Modified Augmented Lagrange Multiplier Methods for Large-Scale Chemical Process Optimization

Chemical process optimization can be described as large-scale nonlinear constrained minimization. The modified augmented Lagrange multiplier methods (MALMM) for large-scale nonlinear constrained minimization are studied in this paper. The Lagrange function contains the penalty terms on equality and inequality constraints and the methods can be applied to solve a series of bound constrained sub-problems instead of a series of unconstrained sub-problems. The steps of the methods are examined in full detail. Numerical experiments are made for a variety of problems, from small to very large-scale, which show the stability and effectiveness of the methods in large-scale problems.

Accelerated Matrix Recovery via Random Projection Based on Inexact Augmented Lagrange Multiplier Method

In this paper, a unified matrix recovery model was proposed for diverse corrupted matrices. Resulting from the separable structure of the proposed model, the convex optimization problem can be solved efficiently by adopting an inexact augmented Lagrange multiplier (IALM) method. Additionally, a random projection accelerated technique (IALM+RP) was adopted to improve the success rate. From the preliminary numerical comparisons, it was indicated that for the standard robust principal component analysis (PCA) problem, IALM+RP was at least two to six times faster than IALM with an insignificant reduction in accuracy; and for the outlier pursuit (OP) problem, IALM+RP was at least 6.9 times faster, even up to 8.3 times faster when the size of matrix was 2 000×2 000.

Modified Lagrange Multiplier Method and Generalized Variational Principle in Fluid Mechanics

The Lagrange multiplier method plays an important role in establishing generalized variational principles notonly in tluid mechallics. but also in elasticity. Sometimes, however, one may come across variational crisis(somemultipliers vanish identically). failing to achieve his aim. The crisis is caused by the fact that the Inultipliers are treatedas independent variables in the process of variatioll. but after identification they become functions of the originalindependent variables. To overcome it, a Inodified Lagrange multiplier method or semi-inverse method has beenproposed to deduce generalized varistional principles. Some e-camples are given to illustrate its convenience andeffectiveness of the novel method.

Distributed Lagrange Multiplier/Fictitious Domain Finite Element Method for a Transient Stokes Interface Problem with Jump Coefficients

The distributed Lagrange multiplier/fictitious domain(DLM/FD)-mixed finite element method is developed and analyzed in this paper for a transient Stokes interface problem with jump coefficients.The semi-and fully discrete DLM/FD-mixed finite element scheme are developed for the first time for this problem with a moving interface,where the arbitrary Lagrangian-Eulerian(ALE)technique is employed to deal with the moving and immersed subdomain.Stability and optimal convergence properties are obtained for both schemes.Numerical experiments are carried out for different scenarios of jump coefficients,and all theoretical results are validated.

A Parameter-Free Approach to Determine the Lagrange Multiplier in the Level Set Method by Using the BESO

A parameter-free approach is proposed to determine the Lagrange multiplier for the constraint of material volume in the level set method.It is inspired by the procedure of determining the threshold of sensitivity number in the BESO method.It first computes the difference between the volume of current design and the upper bound of volume.Then,the Lagrange multiplier is regarded as the threshold of sensitivity number to remove the redundant material.Numerical examples proved that this approach is effective to constrain the volume.More importantly,there is no parameter in the proposed approach,which makes it convenient to use.In addition,the convergence is stable,and there is no big oscillation.

Fully Coupled Fluid-Structure Interaction Model Based on Distributed Lagrange Multiplier/Fictitious Domain Method

This paper, with a finite element method, studies the interaction of a coupled incompressible fluid-rigid structure system with a free surface subjected to external wave excitations. With this fully coupled model, the rigid structure is taken as ＂fictitious＂ fluid with zero strain rate. Both fluid and structure are described by velocity and pressure. The whole domain, including fluid region and structure region, is modeled by the incompressible Navier-Stokes equations which are discretized with fixed Eulerian mesh. However, to keep the structure＇ s rigid body shape and behavior, a rigid body constraint is enforced on the ＂fictitious＂ fluid domain by use of the Distributed Lagrange Multipher/Fictitious Domain （DLM/ FD） method which is originally introduced to solve particulate flow problems by Glowinski et al. For the verification of the model presented herein, a 2D numerical wave tank is established to simulate small amplitude wave propagations, and then numerical results are compared with analytical solutions. Finally, a 2D example of fluid-structure interaction under wave dynamic forces provides convincing evidences for the method excellent solution quality and fidelity.

