比例风险模型下参数极大似然估计的自适应优化算法及其改进算法

自适应优化算法可避免很多常用数值算法遭遇的困难,例如:高维矩阵求逆问题,初值选取的问题和算法的收敛问题等等.因此,自适应优化算法得到了迅速的发展和广泛的应用,本文研究了比例风险模型下的自适应优化算法.首先利用三种自适应优化算法-Adam算法、RMSprop算法、Adagrad算法求解比例风险模型下的参数估计数值解问题,获得了自适应算法的计算优良性.然后,推广了比例风险模型下的Adam算法的研究,发展了一种改进的Adam算法,进一步提高了算法的计算速度并展现了其计算优势.     

求解全局优化问题的改进蝙蝠算法[英文]

蝙蝠算法(BA)是一类基于试探技巧的群智能优化算法,该算法已被广泛用于诸多领域问题的求解.本文提出一个改进的蝙蝠算法NIBA.在算法中,为了加强蝙蝠算法的局部和全局搜索能力,提出了三个改进策略.首先,为了改进蝙蝠的局部搜索能力,在当前最优解处给出了一个新的搜索方程.其次,为了改进算法的全局搜索能力,平衡算法的开发能力和探索能力,算法吸收并改进了和声搜索机制.最后,为了进一步提高NIBA算法的搜索能力,在当前最优解处,算法采用了混沌搜索机制.为了验证算法的性能,针对18个标准测试函数进行了数值实验.与其它算法的比较结果显示,NIBA算法具有更好的稳定性,且效率更高.     

一种古代的中国算法:盈不足术与牛顿迭代算法的比较

详细讨论了大约在公元前二世纪广泛流行的一种中国算法,这种算法在西方被称作为双假设法。强调指出双假设法是中国算法的一种译版。首次给出了中国算法与牛顿迭代算法之间的联系,如果引入了导数的概念,中国算法可以非常方便地转化为牛顿迭代算法。提出了一种改进的中国算法,并给出中国算法在非线性振动方程中的应用。     

学习算法的稳定性与泛化(Ⅱ):有界差分稳定框架

根据有界差分条件,提出了学习算法的有界差分稳定框架.依据新框架,研究了机器学习阈值选择算法,再生核Hilbert空间中的正则化学习算法,Ranking学习算法和Bagging算法,证明了对应学习算法的有界差分稳定性.所获结果断言了这些算法均具有有界差分稳定性,从而为这些算法的应用奠定了理论基础.     

整数扩展欧几里得矩阵序列的选择项算法的修正

Wang和Pan提出了一个计算整数扩展欧几里得矩阵序列的选择项的算法,并把此算法应用于模有理数重构问题和数值有理数重构问题.这个算法仅消耗接近线性的时间复杂度,与目前已知的整数gcd算法的最佳时间复杂度相一致,而整数gcd算法只是此算法的一个特殊情形.分析了这个算法,指出了算法中由于考虑的不够全面而存在的错误,补充了矩阵序列性质的理论部分,并修正这个算法.     

利用模拟退火遗传算法实现图像阈值分割

本文提出了一种利用模拟退火算法和遗传算法相结合的图像阈值分割算法,试验结果表明该算法增强了算法的全局收敛性,加快了算法的收敛速度,提高了图像阈值分割的效率.     

GA-BP嵌套算法的理论及应用

分析了BP算法、遗传算法以及GA-BP-APARTING算法的特点,提出了GA-BP-NESTING算法.在人工神经网络的在线学习和离线学习方式下,分别对BP算法、GA算法、GA-BP-APARTING算法和GA-BP-NESTING算法进行了比较研究,研究发现:第一,网络初始权值的赋值对人工神经网络训练影响很大;第二,离线学习方式下GA-BP-NESTING算法效果最佳.     

偏微分方程的局部保结构算法

讨论偏微分方程的局部保结构算法,它是原来的整体保结构算法的自然推广. 当边界条件适宜时, 局部保结构算法自然是整体保结构算法, 但整体保结构算法却不一定是局部保结构算法. 局部保结构算法的概念能解释不同保结构算法之间的差异性, 也能为分析和构造性能较好的保结构算法提供理论基础. 不仅如此, 合适的边界条件不再是局部保结构算法可应用于偏微分方程的必要条件, 从而拓宽了保结构算法的适用性. 还讨论了局部保结构算法的应用和系统构造问题, 得到了非线性Klein-Gordon方程的一些新的格式.     

矩阵填充的硬阈值修正算法

提出了使用硬阈值进行矩阵填充的修正算法.算法通过对迭代矩阵进行对角修正来完成矩阵填充,并给出了算法的收敛性分析.最后通过数值实验比较了修正算法与硬阈值算法填充的数值结果,显示出了新算法的优越性.     

