Composite penalty method of a low order anisotropic nonconforming finite element for the Stokes problem

Composite penalty method of a low order anisotropic nonconforming quadrilateral finite element for the Stokes problem is presented. This method with a large penalty parameter can achieve the same accuracy as the stand method with a small penalty parameter and the convergence rate of this method is two times as that of the standard method under the condition of the same order penalty parameter. The superconvergence for velocity is established as well. The results of this paper are also valid to the most of the known nonconforming finite element methods.     

求解定常不可压Stokes方程的两层罚函数方法

借助于两套有限元网格空间提出了一种求解定常不可压Stokes方程的两层罚函数方法.该方法只需要求解粗网格空间上的Stokes方程和细网格空间上的两个易于求解的罚参数方程（离散后的线性方程组具有相同的对称正定系数矩阵）.收敛性分析表明粗网格空间相对于细网格空间可以选择很小，并且罚参数的选取只与粗网格步长和问题的正则性有关.因此罚参数不必选择很小仍能够得到最优解.最后通过数值算例验证了上述理论结果，并且数值对比可知两层罚函数方法对于求解定常不可压Stokes方程具有很好的效果.     

General Information

In this paper, a new sequential penalty algorithm, based on the L_infin exact penalty function, is proposed for a general nonlinear constrained optimization problem. The algorithm has the following characteristics: it can start from an arbitrary initial point; the feasibility of the subproblem is guaranteed; the penalty parameter is adjusted automatically; global convergence without any regularity assumption is proved. The update formula of the penalty parameter is new. It is proved that the algorithm proposed in this paper behaves equivalently to the standard SQP method after sufficiently many iterations. Hence, the local convergence results of the standard SQP method can be applied to this algorithm. Preliminary numerical experiments show the efficiency and stability of the algorithm.     

Stokes问题低阶非协调混合元的改进加罚算法(英文)

通过对由经典加罚算法得到的两个解进行线性组合,研究Stokes方程低阶非协调混合元的改进加罚算法.该方法利用较大的罚参数能得到同使用较小参数的经典加罚方法一样的收敛阶.此外,基于单元的特性和插值后处理技巧,得到一些超收敛结果,从而改进以往的文献结果.     

Study of a primal-dual algorithm for equality constrained minimization

The paper proposes a primal-dual algorithm for solving an equality constrained minimization problem. The algorithm is a Newton-like method applied to a sequence of perturbed optimality systems that follow naturally from the quadratic penalty approach. This work is first motivated by the fact that a primal-dual formulation of the quadratic penalty provides a better framework than the standard primal form. This is highlighted by strong convergence properties proved under standard assumptions. In particular, it is shown that the usual requirement of solving the penalty problem with a precision of the same size as the perturbation parameter, can be replaced by a much less stringent criterion, while guaranteeing the superlinear convergence property. A second motivation is that the method provides an appropriate regularization for degenerate problems with a rank deficient Jacobian of constraints. The numerical experiments clearly bear this out. Another important feature of our algorithm is that the penalty parameter is allowed to vary during the inner iterations, while it is usually kept constant. This alleviates the numerical problem due to ill-conditioning of the quadratic penalty, leading to an improvement of the numerical performances.     

混合杂交罚函数有限元方法及其应用

对一般非协调有限元,目前采用最多的两种方法是罚函数法和混合、杂交法.前一种方法总能保证收敛,但精度差,条件数和稀疏性不好;后一种方法则要满足“秩条件”才能保证收敛,故单元的构造受到很大的限制.本文提出把这两种方法结合一起的有限元方法——混合杂交罚函数法.从理论上严格证明了(在非常一般的条件下)这种新方法总是收敛的,并且其精度、条件数以及稀疏性等皆与协调元相同,也就是说都是最优的. 最后应用这一方法具体构造了一个新的九自由度任意三角形弯板单元(每个顶点给三个自由度——一个位移和两个转角),其单元刚度矩阵计算公式与旧的九自由度三角形弯板单元的计算公式相差不多.但它对任意几何形状的平板都收敛于真解,如果真解u∈H3的话,它的三个弯矩具有一阶精度,位移及两个转角均具有二阶精度.     

