一种改进的Heaviside函数与Dirac函数的C-V图像分割研究

C-V模型中Heaviside函数和Dirac函数正则化逼近影响对目标图像的分割,根据Heaviside函数和Dirac函数的性质,提出了新的正则化Heaviside函数和Dirac函数.首先分析了C-V模型中正则化的Heaviside函数和Dirac函数在图像分割中所起的作用,在此基础上提出了新的正则化的Heaviside函数和Dirac函数,改进了C-V模型.实验结果表明,运用正则化的Heaviside函数和Dirac函数的图像分割效果较好.     

分段函数的分类及分离型分段函数与初等函数之间的关系

将分段函数划分为连结型分段函数 ,分离型分段函数和它们的组合形式三种类型 ,得到了分离型分段函数是初等函数的充分必要条件 ,完整地解决了分离型分段函数与初等函数之间的关系 ,并且给出了初等函数在其任一截取集上的限制函数 (截取函数 )仍然是初等函数的结果     

绝对值函数的一致光滑逼近函数

绝对值函数是一个非光滑函数,研究了绝对值函数的光滑逼近函数.给出了绝对值函数的上方一致光滑逼近函数和下方一致光滑逼近函数,分别研究了其性质,并通过图像展示了逼近效果.     

例谈函数定义域对研究函数性质的影响

函数是高中阶段的重点内容．函数的定义域、解析式和值域统称为函数的三要素,其中定义域和解析式决定了函数的值域．许多同学在学习函数时总是比较重视函数的解析式,忽略函数的定义域,从而造成很多错解．实际上,函数的定义域改变了,函数也就改变了,它是函数不可忽视的重要组成部分,下面举例说明．     

工程项目施工状态诊断模型

文章提出了工程项目施工的投资状态函数、进度状态函数、质量状态函数和环境状态函数，并以状态函数为基础，建立了项目施工状态诊断模型。文章给出各层次中的状态函数，理出了施工状态量化诊断的思路，对各层次中状态诊断函数的描述作了探索。     

Walsh函数的一种新定义及快速算法设计

传统的Walsh函数是以Rademacher函数为基函数生成 .本文运用对称复制的观点 ,定义了一种新函数 G函数 ,并以G函数为基础 ,定义了四种序的Walsh函数 ,同时 ,运用序码分析方法 ,实现了两种序Walsh变换的快速算法设计 .     

P-可测函数的构造性质

可测函数的构造性质是定义它关于测度μ的积分的理论基础.为了在P-测度空间上定义P-积分,借鉴可测函数的构造性质,引入了P-示性函数、P-简单函数、P-初等函数以及P-可测函数的概念,在此基础上系统地研究了P-实可测函数、有界P-实可测函数和非负P-可测函数与P-简单函数序列及P-初等函数序列的收敛关系;找出了P-实可测的充分必要条件;证明了实P-可测函数正部和负部都是非负P-实可测函数,最终得出任何P-实可测函数均可以表示为二非负P-可测函数之差,为定义P-积分提供了理论依据.     

亚纯函数多项式与其导函数分担小函数的唯一性

利用Nevanlinna理论与多项式加权和的概念研究了一类亚纯函数多项式与其导函数分担小函数的唯一性.在分担小函数的条件下,得到了亚纯函数的特征函数与计数函数之间的一个不等式,并研究了亚纯函数多项式与其k阶导数分担小函数的唯一性问题,所得到的定理补充,改进与推广了一些已有的结果.     

巧用单调性妙解竞赛题

函数的单调性是函数的重要性质,掌握了一个函数的单调性就意味着我们从总体上把握了函数的变化趋势,函数的单调性是画函数图象求函数极值、最值的重要依据．有些数学问题特别是数学竞赛题,若能自觉运用函数思想构造函数,     

一道大学生数学竞赛题的联想

通过对一个正弦函数不等式的类比,给出了余弦函数、正切函数、双曲正弦函数、双曲余弦函数、反正弦函数、反双曲正弦函数的不等式.     

A survey of simulated annealing as a tool for single and multiobjective optimization

This paper presents a comprehensive review of simulated annealing (SA)-based optimization algorithms. SA-based algorithms solve single and multiobjective optimization problems, where a desired global minimum/maximum is hidden among many local minima/maxima. Three single objective optimization algorithms (SA, SA with tabu search and CSA) and five multiobjective optimization algorithms (SMOSA, UMOSA, PSA, WDMOSA and PDMOSA) based on SA have been presented. The algorithms are briefly discussed and are compared. The key step of SA is probability calculation, which involves building the annealing schedule. Annealing schedule is discussed briefly. Computational results and suggestions to improve the performance of SA-based multiobjective algorithms are presented. Finally, future research in the area of SA is suggested.     

