对等式约束非线性规划问题的Hestenes-Powell增广拉格朗日函数的进一步研究

本文对用无约束极小化方法求解等式约束非线性规划问题的Hestenes-Powell 增广拉格朗日函数作了进一步研究．在适当的条件下,我们建立了Hestenes-Powell增广拉格朗日函数在原问题变量空间上的无约束极小与原约束问题的解之间的关系,并且也给出了Hestenes-Powell增广拉格朗日函数在原问题变量和乘子变量的积空间上的无约束极小与原约束问题的解之间的一个关系．因此,从理论的观点来看,原约束问题的解和对应的拉格朗日乘子值不仅可以用众所周知的乘子法求得,而且可以通过对Hestenes-Powell 增广拉格朗日函数在原问题变量和乘子变量的积空间上执行一个单一的无约束极小化来获得．     

带有不等式约束的非线性规划问题的一个精确增广Lagrange函数

对求解带有不等式约束的非线性非凸规划问题的一个精确增广Lagrange函数进行了研究．在适当的假设下,给出了原约束问题的局部极小点与增广Lagrange函数,在原问题变量空间上的无约束局部极小点之间的对应关系．进一步地,在对全局解的一定假设下,还提供了原约束问题的全局最优解与增广Lagrange函数,在原问题变量空间的一个紧子集上的全局最优解之间的一些对应关系．因此,从理论上讲,采用该文给出的增广Lagrange函数作为辅助函数的乘子法,可以求得不等式约束非线性规划问题的最优解和对应的Lagrange乘子．     

一个修正的罚函数方法

本文通过给出的一个修正的罚函数,把约束非线性规划问题转化为无约束非线性规划问题.我们讨论了原问题与相应的罚问题局部最优解和全局最优解之间的关系,并给出了乘子参数和罚参数与迭代点之间的关系,最后给出了一个简单算法,数值试验表明算法是有效的.     

解约束优化的分段线性有理NCP函数

本文给出新的NCP函数,这些函数是分段线性有理正则伪光滑的,且具有良好的性质.把这些NCP函数应用到解非线性优化问题的方法中.例如,把求解非线性约束优化问题的KKT点问题分别用QP-free方法,乘子法转化为解半光滑方程组或无约束优化问题.然后再考虑用非精确牛顿法或者拟牛顿法来解决该半光滑方程组或无约束优化问题.这个方法是可实现的,且具有全局收敛性.可以证明在一定假设条件下,该算法具有局部超线性收敛性.     

无约束非线性l_p问题的调节熵方法

本文构造了求解无约束非线性lp问题的新方法——调节熵函数法。给出了数值算法,证明了算法的收敛性。通过数值仿真将该方法与求解无约束非线性lp问题的极大熵函数法进行了比较,表明该算法是十分有效的。     

用罚函数法解约束非线性规划——梯级水电站有功功率最优分配问题

<正> 在电力系统中,利用电子计算机实现分级控制生产调度管理综合自动化,对促进国民经济的高速发展,对社会主义经济建设和国防建设都有现实意义. 梯级水电站的经济调度问题,属于系统工程的大系统理论关于电力系统综合自动化问题.研究电力系统经济运行问题,实际上包括开机最优组合及负荷最优分配两个方面,负荷最优分配应在开机组合的基础上进行.而梯级水电站的经济运行问题更要复杂得     

模糊数学和运筹学方法在水火电力系统经济调度中的应用

本文对一个包括有梯级水电站在内的水火电力系统建立了最优开机组合和有功功率分配的数学模型,导出了与其等价的整数线性规划模型,然后给出一种有效算法。采用大系统分解协调法将电厂进行两级分解,改进了修正水火电厂出力的Lagrange松弛方法,并采用将模糊数学和运筹学方法结合起来求解梯级水电站经济调度问题。计算表明,日耗煤率有相当的下降,可获较大经济效益。本文提出的数学模型和最优化算法甚易推广应用于其它大区电力系统经济调度问题。     

求解MINLP的几类罚函数方法的分析和探讨

针对混合整数非线性约束优化问题(MINLP)的一般形式,通过罚函数的方法,给出了它的几种等价形式,并证明了最优解的等价性.将约束优化问题转化成更容易求解的无约束非线性优化问题,并把混合整数规划转化成非整数优化问题,从而将MINLP的求解简化为求解一个连续的无约束非线性优化问题,进而可用已有的一般无约束优化算法进行求解.     

