三角形与三棱锥的一个向量性质的逆定理

文[1]、文[2]、文[3]及文[4]对一个三角形重心向量性质进行拓广，文[5]证明了文[1]的逆定理也成立，文[6]将以上的重心性质进行了再推广得到了两个定理，我们可以将这两个定理加强为以下两个命题，证明类似文[6]在此不再证明．     

一道西班牙奥赛题的再探究

拜读文[1]与文[2],文[2]指(给)出了文[1]中五个例题中证法的错误或结论的错误或证法的改进.但阅读中发现文[2]的证法依然是值得改进的,更为严重的是文[2]竟然把文[1]的例2误判     

三角形与三棱锥的一个向量性质的逆定理

文[1]、文[2]、文[3]及文[4]对一个三角形重心向量性质进行拓广,文[5]证明了文[1]的逆定理也成立,文[6]将以上的重心性质进行了再推广得到了两个定理,我们可以将这两个定理加强为以下两个命题,证明类似文[6]在此不再证明.命题1如图1所示,设P为△ABC所在平面上任意一点,λ1PA λ     

三角形的一个向量性质再探究

文[1]给出了三角形重心向量的一个性质，并进行了空间拓广．文[2]对三角形内任一点的向量性质进行了探究，并进行了空间拓广．文[3]对文[1]的性质进行再探究，本文类比文[3]对文[2]的性质进行再探究，得到了两个定理，现叙述如下．     

关于《一类奇异边值问题的正解》的注记

文[4]通过构造反例断言文[1]中定理的必要性证明有误,本文首先指出文[4] 的这个断言不正确,然后对文[4]中定理2.1作了本质性的改进.     

三角形的一个向量性质及其空间拓广

文[1]、文[2]、文[3]及文[4]对一个三角形重心向量性质进行了探讨，笔者阅读后深受启发，得到了三角形的一个向量性质，并进行空间拓广．     

一个不等式的再质疑与另证

文[2]指出了文[1]的错误，并给出了证明，但文[2]的证明仍然是错误的．原因如下：     

一道不等式试题的加强

文[1]，文[2]，文[3]对以下一道美国大学生数学竞赛试题进行了探究与推广：     

三角形的一个向量性质再探究

文[1]给出了三角形重心向量的一个性质,并进行了空间拓广.文[2]对三角形内任一点的向量性质进行了探究,并进行了空间拓广.文[3]对文[1]的性质进行再探究,本文类比文[3]对文[2]的性质进行再探究,得到了两个定理,现叙述如下.定理1如图1所示,已知△ABC及其内部一点P,若λ1PA λ2PB     

曲线拐点充分条件证明中的常见错误

文[1]给出了判别曲线拐点的两个充分条件,文[2]给出了一个充分条件,但三个定理的证明都是错误的.同时,文[1]的两个推论也是错误的.本文通过反例分析了其错因,并给出了文[1]中一个拐点充分条件的正确证明.     

CSP—1方案的中止概率曲线及其应用

本文结合国家标准ＧＢ８０５２—８７［１］，利用中止概率曲线的概念，讨论了连续抽样方案ＣＳＰ—１的中止检查规则及其统计特性，并提出了确定最优中止参数的原则和方法     

一个矩的不等式及其应用

本文证明了一类随机变量数学期望关于参数的单调性，并指出它在序贯分析中的应用。     

两指标点过程的停止及σ-域

本文给出两指标点过程的跳线刻划、停止方法,论述了跳线的可测性及点过程有关的σ-域的结构和特征。     

On a variational inequality associated with a stopping game combined with a control

We study a non-linear elliptic variational inequality which corresponds to a zero-sum stopping game (Dynkin game) combined with a control. Our result is a generalization of the existing works by Bensoussan [ Stochastic Control by Functional Analysis Methods (North-Holland, Amsterdam), 1982], Bensoussan and Lions [ Applications des Inéquations Variationnelles en Contrôle Stochastique (Dunod, Paris), 1978] and Friedman [ Stochastic Differential Equations and Applications (Academic Press, New York), 1976] in the sense that a non-linear term appears in the variational inequality, or equivalently, that the underlying process for the corresponding stopping game is subject to a control. By using the dynamic programming principle and the method of penalization, we show the existence and uniqueness of a viscosity solution of the variational inequality and describe it as the value function of the corresponding combined-stochastic game problem.     