Hybrid parallel tempering and simulated annealing method

In this paper, we propose a new hybrid scheme of parallel tempering and simulated annealing (hybrid PT/SA). Within the hybrid PT/SA scheme, a composite system with multiple conformations is evolving in parallel on a temperature ladder with various transition step sizes. The simulated annealing (SA) process uses a cooling scheme to decrease the temperature values in the temperature ladder to the target temperature. The parallel tempering (PT) scheme is employed to reduce the equilibration relaxation time of the composite system at a particular temperature ladder configuration in the SA process. The hybrid PT/SA method reduces the waiting time in deep local minima and thus leads to a more efficient sampling capability on high-dimensional complicated objective function landscapes. Compared to the approaches PT and parallel SA with the same temperature ladder, transition step sizes, and cooling scheme (parallel SA) configurations, our preliminary results obtained with the hybrid PT/SA method confirm the expected improvements in simulations of several test objective functions, including the Rosenbrock’s function and the “rugged” funnel-like function, and several instantiations of the traveling salesman problem. The hybrid PT/SA may have slower convergence than genetic algorithms (GA) with good crossover heuristics, but it has the advantage of tolerating “bad” initial values and displaying robust sampling capability, even in the absence of additional information. Moreover, the hybrid PT/SA has natural parallelization potential.     

关于随机优化算法的几点讨论

关于随机优化算法的几点讨论李晓莉，雷功炎（河南驻马店师专，北京大学数学系）ＳＯＭＥＤＩＳＣＵＳＳＩＯＮＳＡＢＯＵＴＳＴＯＣＨＡＳＴＩＣＯＰＴＩＭＩＺＡＴＩＯＮＡＬＧＯＲＩＴＨＭＳ￥ＬｉＸｉａｏ－ｌｉ（ＤｅｐａｒｔｍｅｎｔｏｆＭａｔｈｅｍａｔｉｃｓ，Ｚ...     

Comparative analysis of three metaheuristics for short-term open pit block sequencing

This paper presents the application of simulated annealing (SA), Tabu search (TS) and hybrid TS–SA to solve a real-world mining optimisation problem called open pit block sequencing (OPBS). The OPBS seeks the optimum extraction sequences under a variety of geological and technical constraints over short-term horizons. As industry-scale OPBS instances are intractable for standard mixed integer programming (MIP) solvers, SA, TS and hybrid TS–SA are developed to solve the OPBS problem. MIP exact solution is also combined with the proposed metaheuristics to polish solutions in feasible neighbourhood moves. Extensive sensitivity analysis is conducted to analyse the characteristics and determine the optimum sets of values of the proposed metaheuristics algorithms’ parameters. Computational experiments show that the proposed algorithms are satisfactory for solving the OPBS problem. Additionally, this comparative study shows that the hybrid TS–SA is superior to SA or TS in solving the OPBS problem in several aspects.     

Linear interpolation for image conversion between square structure and hexagonal structure

Spiral Architecture (SA) is a relatively new and powerful image structure consisting of hexagonal pixels arranged in a onedimensional coordinate system. However, all the existing hardware for capturing image and for displaying image are produced based on traditional square image structure. In this paper, we present a software approach to represent an image on SA based on linear interpolation method. We perform edge detection on SA to demonstrate the advantages of image processing on SA. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Performance of simulated annealing-based heuristic for the unconstrained binary quadratic programming problem

The unconstrained binary quadratic programming problem (BQP) is known to be NP-hard and has many practical applications. This paper presents a simulated annealing (SA)-based heuristic for the BQP. The new SA heuristic for the BQP is based on a simple (1-opt) local search heuristic and designed with a simple cooling schedule, but the multiple annealing processes are adopted. To show practical performances of the SA, we test on publicly available benchmark instances of large size ranging from 500 to 2500 variables and compare them with other heuristics such as multi-start local search, the previous SA, tabu search, and genetic algorithm incorporating the 1-opt local search. Computational results indicate that our SA leads to high-quality solutions with short times and is more effective than the competitors particularly for the largest benchmark set. Furthermore, the values of new best-known solutions found by the SA for several large instances are also reported.     

Simulated annealing for discrete optimization with estimation

We extend the basic convergence results for the Simulated Annealing (SA) algorithm to a stochastic optimization problem where the objective function is stochastic and can be evaluated only through Monte Carlo simulation (hence, disturbed with random error). This extension is important when either the objective function cannot be evaluated exactly or such an evaluation is computationally expensive. We present a modified SA algorithm and show that under suitable conditions on the random error, the modified SA algorithm converges in probability to a global optimizer. Computational results and comparisons with other approaches are given to demonstrate the performance of the proposed modified SA algorithm.     

带学习效应的两台平行机时间表长问题

研究机器带学习效应, 目标函数为时间表长的两台平行机排序问题, 问题是NP-难的. 首先建立了求解该问题最优解的整数规划模型. 其次, 基于模拟退火算法给出了该问题的近似算法SA, 并证明了该算法依概率1 全局收敛到最优解. 最后, 通过数值模拟对所提出的算法进行了性能分析. 数值模拟结果表明, 近似算法SA可以达到最优值的99%, 准确度高, 算法较有效.     

On simulated annealing with temperature-dependent energy and temperature-dependent communication

Simulated annealing (SA) is a generic optimization method that is quite popular because of its ease of implementation and its optimal convergence properties. Still, SA is widely reported to converge very slowly and it is common practice to allow extra freedom in its design at the expense of losing global convergence guarantees.In this paper, we derive simple sufficient conditions for the global convergence of SA when the cost function and the candidate solution generation mechanism are temperature-dependent. These conditions are surprisingly weak-they do not involve the variations of the cost function with temperature-and exponential cooling makes it possible to be arbitrarily close to the best possible convergence exponent of standard SA.