一个等式约束问题的SQP方法及其收敛性

本文提出一个SQP算法,其效益函数为Flether^[1]提出的连续可微精确罚函数。该算法具有全局收敛性和超线性收敛速度,并且能自动调节罚参数,能有效地处理计算搜索方向的二次子规划的不可行问题。     

Log-Sigmoid Multipliers Method in Constrained Optimization

In this paper we introduced and analyzed the Log-Sigmoid (LS) multipliers method for constrained optimization. The LS method is to the recently developed smoothing technique as augmented Lagrangian to the penalty method or modified barrier to classical barrier methods. At the same time the LS method has some specific properties, which make it substantially different from other nonquadratic augmented Lagrangian techniques.We established convergence of the LS type penalty method under very mild assumptions on the input data and estimated the rate of convergence of the LS multipliers method under the standard second order optimality condition for both exact and nonexact minimization.Some important properties of the dual function and the dual problem, which are based on the LS Lagrangian, were discovered and the primal–dual LS method was introduced.     

熵函数法的数学理论

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A sequential quadratic penalty method for nonlinear semidefinite programming

In this article, a nonlinear semidefinite program is reformulated into a mathematical program with a matrix equality constraint and a sequential quadratic penalty method is proposed to solve the latter problem. We discuss the differentiability and convexity of the penalty function. Necessary and sufficient conditions for the convergence of optimal values of penalty problems to that of the original semidefinite program are obtained. The convergence of optimal solutions of penalty problems to that of the original semidefinite program is also investigated. We show that any limit point of a sequence of stationary points of penalty problems satisfies the KKT optimality condition of the semidefinite program. Smoothed penalty problems that have the same order of smothness as the original semidefinite program are adopted. Corresponding results such as the convexity of the smoothed penalty function, the convergence of optimal values, optimal solutions and the stationary points of the smoothed penalty problems are obtained.     

Convergence analysis of power penalty method for American bond option pricing

This paper is concerned with the convergence analysis of power penalty method to pricing American options on discount bond, where the single factor Cox–Ingrosll–Ross model is adopted for the short interest rate. The valuation of American bond option is usually formulated as a partial differential complementarity problem. We first develop a power penalty method to solve this partial differential complementarity problem, which produces a nonlinear degenerated parabolic PDE. Within the framework of variational inequalities, the solvability and convergence properties of this penalty approach are explored in a proper infinite dimensional space. Moreover, a sharp rate of convergence of the power penalty method is obtained. Finally, we show that the power penalty approach is monotonically convergent with the penalty parameter.     

Г-环的单位元与其算子环的单位元之间的关系

г—环的单位元是其算子环中的元素.本文探讨Г—的单位与其算子环的单位元之间的关系.举例表明存在Г—环(Г_N—环)M,它的左、右算子环均有单位元,而M既无左单位元,又无右单位元.那么在什么条件下,Г—环(Г_N—环)的左、右算子环具有单位元时,其本身必定具有左、右单位元呢?对Г—环和Г_N—环分别探讨了此问题,并给出了了解答此问题的充要条件.     

A new penalty-free-type algorithm based on trust region techniques

In this paper, we propose a new penalty-free-type method for nonlinear equality constrained problems. The new algorithm uses trust region framework and feasibility safeguarding technique. Moreover, it has no choice of penalty parameter and penalty function as a merit function, and it does not use the filter technique to avoid the penalty function either. We analyze the global convergence of the main algorithm under the standard assumptions. The preliminary numerical tests are reported.     

Convergence of the Neumann parallel scheme of the domain decomposition method for problems of frictionless contact between several elastic bodies

Based on the variational formulation and penalty method, we have considered the Neumann parallel scheme of the domain decomposition method for the solution of problems of one-sided contact between three-dimensional elastic bodies. We have shown the existence and uniqueness of a solution of the variational problem with penalty and convergence in the penalty parameter. The convergence of this scheme has been proved, and the optimal value of iteration parameter has been determined.     

