一个不等式的推广

文[1]，[2]给出了一个不等式：设αi〉0,pi≥0（i=1,2,…,n）且p1＋p2＋…＋pn=1，则1/n↑∑↑i=1pi/αi≤n↑П↑i=1αi^pi≤n∑↑i=1piαi。     

关于多项式系数的整除性

设n是正整数,k1,k2,…+k1=n的非负整数,正整数[nk1k2…ks]=n！/k1!k2!…k5!称为多项式系数,本文讨论了当n=a0+a1p+a2p^2+…arp^r,其中p为素数且p≤n,0≤ai&;lt;p(0≤i≤r);ki=a0^(i)+a1^(i)p+…+ar^(i)p^r,其中ki≤0,∑^si=1,ki=n,0≤ak^(i)p(0≤i&;lt;s)时多项式系数的整除性问题,得出的结果推广了著名的Lucas定理^[1].     

一个不等式的逆向

文[1]证明了文[2]提出的一个猜想:说ai≥0,pi≥0,(i=1,2,…,n)且p1 p2 … pn=1,则n∑i=1piai≥n∏i=1aipi.本文将给出上述不等式的一个逆向不等式,从而得出一个不等式组.命题设ai>0,pi≥0(i=1,2,…,n),且p1 p2 … pn=1,则1∑ni=1piai≤n∏i=1aipi≤n∑i=1piai.证为了使命题证明     

关于一类随机矩阵特征根极限分布的注记

一 引言 考虑k个p维总体X_1,X_2,…,X_k。假定它们都服从正态分布,其均值向量分别是(ξ_(1i),ξ_(2i),…,ξ_(pi)),i=1,2,…,k,且具有共同的协方差矩阵∑=(σ_(ij)),i,j=1,2,…,p。考虑矩阵     

一个不等式的推广

文[1],[2]给出了一个不等式:设ai>0,pi≥0(i=1,2,…,n)且p1 p2 … pn=1,则1∑ni=1piai≤∏ni=1aipi≤∑ni=1piai.本文再给出上述不等式一个推广情形:命题设ai>0,pi≥0(i=1,2,…,n),且各pi不全为0.则:∑ni=1pi∑ni=1piai≤(∏ni=1aipi)1∑ni=1pi≤∑ni=1piai∑ni=1pi.为了证明该     

关于调和数的一个结论

设n是大于1的正常数,并且设n=pα11p2α2…ptαt,其中pi为素数,i=1,2,…,t,ω(n)表示n的不同素因子的个数,即ω(n)=t.若n的所有因子的倒数和为整数,即0≤∑ij≤αjj=1,2,…,t1p1i1pi22…ptit为整数,称n是调和数.证明了和调和数相关的一个结论.     

关于m个相关回归方程系统回归系数的两步估计

一、前言 考虑m个回归方程系统如下yi=Xiβi εi(i=1,2,…,m),(1)其中在第i(i=1,2,…,m)个方程中,yi是n×1的随机观察值向量,Xi是秩为pi的n×pi阶矩阵,βi是pi×1的未知参数向量,而εi是n×1的误差向量。 惯常的方法是假定误差向量ε_1,ε_2,…,ε_m是互相独立地服从正态分布,其均值是E(εi)=0,方(协)差矩阵是D(εi)=σ_i~2I_n(i=1,2,…,m),这里I_n表示n阶单位阵,σ_i~2是未知参数。在这样的假定下,估计回归系数βi只须单从第i个方程求得其最小     

OSCILLATION OF ADVANCED DIFFERENCE EQUATIONS WITH VARIABLE COEFFICIENTS

The oscillations of the advanced difference equationsxn - xn-1 - p(n)xn+k = 0, n ≥ 0, (*)andXn - Xn-1 - ∑i=1mpi(n)xn+ki=0, n ≥0, (**)are investigated, where p(n),pi(n)(i = 1,2,..., m) are nonnegative real numbers. First. a sufficienet condition for the oscillation of equation (*) is obtained, then the result is generalized to the equation (**). At last, an example is given to illustrate the advantages of our results. Our results are new.     