一类求行波解的线性方法

基于齐次平衡法和李志斌的 tanh函数法 ,本文得到一类简单有效的求解非线性发展方程的线性方法 .这类方法利用非线性发展方程孤立波的局部性特点 ,适当地选取函数 f 和 g,将孤波表示为 f,g的多项式 ,从而将非线性发展方程求解问题转化为非线性代数方程组的求解问题 ,再利用吴消元法求解方程组从而得到非线性发展方程的行波解     

鲁棒稀疏重构问题的凝聚同伦算法

鲁棒稀疏重构问题是信号处理领域的重要问题,该问题的数学本质是一个NP难的数学优化问题.同伦算法是一类典型的路径跟踪算法,该算法是解非线性问题的一类成熟算法,具有全局收敛性,且易于并行实现.本文考虑同伦算法在鲁棒稀疏重构问题中的数值求解.基于l_∞范数及罚函数策略,我们首先将原始的基于l_0范数的最优化模型,转化为含参数的无约束极大极小值问题,进而构造凝聚函数光滑化模型中的极大值函数,并构造凝聚同伦算法数值求解.数值仿真实验验证了新方法的有效性,为大规模鲁棒重构问题的并行化数值求解奠定基础.     

Algorithms for Unconstrained Optimization Problems via Control Theory

Existing algorithms for solving unconstrained optimization problems are generally only optimal in the short term. It is desirable to have algorithms which are long-term optimal. To achieve this, the problem of computing the minimum point of an unconstrained function is formulated as a sequence of optimal control problems. Some qualitative results are obtained from the optimal control analysis. These qualitative results are then used to construct a theoretical iterative method and a new continuous-time method for computing the minimum point of a nonlinear unconstrained function. New iterative algorithms which approximate the theoretical iterative method and the proposed continuous-time method are then established. For convergence analysis, it is useful to note that the numerical solution of an unconstrained optimization problem is none other than an inverse Lyapunov function problem. Convergence conditions for the proposed continuous-time method and iterative algorithms are established by using the Lyapunov function theorem.     

主观几何中一组余弦定律方程的解*

本文讨论在主观几何应用例子[1]中出现的由余弦定理建立的一组六元二次带根式的代数方程的解.应用隐函数存在定理,本文证明这组方程存在有唯一的实解.把求解问题转化为无约束非线性优化问题,可以用已知的诸法来求解.文中给出了用下降法求解的数值例子.     

隐互补问题的极小化变形及其稳定点

1 引言 互补问题在最优化中有着广泛的应用,例如线性规划中的对偶问题,非线性规划中求稳定点的KKT条件以及变分不等式的求解都可以转化为互补问题,另外,某些均衡网络设计问题、信号最优化问题以及交通配置等问题也可利用互补问题来求解.     

Fractional-Order Legendre Functions and Their Application to Solve Fractional Optimal Control of Systems Described by Integro-differential Equations

In this paper, we introduce a set of functions called fractional-order Legendre functions (FLFs) to obtain the numerical solution of optimal control problems subject to the linear and nonlinear fractional integro-differential equations. We consider the properties of these functions to construct the operational matrix of the fractional integration. Also, we achieved a general formulation for operational matrix of multiplication of these functions to solve the nonlinear problems for the first time. Then by using these matrices the mentioned fractional optimal control problem is reduced to a system of algebraic equations. In fact the functions of the problem are approximated by fractional-order Legendre functions with unknown coefficients in the constraint equations, performance index and conditions. Thus, a fractional optimal control problem converts to an optimization problem, which can then be solved numerically. The convergence of the method is discussed and finally, some numerical examples are presented to show the efficiency and accuracy of the method.     

