基于遗传算法与牛顿法的联合算法在地表发射率反演中的应用

结合遗传算法全局高效搜索和牛顿法局部细致搜索的优势,充分利用一种算法的优点弥补另一种算法的不足,进而引入一种基于遗传算法和牛顿法的联合算法,并将联合算法应用于反演地表发射率的函数关系中.结果表明,联合算法中由遗传算法提供的初始值使得牛顿法下降的速度快,且很快趋于稳定,达到精度要求;而由任意初始值提供给牛顿法,目标函数下降到一定阶段后反而有所回升,然后才保持稳定,且经和联合算法迭代相同的次数后,目标函数的值仍然非常大,远远达不到要求.因此,从可行性、计算效率上看,联合算法均优于单纯的牛顿法,是一种性能稳定,计算高效的下降方法.     

非线性算子方程的泰勒展式算法

本文的目的是给出一种解Ｈｉｌｂｅｒｔ空间中非线性方程的ｋ阶泰勒展式算法（ｋ１）．标准Ｇａｌｅｒｋｉｎ方法可以看作１阶泰勒展式算法，而最优非线性Ｇａｌｅｒｋｉｎ方法可视为２阶泰勒展式算法．我们应用这种算法于定常的Ｎａｖｉｅｒ－Ｓｔｏｋｅｓ方程的数值逼近．在一定情景下，最优非线性Ｇａｌｅｒｋｉｎ方法提供比标准Ｇａｌｅｒｋｉｎ方法和非线性Ｇａｌｅｒｋｉｎ方法更高阶的收敛速度．     

An Arnoldi-Extrapolation algorithm for computing PageRank

The Arnoldi-type algorithm proposed by Golub and Greif [G. Golub, C. Greif, An Arnoldi-type algorithm for computing PageRank, BIT 46 (2006) 759-771] is a restarted Krylov subspace method for computing PageRank. However, this algorithm may not be efficient when the damping factor is high and the dimension of the search subspace is small. In this paper, we first develop an extrapolation method based on Ritz values. We then consider how to periodically knit this extrapolation method together with the Arnoldi-type algorithm. The resulting algorithm is the Arnoldi-Extrapolation algorithm. The convergence of the new algorithm is analyzed. Numerical experiments demonstrate the numerical behavior of this algorithm.     

一维流动的Boussinesq方程渗流系数反演的变分伴随方法研究

利用偏微分方程最优控制中的伴随方法讨论一维Boussinesq方程渗流系数反演问题的数值解法.吸收正则化思想改造最小二乘方法,利用变分伴随思想构造新迭代算法.迭代过程中首次搜索方向采用泛函下降最快的负梯度方向,第二次及以后搜索方向采用一种新的全局收敛的下降算法(Pan-Chen算法).与共轭梯度法比较,新算法具有更好的收敛性.数值模拟结果验证了理论算法的可靠性.     

Application of the dual active set algorithm to quadratic network optimization

A new algorithm, the dual active set algorithm, is presented for solving a minimization problem with equality constraints and bounds on the variables. The algorithm identifies the active bound constraints by maximizing an unconstrained dual function in a finite number of iterations. Convergence of the method is established, and it is applied to convex quadratic programming. In its implementable form, the algorithm is combined with the proximal point method. A computational study of large-scale quadratic network problems compares the algorithm to a coordinate ascent method and to conjugate gradient methods for the dual problem. This study shows that combining the new algorithm with the nonlinear conjugate gradient method is particularly effective on difficult network problems from the literature.     

置换流水车间调度问题的中心引力优化算法求解

目前求解置换流水车间调度问题的智能优化算法都是随机型优化方法,存在的一个问题是解的稳定性较差。针对该问题,本文给出一种确定型智能优化算法——中心引力优化算法的求解方法。为处理基本中心引力优化算法对初始解选择要求高的问题,利用低偏差序列生成初始解,提高初始解质量;利用加速度和位置迭代方程更新解的状态;利用两位置交换排序法进行局部搜索,提高算法的优化性能。采用置换流水车间调度问题标准测试算例进行数值实验,并和基本中心引力优化算法、NEH启发式算法、微粒群优化算法和萤火虫算法进行比较。结果表明该算法不仅具有更好的解的稳定性,而且具有更高的计算精度,为置换流水车间调度问题的求解提供了一种可行有效的方法。     