Optimizing Forest Sampling by Using Lagrange Multipliers

In two-phase sampling, or double sampling, from a population with size N we take one, relatively large, sample size n. From this relatively large sample we take a small sub-sample size m, which usually costs more per sample unit than the first one. In double sampling with regression estimators, the sample of the first phase n is used for the estimation of the average of an auxiliary variable X, which should be strongly related to the main variable Y (which is estimated from the sub-sample m). Sampling optimization can be achieved by minimizing cost C with fixed var Y, or by finding a minimum var Y for fixed C. In this paper we optimize sampling with use of Lagrange multipliers, either by minimizing variance of Y and having predetermined cost, or by minimizing cost and having predetermined variance of Y.

The Mortar Element Method with Lagrange Multipliers for Stokes Problem

In this paper,we propose a mortar element method with Lagrange multiplier for incompressible Stokes problem,i.e.,the matching constraints of velocity on mortar edges are expressed in terms of Lagrange multipliers.We also present P_1 noncon- forming element attached to the subdomains.By proving inf-sup condition,we derive optimal error estimates for velocity and pressure.Moreover,we obtain satisfactory approximation for normal derivatives of the velocity across the interfaces.

A Modified Lagrange Method for Solving Convex Quadratic Optimization Problems

In this paper, a modified version of the Classical Lagrange Multiplier method is developed for convex quadratic optimization problems. The method, which is evolved from the first order derivative test for optimality of the Lagrangian function with respect to the primary variables of the problem, decomposes the solution process into two independent ones, in which the primary variables are solved for independently, and then the secondary variables, which are the Lagrange multipliers, are solved for, afterward. This is an innovation that leads to solving independently two simpler systems of equations involving the primary variables only, on one hand, and the secondary ones on the other. Solutions obtained for small sized problems (as preliminary test of the method) demonstrate that the new method is generally effective in producing the required solutions.

A New Lagrangian Multiplier Method on Constrained Optimization

In this paper, a new augmented Lagrangian function with 4-piecewise linear NCP function is introduced for solving nonlinear programming problems with equality constrained and inequality constrained. It is proved that a solution of the original constrained problem and corresponding values of Lagrange multipliers can be found by solving an unconstrained minimization of the augmented Lagrange function. Meanwhile, a new Lagrangian multiplier method corresponding with new augmented Lagrangian function is proposed. And this method is implementable and convergent.

用第一类Lagrange方程求解平面多体系统约束力的方法

利用第一类Lagrange方程建立多体系统的动力学方程时,系统的约束力是与Lagrange乘子有关的函数。该文采用笛卡儿坐标和建立约束方程的局部方法,给出了一种求解多体系统约束力的方法。该方法可使固定支承面的法向约束力及铰链约束力在惯性坐标轴上的投影与Lagrange乘子一一对应,从而便于系统约束力的分析和求解,最后用算例验证了该方法的正确性。

Lagrange乘子初始值和罚因子迭代方式的研究

以Ｒｏｃｋａｆｅｌｌａｒ乘子罚函数作为基准，利用Ｍａｔｌａｂ强大的数值计算功能，通过数值试验，对Ｌａｇｒａｎｇｅ乘子初始值和罚因子迭代方式进行了研究，比较了不同的乘子初始值和罚因子迭代序列对算法效率的影响，为大规模优化算法的研究提供了有益的借鉴．

采用结构进化策略的Lagrange乘子法优化换热网络

针对罚函数法处理有约束问题时存在的不足,采用Lagrange乘子法优化换热网络。为求解Lagrange函数方程组,根据确定性方法,提出最速下降法求解策略以及Powell法求解策略。通过极小值判断机制,保证Lagrange函数方程组的解是原换热网络目标函数值的极小值。根据实际工况,提出结构进化策略,与Lagrange乘子法相结合,实现了换热网络全局最优化。通过经典算例验证了两种求解策略的有效性、准确性以及结构进化策略的通用性。与文献结果进行对比,结果表明本算法具有较强的局部搜索能力以及全局搜索能力,能够找到更优的换热网络结构,有利于在工业生产中节约成本。

