与基于单极偏好的多目标粒子算法进行性能比较．结果显 示,基于双极偏好控制的多目标粒子群算法的收敛性和综合性能更优秀．