服从OldroydB型微分模型的粘弹性流体问题的数值解法

本文对服从OldroydB型微分模型的粘弹性流体问题给出了一种数值逼近算法．该算法对压力方程采用标准混合有限元方法,对速度方程采用并行非重叠区域分解方法和特征线法．这种并行算法在子区域上用Galerkin方法,通过积分平均方法显式地给出内边界的数值流．在本文最后还给出了该算法的最优L^2。一误差估计．     

A new shrinking gradient-like projection method for equilibrium problems

The paper proposes a new shrinking gradient-like projection method for solving equilibrium problems. The algorithm combines the generalized gradient-like projection method with the monotone hybrid method. Only one optimization program is solved onto the feasible set at each iteration in our algorithm without any extra-step dealing with the feasible set. The absence of an optimization problem in the algorithm is explained by constructing slightly different cutting-halfspace in the monotone hybrid method. Theorem of strong convergence is established under standard assumptions imposed on equilibrium bifunctions. An application of the proposed algorithm to multivalued variational inequality problems (MVIP) is presented. Finally, another algorithm is introduced for MVIPs in which we only use a value of main operator at the current approximation to construct the next approximation. Some preliminary numerical experiments are implemented to illustrate the convergence and computational performance of our algorithms over others.     

随机优化磨光算法对卫生陶瓷进出口的分析模型

结合磨光法和最优化理论提出一种随机优化磨光算法(SOS算法),算法通过原始值的参数化和调整幅度的修改,利用优化理论优化控制点.实例表明,随机优化磨光算法比样条修正磨光法和灰色马尔可夫链预测模型精度要高得多;而且所得到的误差变化更稳定.     

A convergent multigrid cycle for the hybridized mixed method

We consider the application of a variable V‐cycle multigrid algorithm for the hybridized mixed method for second‐order elliptic boundary‐value problems. Our algorithm differs from the previous works on multigrid for the mixed method in that it is targeted at efficiently solving the matrix system for the Lagrange multiplier of the method. Since the mixed method is best implemented by first solving for the Lagrange multiplier and recovering the remaining unknowns locally, our algorithm is more useful in practice. The critical ingredient in the algorithm is a suitable intergrid transfer operator. We design such an operator and prove mesh‐independent convergence of the variable V‐cycle algorithm. Numerical experiments indicating the asymptotically optimal performance of our algorithm, as well as the failure of certain seemingly plausible intergrid transfer operators, are presented. Copyright © 2009 John Wiley & Sons, Ltd.     

进化泛函网络多维函数逼近理论及学习算法

提出了一种用于多维函数逼近的进化策略修正泛函网络基函数系数的新算法,并给出了其算法学习过程.利用进化策略的自适应性来确定基函数前的系数,改进了泛函网络的参数通过解方程组来得到这一传统方法.仿真结果表明,这种新的逼近算法简单可行,能够逼近给定的函数到预先给定的精度,具有较快的收敛速度和良好的逼近性能.     

An approximate subgradient algorithm for unconstrained nonsmooth,nonconvex optimization

In this paper a new algorithm for minimizing locally Lipschitz functions is developed. Descent directions in this algorithm are computed by solving a system of linear inequalities. The convergence of the algorithm is proved for quasidifferentiable semismooth functions. We present the results of numerical experiments with both regular and nonregular objective functions. We also compare the proposed algorithm with two different versions of the subgradient method using the results of numerical experiments. These results demonstrate the superiority of the proposed algorithm over the subgradient method.      

关于构造数学物理中非线性发展方程组孤波解的一种新算法

根据改进的sine-cosine法和吴文俊消元法,给出了一种构造非线性发展方程组孤波解的新算法。这种算法比已知的双曲函数法有更好的结论,并且在使用的过程中更简单。借助于MATH-EMATICA软件,这一算法能够在计算机上实现。     

线性规划基线算法的基本概念

１．运算表格线性规划的基线算法是单纯形法（基点算法）的发展,因为每张运算表格对应着一条基线而得名．它象单纯形法一样好学易用,操作简便,而解题速度比单纯形法快．考虑标准型线性规划问题（ＬＰ）：：其中ｃ,ｘｅＲ"＋",Ａ是。ｘ（佩十。）矩阵,ｂｅＲ"。是（ＬＰ）的维数,。是约束个数．Ｘ＝｛ＸＥＲ""叫ＡＸ＝ｂ,Ｘ三０｝是（＊利的可行集．Ｘ是一个多面凸集．本文假定Ｃ４０．并且原点不是最优解．把Ｘ看作参数．方程组０．】Ｘ＝０,】的系数表称为母表（表１）．恒假设矩阵０－１－＼Ａｊ＼ｈｉ一"-一'-－"－"'一'-"-一"…     