几个不等式的简证

《数学通讯》(教师版)2006年上年度刊登了一组关于不等式研究的专题文章,笔者拜读之后受益匪浅,笔者探究发现其中的几个不等式更加简捷的证明方法,现写出来,供读者参考.例1[1]设ai>0,pi≥0(i=1,2,…,n),且p1 p2 … pn=1,则1n∑i=1piai≤n∏i=1aipi≤n∑i=1piai(1)这是文[1]对文[2]证明的如下一个不等式的逆向不等式:设ai>0,pi≥0,(i=1,2,…,n)且p1 p2 … pn=1.则n∑i=1piai≥n∏i=1aipi(2)文[1]通过构造函数,考察函数的凸性,然后用数学归纳法证明了(1)式.其实,有了(2)式,(1)式的证明便唾手可得,不必绕道而行.事实上,由(2)式∑ni=1piai=∑ni=…     

无失效数据失效概率的估计

本文对无失效数据(ti,ni),在时刻ti的失效概率pi=p{T＜ti}的先验分布为不完全Beta分布Beta(pi-1,λi;1,b)时,给出了pi的多层Bayes估计,从而可以得到无失效数据可靠度的估计.     

Convex quadratic programming with one constraint and bounded variables

In this paper we propose an iterative algorithm for solving a convex quadratic program with one equality constraint and bounded variables. At each iteration, a separable convex quadratic program with the same constraint set is solved. Two variants are analyzed: one that uses an exact line search, and the other a unit step size. Preliminary testing suggests that this approach is efficient for problems with diagonally dominant matrices. This work was supported by a research grant from the France-Quebec exchange program and also by NSERC Grant No. A8312. The first author was supported by a scholarship from Transport Canada while doing this research.     

A partitioning and bounded variable algorithm for linear programming

An interesting new partitioning and bounded variable algorithm (PBVA) is proposed for solving linear programming problems. The PBVA is a variant of the simplex algorithm which uses a modified form of the simplex method followed by the dual simplex method for bounded variables. In contrast to the two-phase method and the big M method, the PBVA does not introduce artificial variables. In the PBVA, a reduced linear program is formed by eliminating as many variables as there are equality constraints. A subproblem containing one ‘less than or equal to’ constraint is solved by executing the simplex method modified such that an upper bound is placed on an unbounded entering variable. The remaining constraints of the reduced problem are added to the optimal tableau of the subproblem to form an augmented tableau, which is solved by applying the dual simplex method for bounded variables. Lastly, the variables that were eliminated are restored by substitution. Differences between the PBVA and two other variants of the simplex method are identified. The PBVA is applied to solve an example problem with five decision variables, two equality constraints, and two inequality constraints. In addition, three other types of linear programming problems are solved to justify the advantages of the PBVA.     

Nonlinear integer programming for optimal allocation in stratified sampling

A stratified random sampling plan is one in which the elements of the population are first divided into nonoverlapping groups, and then a simple random sample is selected from each group. In this paper, we focus on determining the optimal sample size of each group. We show that various versions of this problem can be transformed into a particular nonlinear program with a convex objective function, a single linear constraint, and bounded variables. Two branch and bound algorithms are presented for solving the problem. The first algorithm solves the transformed subproblems in the branch and bound tree using a variable pegging procedure. The second algorithm solves the subproblems by performing a search to identify the optimal Lagrange multiplier of the single constraint. We also present linearization and dynamic programming methods that can be used for solving the stratified sampling problem. Computational testing indicates that the pegging branch and bound algorithm is fastest for some classes of problems, and the linearization method is fastest for other classes of problems.     

Bounded knapsack sharing

A bounded knapsack sharing problem is a maximin or minimax mathematical programming problem with one or more linear inequality constraints, an objective function composed of single variable continuous functions called tradeoff functions, and lower and upper bounds on the variables. A single constraint problem which can have negative or positive constraint coefficients and any type of continuous tradeoff functions (including multi-modal, multiple-valued and staircase functions) is considered first. Limiting conditions where the optimal value of a variable may be plus or minus infinity are explicitly considered. A preprocessor procedure to transform any single constraint problem to a finite form problem (an optimal feasible solution exists with finite variable values) is developed. Optimality conditions and three algorithms are then developed for the finite form problem. For piecewise linear tradeoff functions, the preprocessor and algorithms are polynomially bounded. The preprocessor is then modified to handle bounded knapsack sharing problems with multiple constraints. An optimality condition and algorithm is developed for the multiple constraint finite form problem. For multiple constraints, the time needed for the multiple constraint finite form algorithm is the time needed to solve a single constraint finite form problem multiplied by the number of constraints. Some multiple constraint problems cannot be transformed to multiple constraint finite form problems.     

