粘塑性动力学问题的最优控制变分原理与有限元分析

本文运用最优控制变分理论,对Perzyna型粘塑性体,提出了粘塑性动力问题的参数变分原理,并给出了相应的动力有限元方程和参数二次解法.     

与0—1二次规划问题等价的连续问题

在较一般的条件下，证明了线性约束０－１二次规划问题等价于一个凹二次规划问题，改进了已有的结果。     

Ti-6242S钛合金高温循环粘塑性行为有限元模拟

采用ANSYS提供的粘塑性流动准则下的双线性各向同性硬化本构模型,考虑材料参数的温度相关性,通过单个单元有限模型对4种温度下施加相同平均应力和不同应力幅值的应力循环工况进行有限元模拟。实验与模拟结果的对比表明,该模型能够较好的地模拟高温450℃以下Ti-6242S钛合金粘塑性变形的循环累积。此外,由于该模型没有考虑循环过程中背应力的演化,对滞回环的预测不够理想,且过高预测了520℃下的粘塑性累积变形。     

一种新的二次约束二次规划问题的分支定界算法

本文为了获得二次约束二次规划(QCQP)问题的全局最优解,提出一种新的参数化线性松弛分支定界算法.该算法利用参数化线性松弛技术,得到(QCQP)的全局最小值的下界,并利用区域缩减技术以最大限度地删除不可行区域,加快该算法的收敛速度.数值实验表明,本文提出的算法是有效并且可行的.     

大规模严格凸二次规划问题一个新算法

根据广义乘子法的思想,将具有等式约束和非负约束的凸二次规划问题转化只有非负约束的简单凸二次规划,通过简单凸二次规划来得到解等式约束一非负约束的凸二次规划新算法,新算法不用求逆矩阵,这样可充分保持矩阵的稀疏性,用来解大规模稀疏问题,数值结果表明：在微机４８６／３３上就能解较大规模的凸二次规划。     

带球（椭球）约束的不定二次规划问题

本文证明了带球（椭球）约束的不定二次规划问题具有强Ｌａｇｒａｎｇｅ对偶性，设计了一个求解这类问题的算法，本语文的结论比文「７」强，所设计的算法比文「７」简洁。     

混凝土材料的粘塑性损伤本构模型

基于不可逆热力学,引入运动硬化、等向硬化和损伤内变量,构造了相应的自由能函数和流动势函数,推导出了混凝土材料的粘塑性损伤本构模型．数值模拟的结果表明,该模型能够避开屈服面和破坏准则的基本假设来描述混凝土材料的以下特性:压缩载荷作用下的体积膨胀现象;应变率敏感性;峰值后由损伤和破坏引起的应力软化和刚度退化现象A·D2由于此模型避开了根据各种变形阶段选择与其相应的本构模型的繁琐计算,因此更便于纳入复杂工况下应力分析有限元程序中．     

与0-1二次规划问题等价的连续问题(英)

在较一般的条件下，证明了线性约束0-1二次规划问题等价于一个凹二次规划问题，改进了已有的结果．     

Perzyna粘塑性模型的参变量变分原理*

Perzyna模型是粘塑性本构关系的主要形式之一,本文给出该模型的参变量变分原理,该原理将原问题化为求解带约束条件的泛函极值,其约束条件就是由粘塑性本构关系推导出的系统状态方程,所讨论的问题其塑性流动不受Drucker假定的限制,文中给出原理的证明,并研究弹塑性蠕变问题.     

一类0-1二次规划最优解的新算法

从矩阵的基础知识出发,给出了当目标函数矩阵是严格对角占优阵时,快速地获得0-1二次规划最优解的一个新算法;该方法具有很强的实用性,是此类问题的一个高效求解算法.     