Optimal control for lithium-ion batteries

Modelling, simulation and optimal control for a lithium-ion battery cell is discussed. The model involves ionic concentrations, currents and potentials in the electrodes and the separator together with the battery temperature as state variables. The resulting system is a nonlinear PDAE system with 10 partial, 1 ordinary differential and 4 algebraic equations involving the Butler-Volmer kinetics for describing the interaction of ionic currents and potentials. Time-optimal charging of the battery subject to age-preventing leads to a state-constrained optimal control problem which is solved in two ways. A first-discretize-then-optimize approach leads to a high-dimensional nonlinear optimization problem which is solved by an efficient solver. As an alternative, a feedback control law along an active arc of the state constraint of order 1 is derived to formulate and solve the corresponding so-called induced optimization problem. (© 2015 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

An exact penalty function method for nonlinear mixed discrete programming problems

In this paper, we consider a general class of nonlinear mixed discrete programming problems. By introducing continuous variables to replace the discrete variables, the problem is first transformed into an equivalent nonlinear continuous optimization problem subject to original constraints and additional linear and quadratic constraints. Then, an exact penalty function is employed to construct a sequence of unconstrained optimization problems, each of which can be solved effectively by unconstrained optimization techniques, such as conjugate gradient or quasi-Newton methods. It is shown that any local optimal solution of the unconstrained optimization problem is a local optimal solution of the transformed nonlinear constrained continuous optimization problem when the penalty parameter is sufficiently large. Numerical experiments are carried out to test the efficiency of the proposed method.     

波纹壳的格林函数方法

应用轴对称旋转扁壳的基本方程,研究了在任意载荷作用下具有型面锥度的浅波纹壳的非线性弯曲问题·　采用格林函数方法,将扁壳的非线性微分方程组化为非线性积分方程组·　再使用展开法求出格林函数,即将格林函数展成特征函数的级数形式,积分方程就成为具有退化核的形式,从而容易得到非线性代数方程组·　应用牛顿法求解非线性代数方程组时,为了保证迭代的收敛性,选取位移作为控制参数,逐步增加位移,求得相应的载荷·　在算例中,研究了具有球面度的浅波纹壳的弹性特征·　结果表明,由于型面锥度的引入,特征曲线发生显著变化,随着荷载的增加,将出现类似扁球壳的总体失稳现象·　本文的解答符合实验结果·     

Local Minimum Principle for Optimal Control Problems Subject to Differential-Algebraic Equations of Index Two

Necessary conditions in terms of a local minimum principle are derived for optimal control problems subject to index-2 differential-algebraic equations, pure state constraints, and mixed control-state constraints. Differential-algebraic equations are composite systems of differential equations and algebraic equations, which arise frequently in practical applications. The local minimum principle is based on the necessary optimality conditions for general infinite optimization problems. The special structure of the optimal control problem under consideration is exploited and allows us to obtain more regular representations for the multipliers involved. An additional Mangasarian-Fromowitz-like constraint qualification for the optimal control problem ensures the regularity of a local minimum. An illustrative example completes the article.The author thanks the referees for careful reading and helpful suggestions and comments.     

Convergence of Algorithms in Optimization and Solutions of Nonlinear Equations

It is desirable that an algorithm in unconstrained optimization converges when the guessed initial position is anywhere in a large region containing a minimum point. Furthermore, it is useful to have a measure of the rate of convergence which can easily be computed at every point along a trajectory to a minimum point. The Lyapunov function method provides a powerful tool to study convergence of iterative equations for computing a minimum point of a nonlinear unconstrained function or a solution of a system of nonlinear equations. It is surprising that this popular and powerful tool in the study of dynamical systems is not used directly to analyze the convergence properties of algorithms in optimization. We describe the Lyapunov function method and demonstrate how it can be used to study convergence of algorithms in optimization and in solutions of nonlinear equations. We develop an index which can measure the rate of convergence at all points along a trajectory to a minimum point and not just at points in a small neighborhood of a minimum point. Furthermore this index can be computed when the calculations are being carried out.     

An exact penalty function for nonlinear programming with inequalities

It is shown how, given a nonlinear programming problem with inequality constraints, it is possible to construct an exact penalty function with a local unconstrained minimum at any local minimum of the constrained problem. The unconstrained minimum is sufficiently smooth to permit conventional optimization techniques to be used to locate it. Numerical evidence is presented on five well-known test problems.