蚂蚁算法和小生境遗传算法的融合

蚂蚁算法是一种新型的模拟进化算法,也是一种随机型智能搜索算法.较为系统的总结了算法的基本理论,分析了其基本算法解决TSP问题的模型,针对蚂蚁算法易出现停滞的缺点,把小生境遗传算法和蚂蚁算法融合,仿真比较实验结果表明优于基本蚂蚁算法.     

二维恒定各向同性介质渗透系数反演的遗传算法

给出了利用遗传算法求解二维恒定各项同性介质渗透系数反演的一种新方法,该方法把参数反演问题转化为优化问题通过遗传算法求解.数值模拟结果表明:该方法具有精度高、收敛速度快、编程简单、易于计算机实现等优点,值得在实际工作采用.     

Dual extragradient algorithms extended to equilibrium problems

In this paper we propose two iterative schemes for solving equilibrium problems which are called dual extragradient algorithms. In contrast with the primal extragradient methods in Quoc et al. (Optimization 57(6):749–776, 2008) which require to solve two general strongly convex programs at each iteration, the dual extragradient algorithms proposed in this paper only need to solve, at each iteration, one general strongly convex program, one projection problem and one subgradient calculation. Moreover, we provide the worst case complexity bounds of these algorithms, which have not been done in the primal extragradient methods yet. An application to Nash-Cournot equilibrium models of electricity markets is presented and implemented to examine the performance of the proposed algorithms.     

Solving large scale Max Cut problems via tabu search

In recent years many algorithms have been proposed in the literature for solving the Max-Cut problem. In this paper we report on the application of a new Tabu Search algorithm to large scale Max-cut test problems. Our method provides best known solutions for many well-known test problems of size up to 10,000 variables, although it is designed for the general unconstrained quadratic binary program (UBQP), and is not specialized in any way for the Max-Cut problem.     

Some algorithms for semi-invariants of quivers

We present some theorems and algorithms for calculating perpendicular categories and locally semi-simple decompositions. We implemented a computer program TETIVA based on these algorithms and we offer this program for everybody's use.     

Microlay: An interactive computer program for factory layout planning on microcomputers

In this paper an interactive computer program for factory layout planning on microcomputers is presented. Construction and improvement algorithms have been integrated into the program as tools for supporting the planner in generating a satisfactory layout. To avoid unrealistic solutions the geometry of the location objects is not affected by these algorithms. Finally, the program offers several options for speeding up the editing process.     

Global Optimization Approach to Unequal Global Optimization Approach to Unequal Sphere Packing Problems in 3D

The problem of the unequal sphere packing in a 3-dimen-sional polytope is analyzed. Given a set of unequal spheres and a poly-tope, the double goal is to assemble the spheres in such a way that (i) they do not overlap with each other and (ii) the sum of the volumes of the spheres packed in the polytope is maximized. This optimization has an application in automated radiosurgical treatment planning and can be formulated as a nonconvex optimization problem with quadratic constraints and a linear objective function. On the basis of the special structures associated with this problem, we propose a variety of algorithms which improve markedly the existing simplicial branch-and-bound algorithm for the general nonconvex quadratic program. Further, heuristic algorithms are incorporated to strengthen the efficiency of the algorithm. The computational study demonstrates that the proposed algorithm can obtain successfully the optimization up to a limiting size.     

Automatic grading of students' ALGOL programming

The report describes an experiment on automatic grading of student algorithms, using an ALGOL compiler. The experiment is based on an evaluation of the efficiency and logical completeness of the algorithms, not on their formal correctness, which is supposed to be checked in advance by the individual student. The technique used is to embed the student algorithms within a larger grading program structure, which supplies test cases and performs checks and evaluation. The complete text of such a grading program is given. The experience gained through the experiment, and suggestions for further developments, are discussed.     

New stopping criteria for detecting infeasibility in conic optimization

Detecting infeasibility in conic optimization and providing certificates for infeasibility pose a bigger challenge than in the linear case due to the lack of strong duality. In this paper we generalize the approximate Farkas lemma of Todd and Ye (Math Program 81:1–22, 1998) from the linear to the general conic setting, and use it to propose stopping criteria for interior point algorithms using self-dual embedding. The new criteria can identify if the solutions have large norm, thus they give an indication of infeasibility. The modified algorithms enjoy the same complexity bounds as the original ones, without assuming that the problem is feasible. Issues about the practical application of the criteria are also discussed. The authors were supported by the Canada Research Chairs program, NSERC Discovery Grant #5-48923 and MITACS.     

An Application of the Micro to Product Design and Distribution

This article illustrates an application of the micro-computer to some common problems in product design and distribution. One problem which receives particular attention is that of pallet loading. Existing algorithms for determining efficient layouts on rectangular pallets are shown to suffer from a number of shortcomings, and an improved method is presented. Extensions of this method to more complex pallet designs are discribed. Initially a program for mainframe computer operation was produced, and this was subsequently transferred to a micro-computer. The problems of such a transfer are discussed, and the flexibility and power of the micro-computer as a management tool are illustrated.