An O(n 2) Active Set Algorithm for Solving Two Related Box Constrained Parametric Quadratic Programs

Recently, O(n ²) active set methods have been presented for minimizing the parametric quadratic functions (1/2)x Dx–a x+| x–c| and (1/2)x Dx–a x+(/2)( x–c)², respectively, subject to lxb, for all nonnegative values of the parameter . Here, D is a positive diagonal n×n matrix, and a are arbitrary n-vectors, c is an arbitrary scalar; l and b are arbitrary n-vectors such that lb. In this paper, we show that each one of these algorithms may be used to simultaneously solve both parametric programs withno additional computational cost.     

Redundancies in Positive-Semidefinite Quadratic Programming

The question investigated is how to detect nonactive restrictions in positive-semidefinite quadratic programming. If the optimization problem satisfies some regularity conditions, we can use parametric optimization techniques for that analysis. It turns out that results obtained in Ref. 1, where only positive-definite matrices are considered, can be generalized to the semidefinite case. Simple calculations based exclusively on the problem data allow one to delete superfluous restrictions for this problem class during an optimization procedure.     

参数凸二次规划的线性稳定性

本文研究参数凸二次规划的最优解集的稳定性。首先给出参数数学规划的方向线性稳定的定义，然后利用集值映射的微分理论证明线性约束参数凸二次规划是线性稳定的。     

Duality Bound Method for the General Quadratic Programming Problem with Quadratic Constraints

The purpose of this article is to develop a branch-and-bound algorithm using duality bounds for the general quadratically-constrained quadratic programming problem and having the following properties: (i) duality bounds are computed by solving ordinary linear programs; (ii) they are at least as good as the lower bounds obtained by solving relaxed problems, in which each nonconvex function is replaced by its convex envelope; (iii) standard convergence properties of branch-and-bound algorithms for nonconvex global optimization problems are guaranteed. Numerical results of preliminary computational experiments for the case of one quadratic constraint are reported.     

球约束凸二次规划的一个新算法

首先利用Lagrange对偶 ,将球约束凸二次规划问题转化为无约束优化问题 ,然后运用单纯形法求解无约束优化问题 ,从而获得原问题的最优解     

A Semidefinite Programming Heuristic for Quadratic Programming Problems with Complementarity Constraints

The presence of complementarity constraints brings a combinatorial flavour to an optimization problem. A quadratic programming problem with complementarity constraints can be relaxed to give a semidefinite programming problem. The solution to this relaxation can be used to generate feasible solutions to the complementarity constraints. A quadratic programming problem is solved for each of these feasible solutions and the best resulting solution provides an estimate for the optimal solution to the quadratic program with complementarity constraints. Computational testing of such an approach is described for a problem arising in portfolio optimization.Research supported in part by the National Science Foundations VIGRE Program (Grant DMS-9983646).Research partially supported by NSF Grant number CCR-9901822.     

An Exterior Newton Method for Strictly Convex Quadratic Programming

We propose an exterior Newton method for strictly convex quadratic programming (QP) problems. This method is based on a dual formulation: a sequence of points is generated which monotonically decreases the dual objective function. We show that the generated sequence converges globally and quadratically to the solution (if the QP is feasible and certain nondegeneracy assumptions are satisfied). Measures for detecting infeasibility are provided. The major computation in each iteration is to solve a KKT-like system. Therefore, given an effective symmetric sparse linear solver, the proposed method is suitable for large sparse problems. Preliminary numerical results are reported.     

Sequential Quadratic Programming Methods for Large-Scale Problems

Sequential quadratic (SQP) programming methodsare the method of choice when solving small or medium-sized problems. Sincethey are complex methods they are difficult (but not impossible) to adapt tosolve large-scale problems. We start by discussing the difficulties that needto be addressed and then describe some general ideas that may be used toresolve these difficulties. A number of SQP codes have been written to solve specific applications and there is a general purposed SQP code called SNOPT,which is intended for general applications of a particular type. These aredescribed briefly together with the ideas on which they are based. Finally wediscuss new work on developing SQP methods using explicit second derivatives.