On solving simple bilevel programs with a nonconvex lower level program

In this paper, we consider a simple bilevel program where the lower level program is a nonconvex minimization problem with a convex set constraint and the upper level program has a convex set constraint. By using the value function of the lower level program, we reformulate the bilevel program as a single level optimization problem with a nonsmooth inequality constraint and a convex set constraint. To deal with such a nonsmooth and nonconvex optimization problem, we design a smoothing projected gradient algorithm for a general optimization problem with a nonsmooth inequality constraint and a convex set constraint. We show that, if the sequence of penalty parameters is bounded then any accumulation point is a stationary point of the nonsmooth optimization problem and, if the generated sequence is convergent and the extended Mangasarian-Fromovitz constraint qualification holds at the limit then the limit point is a stationary point of the nonsmooth optimization problem. We apply the smoothing projected gradient algorithm to the bilevel program if a calmness condition holds and to an approximate bilevel program otherwise. Preliminary numerical experiments show that the algorithm is efficient for solving the simple bilevel program.     

On convex quadratic programs with linear complementarity constraints

The paper shows that the global resolution of a general convex quadratic program with complementarity constraints (QPCC), possibly infeasible or unbounded, can be accomplished in finite time. The method constructs a minmax mixed integer formulation by introducing finitely many binary variables, one for each complementarity constraint. Based on the primal-dual relationship of a pair of convex quadratic programs and on a logical Benders scheme, an extreme ray/point generation procedure is developed, which relies on valid satisfiability constraints for the integer program. To improve this scheme, we propose a two-stage approach wherein the first stage solves the mixed integer quadratic program with pre-set upper bounds on the complementarity variables, and the second stage solves the program outside this bounded region by the Benders scheme. We report computational results with our method. We also investigate the addition of a penalty term y ^T Dw to the objective function, where y and w are the complementary variables and D is a nonnegative diagonal matrix. The matrix D can be chosen effectively by solving a semidefinite program, ensuring that the objective function remains convex. The addition of the penalty term can often reduce the overall runtime by at least 50 %. We report preliminary computational testing on a QP relaxation method which can be used to obtain better lower bounds from infeasible points; this method could be incorporated into a branching scheme. By combining the penalty method and the QP relaxation method, more than 90 % of the gap can be closed for some QPCC problems.     

Convex programming with single separable constraint and bounded variables

In this paper a minimization problem with convex objective function subject to a separable convex inequality constraint “≤” and bounded variables (box constraints) is considered. We propose an iterative algorithm for solving this problem based on line search and convergence of this algorithm is proved. At each iteration, a separable convex programming problem with the same constraint set is solved using Karush-Kuhn-Tucker conditions. Convex minimization problems subject to linear equality/ linear inequality “≥” constraint and bounds on the variables are also considered. Numerical illustration is included in support of theory.     

Constraint qualifications for constrained Lipschitz optimization problems and applications to a MPCC

Three constraint qualifications (the weak generalized Robinson constraint qualification, the bounded constraint qualification, and the generalized Abadie constraint qualification), which are weaker than the generalized Robinson constraint qualification (GRCQ) given by Yen (1997) [1], are introduced for constrained Lipschitz optimization problems. Relationships between those constraint qualifications and the calmness of the solution mapping are investigated. It is demonstrated that the weak generalized Robinson constraint qualification and the bounded constraint qualification are easily verifiable sufficient conditions for the calmness of the solution mapping, whereas the proposed generalized Abadie constraint qualification, described in terms of graphical derivatives in variational analysis, is weaker than the calmness of the solution mapping. Finally, those constraint qualifications are written for a mathematical program with complementarity constraints (MPCC), and new constraint qualifications ensuring the C-stationary point condition of a MPCC are obtained.     

An O(n) algorithm for quadratic knapsack problems

An algorithm is presented which solves bounded quadratic optimization problems with n variables and one linear constraint in at most O(n) steps. The algorithm is based on a parametric approach combined with well-known ideas for constructing efficient algorithms. It improves an O(n log n) algorithm which has been developed for a more restricted case of the problem.     

Bilevel multiplicative problems: A penalty approach to optimality and a cutting plane based algorithm

Bilevel programming has been proposed for dealing with decision processes involving two decision makers with a hierarchical structure. They are characterised by the existence of two optimisation problems in which the constraint region of the upper level problem is implicitly determined by the lower level optimisation problem. In this paper we focus on the class of bilevel problems in which the upper level objective function is linear multiplicative, the lower level one is linear and the common constraint region is a bounded polyhedron. After replacing the lower level problem by its Karush–Kuhn–Tucker conditions, the existence of an extreme point which solves the problem is proved by using a penalty function approach. Besides, an algorithm based on the successive introduction of valid cutting planes is developed obtaining a global optimal solution. Finally, we generalise the problem by including upper level constraints which involve both level variables.