凸二次规划的预估校正光滑算法

本文研究了凸二次规划的一种光滑算法,将规划的对应中心线条件改造成一个非线性方程组,对其应用牛顿法及其变形形式,并且证明了算法的全局收敛性.     

一个解凸二次规划的预测-校正光滑化方法

本文为凸二次规划问题提出一个光滑型方法,它是Engelke和Kanzow提出的解线性规划的光滑化算法的推广。其主要思想是将二次规划的最优性K-T条件写成一个非线性非光滑方程组,并利用Newton型方法来解其光滑近似。本文的方法是预测-校正方法。在较弱的条件下,证明了算法的全局收敛性和超线性收敛性。     

非凸二次规划的粒子群算法

给出了粒子群算法中惯性权值和学习因子的一种简单改进,并将其应用到非凸二次规划的求解中,通过数值试验与现有的求解非凸二次规划问题的分支定界法进行了比较,得到了较好的结果.     

求解凸二次规划问题的势下降内点算法

1　引　言二次规划问题的求解是数学规划和工业应用等领域的一个重要课题 ,同时也是解一般非线性规划问题的序列二次规划算法的关键 .求解二次规划问题的早期技术是利用线性规划问题的单纯形方法求解二次规划问题的 KKT最优性必要条件[1 ] .这类算法比较直观 ,但在处理不等式约束时 ,松弛变量的引进很容易导致求解过程的明显减慢 .有效集策略是求解二次规划问题的另一类主要技术 .这类方法一般都是稳定的 ,但随着问题中大量不等式约束的出现 ,其收敛速度将越来越低[2 ] .简约空间技术将所求问题的 Hessian阵投影到自由变量所在的子空间中 …     

混合整数二次规划问题的全局最优性条件

本文给出了混合整数二次规划问题的全局最优性条件,包括全局最优充分性条件和全局最优必要性条件.我们还给出了一个数值实例用以说明如何利用本文所给出的全局最优性条件来判定一个给定点是否是全局最优解.     

框式约束凸二次规划问题的内点算法

In this paper,a primal-dual interior point algorithm for convex quadratic progromming problem with box constrains is presented.It can be started at any primal-dual interior feasible point.If the initial point is close to the central path,it becomes a central path-following alogorithm and requires a total of O(√nL)number of iterations,where L is the input length.     

求解中大规模复杂凸二次整数规划问题的新型分枝定界算法

针对现有分枝定界算法在求解高维复杂二次整数规划问题时所存在的诸多不足,本文通过充分挖掘二次整数规划问题的结构特性来设计选择分枝变量与分枝方向的新方法,并将HNF算法与原问题松弛问题的求解相结合来寻求较好的初始整数可行解,由此导出可用于有效求解中大规模复杂二次整数规划问题的改进型分枝定界算法．数值试验结果表明所给算法大大改进了已有相关的分枝定界算法,并具有较好的稳定性与广泛的适用性．     

一种无约束全局优化的水平值下降算法

本文研究无约束全局优化问题,建立了一种新的水平值下降算法(Level-value Descent Method,LDM).讨论并建立了概率意义下取全局最小值的一个充分必要条件,证明了算法LDM是依概率测度收敛的.这种LDM算法是基于重点度取样(Improtance Sampling)和Markov链Monte-Carlo随机模拟实现的,并利用相对熵方法(TheCross-Entropy Method)自动更新取样密度,算例表明LDM算法具有较高的数值精度和较好的全局收敛性.     

二元二次对角逼近的逼近阶

给出了二元二次对角逼近的逼近阶     

二层随机规划逼近解的收敛性

对二层随机规划的逼近解的收敛性作了探讨,证明了当随机向量序列{ζ(k)(w)}依分布收敛于ζ(w)时,相应于ζ(k)(w)的二层随机规划问题的任何最优解序列将收敛到原问题的最优解.     