Enhancing PSO methods for global optimization

The Particle Swarm Optimization (PSO) method is a well-established technique for global optimization. During the past years several variations of the original PSO have been proposed in the relevant literature. Because of the increasing necessity in global optimization methods in almost all fields of science there is a great demand for efficient and fast implementations of relative algorithms. In this work we propose three modifications of the original PSO method in order to increase the speed and its efficiency that can be applied independently in almost every PSO variant. These modifications are: (a) a new stopping rule, (b) a similarity check and (c) a conditional application of some local search method. The proposed were tested using three popular PSO variants and a variety test functions. We have found that the application of these modifications resulted in significant gain in speed and efficiency.     

A model of a 2-player stopping game with priority and asynchronous observation

Various models of 2-player stopping games have been considered which assume that players simultaneously observe a sequence of objects. Nash equilibria for such games can be found by first solving the optimal stopping problems arising when one player remains and then defining by recursion the normal form of the game played at each stage when both players are still searching (a 2 × 2 matrix game). The model considered here assumes that Player 1 always observes an object before Player 2. If Player 1 accepts the object, then Player 2 does not see that object. If Player 1 rejects an object, then Player 2 observes it and may choose to accept or reject it. It is shown that such a game can be solved using recursion by solving appropriately defined subgames, which are played at each moment when both players are still searching. In these subgames Player 1 chooses a threshold, such that an object is accepted iff its value is above this threshold. The strategy of Player 2 in this subgame is a stopping rule to be used when Player 1 accepts this object, together with a threshold to be used when Player 1 rejects the object. Whenever the payoff of Player 1 does not depend on the value of the object taken by Player 2, such a game can be treated as two optimisation problems. Two examples are given to illustrate these approaches.     

Statistical testing of optimality conditions in multiresponse simulation-based optimization

This article studies simulation-based optimization with multiple outputs. It assumes that the simulation model has one random objective function and must satisfy given constraints on the other random outputs. It presents a statistical procedure for testing whether a specific input combination (proposed by some optimization heuristic) satisfies the Karush–Kuhn–Tucker (KKT) first-order optimality conditions. The article focuses on “expensive” simulations, which have small sample sizes. The article applies the classic t test to check whether the specific input combination is feasible, and whether any constraints are binding; next, it applies bootstrapping (resampling) to test the estimated gradients in the KKT conditions. The new methodology is applied to three examples, which gives encouraging empirical results.     

Reliability analysis of a renewable multiple cold standby system

We present a general reliability analysis of the basic multiple cold standby system attended by a single repair facility. The particular case of deterministic repair provides some explicit results for the survival function illustrated by computer-plotted graphs.     

GEOMETRIC METHOD OF SEQUENTIAL ESTIMATION RELATED TO MULTINOMIAL DISTRIBUTION MODELS

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Optimal selling time in stock market over a finite time horizon

In this paper, we examine the best time to sell a stock at a price being as close as possible to its highest price over a finite time horizon [0, T ], where the stock price is modelled by a geometric Brownian motion and the ’closeness’ is measured by the relative error of the stock price to its highest price over [0, T ]. More precisely, we want to optimize the expression: where (V t ) t≥0 is a geometric Brownian motion with constant drift α and constant volatility σ > 0, M t = max Vs is the running maximum of the stock price, and the supremum is taken over all possible stopping times 0 ≤τ≤ T adapted to the natural filtration (F t ) t≥0 of the stock price. The above problem has been considered by Shiryaev, Xu and Zhou (2008) and Du Toit and Peskir (2009). In this paper we provide an independent proof that when α = 1 2 σ 2 , a selling strategy is optimal if and only if it sells the stock either at the terminal time T or at the moment when the stock price hits its maximum price so far. Besides, when α > 1 2 σ 2 , selling the stock at the terminal time T is the unique optimal selling strategy. Our approach to the problem is purely probabilistic and has been inspired by relating the notion of dominant stopping ρτ of a stopping time τ to the optimal stopping strategy arisen in the classical "Secretary Problem".