拟可微约束优化的次线性Lagrange乘子法则

约束拟可微优化的Lagrange乘子型最优性条件,往往与某些特殊对象(超梯度,方向)的选取有关,这是拟可微优化的核心问题之一.应用凸紧集与次线性函数的Minkowski对偶,利用次线性泛函产生的非线性Lagrange函数,对于具有有限个等式和不等式约束的拟可微优化,给出了一个与特殊对象选取无关的次线性的Lagrange乘子法则,推广了已有的结果.

用于分布式目标最优极化求解的Lagrange乘因子法优化

目前拉格朗日乘因子法是求解分布式目标最优极化的主要算法。该算法需要计算一个以拉格朗日乘法因子为自变量的六次多项式方程。针对拉格朗日乘因子法在计算该多项式方程根时存在的问题,提出了一种优化求解法。在理论证明该方程最大根对应天线最大接收功率而最小根对应天线最小接收功率的基础上,该优化求解法通过缩小迭代搜索区间获取方程最大、最小根,然后利用这些根与天线极化之间的关系式求解目标最优极化。为了提高迭代收敛速度,通过理论分析确定了最小初始迭代搜索区间。实验结果表明,该优化求解法消除了算法对于拉格朗日乘因子初始值的依赖,提高了算法的运算速度。

改进PSO算法和Lagrange乘数法应用于短期发电计划

电力系统短期发电计划研究是一个离散、复杂、多维的非线性整数规划问题,求解非常困难。采用改进的粒子群(particle swarm optimization,PSO)算法通过线性改变权重因子,连续变量离散化,以及增加第二最优项用于求解最优机组组合问题;拉格朗日乘数法适合于多维函数在约束条件下的求解极值问题,用于求解各机组在各时段的经济出力。方法的可行性通过10机系统中检验。仿真结果表明,该方法能够求得高质量解,减少机组运行费用,具有有效性和可行性。

Lagrange乘数法的几何直观推导

从几何上,直观地介绍求解一类条件极值问题的Lagrange乘数法,显得很形象、易于理解。另外,用Lagrange乘数法求出的解不一定是条件极值问题的极小值解。利用二阶导数给出了用Lagrange乘数法求出的解是条件极值问题的极小值解的一个充分条件。用该条件判别,比用已有的方法判别简单易行。

一种Lagrange乘数法及其推广的新证明

针对Lagrange乘数法传统证明方法过于繁琐的问题,利用向量及其相关性质,讨论了Lagrange乘数法一种新的证明方法。分别研究了二元函数、三元函数以及n元函数Lagrange乘数法的向量形式证明,这种以向量为工具研究等式约束的Lagrange乘数法,解决了传统方法难以迁移且结论模糊的问题。

基于Lagrange的H.264率失真编码优化算法

为了进一步提高编码速度,促进视频编码技术在多媒体通信领域的实际应用,本文对率失真视频编码的关键算法进行了深入的研究并提出了其优化方法,并从信息论理论出发,着重围绕图像压缩的理论,利用率失真特征数学模型、拉格朗日乘子法,对目前新一代的H.264视频标准进行编码算法模式选择,试图从最根本的理论角度探讨信源编码,以便得到更高的压缩比、更好的图像质量和更快的编码速度。最后,利用本文提出的编码模式优化算法,对H.264的J M61e(JUT Test Model)系统测试模型的性能进行全面测试。

改进Lagrange乘子法及收敛性分析

将与不等式约束相关的乘子重新定义为原乘子的正定函数,则Karush-Kuhn-Tucker必要条件中关于不等式约束乘子的非负约束可以去掉,并能构造出直接处理不等式约束的Lagrange乘子法.分析了算法的收敛性,利用LaSalle不变集原理揭示其稳定机制,并讨论如何减弱收敛条件和扩大收敛域.