An Alternating Direction Method of Multipliers for MCP-penalized Regression with High-dimensional Data

The minimax concave penalty (MCP) has been demonstrated theoretically and practically to be effective in nonconvex penalization for variable selection and parameter estimation. In this paper, we develop an efficient alternating direction method of multipliers (ADMM) with continuation algorithm for solving the MCP-penalized least squares problem in high dimensions. Under some mild conditions, we study the convergence properties and the Karush–Kuhn–Tucker (KKT) optimality conditions of the proposed method. A high-dimensional BIC is developed to select the optimal tuning parameters. Simulations and a real data example are presented to illustrate the efficiency and accuracy of the proposed method.     

Low-rank and sparse matrix recovery from noisy observations via 3-block ADMM algorithm

Recovering low-rank and sparse matrix from a given matrix arises in many applications, such as image processing, video background substraction, and so on. The 3-block alternating direction method of multipliers (ADMM) has been applied successfully to solve convex problems with 3-block variables. However, the existing sufficient conditions to guarantee the convergence of the 3-block ADMM usually require the penalty parameter $\gamma$ to satisfy a certain bound, which may affect the performance of solving the large scale problem in practice. In this paper, we propose the 3-block ADMM to recover low-rank and sparse matrix from noisy observations. In theory, we prove that the 3-block ADMM is convergent when the penalty parameters satisfy a certain condition and the objective function value sequences generated by 3-block ADMM converge to the optimal value. Numerical experiments verify that proposed method can achieve higher performance than existing methods in terms of both efficiency and accuracy.     

The Method of Multipliers for Nonlinearly Constrained Variational Inequalities

The method of multipliers for variational inequalities with nonstrictly monotone cost mappings and convex differentiable constraints is considered. We prove the convergence of the method with an arbitrary value of the penalty parameter. We suggest to evaluate accuracy of solutions of auxiliary subproblems with the help of gap functions.     

General primal-dual penalty/barrier path-following Newton methods for nonlinear programming

In the present article rather general penalty/barrier-methods are considered, that define a local continuously differentiable primal-dual path. The class of penalty/barrier terms includes most of the usual techniques like logarithmic barriers, SUMT, quadratic loss functions as well as exponential penalties, and the optimization problem which may contain inequality as well as equality constraints. The convergence of the corresponding general primal-dual path-following method is shown for local minima that satisfy strong second-order sufficiency conditions with linear independence constraint qualification (LICQ) and strict complementarity. A basic tool in the analysis of these methods is to estimate the radius of convergence of Newton's method depending on the penalty/barrier-parameter. Without using self-concordance properties convergence bounds are derived by direct estimations of the solutions of the Newton equations. Parameter selection rules are proposed which guarantee the local convergence of the considered penalty/barrier-techniques with only a finite number of Newton steps at each parameter level. Numerical examples illustrate the practical behavior of the proposed class of methods.     

An augmented penalty function method with penalty parameter updates for nonconvex optimization

Given an augmented Lagrangian scheme for a general optimization problem, we use an epsilon subgradient step for improving the dual function. This can be seen as an update for an augmented penalty method, which is more stable because it does not force the penalty parameter to tend to infinity. We establish for this update primal-dual convergence for our augmented penalty method. As illustration, we apply our method to the test-bed kissing number problem.     

THE CONVERGENCE OF APPROACH PENALTY FUNCTION METHOD FOR APPROXIMATE BILEVEL PROGRAMMING PROBLEM

' 1 IntroductionWe collsider the fOllowi11g bilevel programndng problen1:max f(x, y),(BP) s.t.x E X = {z E RnIAx = b,x 2 0}, (1)y e Y(x).whereY(x) = {argmaxdTyIDx Gy 5 g, y 2 0}, (2)and b E R", d, y E Rr, g E Rs, A, D.and G are m x n1 s x n aild 8 x r matrices respectively. If itis not very difficult to eva1uate f(and/or Vf) at all iteration points, there are many algorithmeavailable fOr solving problem (BP) (see [1,2,3etc1). However, in some problems (see [4]), f(x, y)is too com…