A globalization scheme for the generalized Gauss-Newton method

Summary For solving an equality constrained nonlinear least squares problem, a globalization scheme for the generalized Gauss-Newton method via damping is proposed. The stepsize strategy is based on a special exact penalty function. Under natural conditions the global convergence of the algorithm is proved. Moreover, if the algorithm converges to a solution having a sufficiently small residual, the algorithm is shown to change automatically into the undamped generalized Gauss-Newton method with a fast linear rate of convergence. The behaviour of the method is demonstrated on hand of some examples taken from the literature.     

一般线性或非线性约束下的共轭投影变尺度方法

梯度投影法是一类有效的约束最优化算法，在最优化领域中占有重要的地位．但是，梯度投影法所采用的投影是正交投影，不包含目标函数和约束函数的二阶导数信息·因而；收敛速度不太令人满意．本文介绍一种共轭投影概念，利用共轭投影构造了一般线性或非线性约束下的共轭投影变尺度算法，并证明了算法在一定条件下具有全局收敛性．由于算法中的共轭投影恰当地包含了目标函数和约束函数的二阶导数信息，因而收敛速度有希望加快．数值试验的结果表明算法是有效的．     

A practicable branch-and-bound algorithm for globally solving linear multiplicative programming

To globally solve linear multiplicative programming problem (LMP), this paper presents a practicable branch-and-bound method based on the framework of branch-and-bound algorithm. In this method, a new linear relaxation technique is proposed firstly. Then, the branch-and-bound algorithm is developed for solving problem LMP. The proposed algorithm is proven that it is convergent to the global minimum by means of the subsequent solutions of a series of linear programming problems. Some experiments are reported to show the feasibility and efficiency of this algorithm.     

Fast algorithm for viscous Cahn-Hilliard equation

The main purpose of this paper is to solve the viscous Cahn-Hilliard equation via a fast algorithm based on the two time-mesh (TT-M) finite element (FE) method to ease the problem caused by strong nonlinearities. The TT-M FE algorithm includes the following main computing steps. First, a nonlinear FE method is applied on a coarse time-mesh τ_c. Here, the FE method is used for spatial discretization and the implicit second-order θ scheme (containing both implicit Crank-Nicolson and second-order backward difference) is used for temporal discretization. Second, based on the chosen initial iterative value, a linearized FE system on time fine mesh is solved, where some useful coarse numerical solutions are found by Lagrange’s interpolation formula. The analysis for both stability and a priori error estimates is made in detail. Numerical examples are given to demonstrate the validity of the proposed algorithm. Our algorithm is compared with the traditional Galerkin FE method and it is evident that our fast algorithm can save computational time.     

Preconditioned Spectral Gradient Method

The spectral gradient method is a nonmonotone gradient method for large-scale unconstrained minimization. We strengthen the algorithm by modifications which globalize the method and present strategies to apply preconditioning techniques. The modified algorithm replaces a condition of uniform positive definitness of the preconditioning matrices, with mild conditions on the search directions. The result is a robust algorithm which is effective on very large problems. Encouraging numerical experiments are presented for a variety of standard test problems, for solving nonlinear Poisson-type equations, an also for finding molecular conformations by distance geometry.     

A linear programming-based optimization algorithm for solving nonlinear programming problems

In this paper a linear programming-based optimization algorithm called the Sequential Cutting Plane algorithm is presented. The main features of the algorithm are described, convergence to a Karush–Kuhn–Tucker stationary point is proved and numerical experience on some well-known test sets is showed. The algorithm is based on an earlier version for convex inequality constrained problems, but here the algorithm is extended to general continuously differentiable nonlinear programming problems containing both nonlinear inequality and equality constraints. A comparison with some existing solvers shows that the algorithm is competitive with these solvers. Thus, this new method based on solving linear programming subproblems is a good alternative method for solving nonlinear programming problems efficiently. The algorithm has been used as a subsolver in a mixed integer nonlinear programming algorithm where the linear problems provide lower bounds on the optimal solutions of the nonlinear programming subproblems in the branch and bound tree for convex, inequality constrained problems.