Convex Quadratic Approximation

For some applications it is desired to approximate a set of m data points in ⁿ with a convex quadratic function. Furthermore, it is required that the convex quadratic approximation underestimate all m of the data points. It is shown here how to formulate and solve this problem using a convex quadratic function with s = (n + 1)(n + 2)/2 parameters, s m, so as to minimize the approximation error in the L ¹ norm. The approximating function is q(p,x), where p ^s is the vector of parameters, and x n. The Hessian of q(p,x) with respect to x (for fixed p) is positive semi-definite, and its Hessian with respect to p (for fixed x) is shown to be positive semi-definite and of rank n. An algorithm is described for computing an optimal p* for any specified set of m data points, and computational results (for n = 4,6,10,15) are presented showing that the optimal q(p*,x) can be obtained efficiently. It is shown that the approximation will usually interpolate s of the m data points.     

An Approximation Approach to Non-strictly Convex Quadratic Semi-infinite Programming

We present in this paper a numerical method for solving non-strictly-convex quadratic semi-infinite programming including linear semi-infinite programming. The proposed method transforms the problem into a series of strictly convex quadratic semi-infinite programming problems. Several convergence results and a numerical experiment are given.     

An Approximation Approach to Non-strictly Convex Quadratic Semi-infinite Programming

We present in this paper a numerical method for solving non-strictly-convex quadratic semi-infinite programming including linear semi-infinite programming. The proposed method transforms the problem into a series of strictly convex quadratic semi-infinite programming problems. Several convergence results and a numerical experiment are given.     

Some Randomized Algorithms for Convex Quadratic Programming

We adapt some randomized algorithms of Clarkson [3] for linear programming to the framework of so-called LP-type problems, which was introduced by Sharir and Welzl [10]. This framework is quite general and allows a unified and elegant presentation and analysis. We also show that LP-type problems include minimization of a convex quadratic function subject to convex quadratic constraints as a special case, for which the algorithms can be implemented efficiently, if only linear constraints are present. We show that the expected running times depend only linearly on the number of constraints, and illustrate this by some numerical results. Even though the framework of LP-type problems may appear rather abstract at first, application of the methods considered in this paper to a given problem of that type is easy and efficient. Moreover, our proofs are in fact rather simple, since many technical details of more explicit problem representations are handled in a uniform manner by our approach. In particular, we do not assume boundedness of the feasible set as required in related methods. Accepted 7 May 1997     

Separable Relaxation for Nonconvex Quadratic Integer Programming: Integer Diagonalization Approach

We present in this paper an integer diagonalization approach for deriving new lower bounds for general quadratic integer programming problems. More specifically, we introduce a semiunimodular transformation in order to diagonalize a symmetric matrix and preserve integral property of the feasible set at the same time. Via the semiunimodular transformation, the resulting separable quadratic integer program is a relaxation of the nonseparable quadratic integer program. We further define the integer diagonalization dual problem to identify the best semiunimodular transformation and analyze some basic properties of the set of semiunimodular transformations for a rational symmetric matrix. In particular, we present a complete characterization of the set of all semiunimodular transformations for a nonsingular 2×2 symmetric matrix. We finally discuss Lagrangian relaxation and convex relaxation schemes for the resulting separable quadratic integer programming problem and compare the tightness of different relaxation schemes.     

一类凸二次规划的新算法

本文给出了求解一类凸二次规划问题的新算法.这种算法既保留了传统算法的优点,又避免了其它算法中出现的添加人工变量过多、循环等问题.算例表明,这种算法是简便而有效的.     

Reconstructing Convex Matrices by Integer Programming Approaches

We consider the problem of reconstructing two-dimensional convex binary matrices from their row and column sums with adjacent ones. Instead of requiring the ones to occur consecutively in each row and column, we maximize the number of adjacent ones. We reformulate the problem by using integer programming and we develop approximate solutions based on linearization and convexification techniques.     

Duality Gaps in Stochastic Integer Programming

In this note, we explore the implications of a result that suggests that the duality gap caused by a Lagrangian relaxation of the nonanticipativity constraints in a stochastic mixed integer (binary) program diminishes as the number of scenarios increases. By way of an example, we illustrate that this is not the case. In general, the duality gap remains bounded away from zero.