Semi-implicit finite volume level set method for advective motion of interfaces in normal direction

In this paper a semi-implicit finite volume method is proposed to solve the applications with moving interfaces using the approach of level set methods. The level set advection equation with a given speed in normal direction is solved by this method. Moreover, the scheme is used for the numerical solution of eikonal equation to compute the signed distance function and for the linear advection equation to compute the so-called extension speed [1]. In both equations an extrapolation near the interface is used in our method to treat Dirichlet boundary conditions on implicitly given interfaces. No restrictive CFL stability condition is required by the semi-implicit method that is very convenient especially when using the extrapolation approach. In summary, we can apply the method for the numerical solution of level set advection equation with the initial condition given by the signed distance function and with the advection velocity in normal direction given by the extension speed. Several advantages of the proposed approach can be shown for chosen examples and application. The advected numerical level set function approximates well the property of remaining the signed distance function during whole simulation time. Sufficiently accurate numerical results can be obtained even with the time steps violating the CFL stability condition.     

水平集方法与距离函数

讨论了有关水平集方法的基本问题,如保持为距离函数的方法,水平集方程解的存在性和唯一性。主要贡献是证明了,在距离函数约束下,水平集方程在初始零水平集附近有唯一解,它是关于演化界面的有向距离函数。并且用到了一些处理技巧:如注意到原始方程的任意解都是距离函数,将原始方程变化为另一简单形式。由于新的方程组不是一个经典方程组,则它被变换为一个普通形式,其中隐函数方法被采用。     

一种无重新初始化的水平集方法的错觉轮廓捕捉新方法

在错觉轮廓捕捉模型建立前,我们要得到根据物体边界的符号距离函数时,用Eikonal方程不能实现的,我们用基于水平集方法的分割技术实现,扩大了模型的使用范围;在Zhu和Chan等人的错觉轮廓捕捉模型基础上引入了李纯明等人提出的符号距离约束信息,这就使得在水平集函数演化时不必对其重新初始化,并大大简化了模型的数值处理水平集函数的演化速度.并通过实验验证了该方法的优势.     

A splitting bundle approach for non-smooth non-convex minimization

We present a bundle-type method for minimizing non-convex non-smooth functions. Our approach is based on the partition of the bundle into two sets, taking into account the local convex or concave behaviour of the objective function. Termination at a point satisfying an approximate stationarity condition is proved and numerical results are provided.     

Finite Element Approximation of Hemivariational Inequalities

The paper deals with a finite element approximation of elliptic and parabolic variational inequalities. Elliptic hemivariational inequalities are equivalently expressed as a system consisting of one equation and one inclusion for a couple of unknowns, namely a primal variable u and an element belonging to a multivalued mapping at u. Both components of the solution are approximated independently each other by a finite element method. Parabolic inequalities are transformed into a system of elliptic ones by using an appropriate time discretization. A numerical experiment is realized by using non-smooth optimization methods.     

On inverse methods for the resolution of the Gibbs phenomenon

When Fourier expansions, or more generally spectral methods, are used for the representation of nonsmooth functions, then one has to face the so-called Gibbs phenomenon. Considerable progresses have been made these last years to overcome the Gibbs phenomenon, using direct or inverse approaches, both in the discrete or continuous framework. A discrete inverse method for the global or local reconstruction of a non-smooth function starting from its oscillating (trigonometric) polynomial interpolant is introduced and both its capabilities and limits are emphasized.     

A Petrov-Galerkin method for a singularly perturbed ordinary differential equation with non-smooth data

In this paper, a singularly perturbed ordinary differential equation with non-smooth data is considered. The numerical method is generated by means of a Petrov-Galerkin finite element method with the piecewise-exponential test function and the piecewise-linear trial function. At the discontinuous point of the coefficient, a special technique is used. The method is shown to be first-order accurate and singular perturbation parameter uniform convergence. Finally, numerical results are presented, which are in agreement with theoretical results.     

混合广义Jacobi和Chebyshev谱配置法求解时间分数阶对流扩散方程

研究时间Caputo分数阶对流扩散方程的高效高阶数值方法.对于给定的时间分数阶偏微分方程,在时间和空间方向分别采用基于移位广义Jacobi函数为基底和移位Chebyshev多项式运算矩阵的谱配置法进行数值求解.这样得到的数值解可以很好地逼近一类在时间方向非光滑的方程解.最后利用一些数值例子来说明该数值方法的有效性和准确性.     

Newton’s method and its use in optimization

Newton’s method is a basic tool in numerical analysis and numerous applications, including operations research and data mining. We survey the history of the method, its main ideas, convergence results, modifications, its global behavior. We focus on applications of the method for various classes of optimization problems, such as unconstrained minimization, equality constrained problems, convex programming and interior point methods. Some extensions (non-smooth problems, continuous analog, Smale’s results, etc.) are discussed briefly, while some others (e.g., versions of the method to achieve global convergence) are addressed in more details.     

求解带箱子集约束的非光滑全局优化问题的填充函数方法

提出了一个求解带箱子集约束的非光滑全局优化问题的填充函数方法.构造的填充函数只包含一个参数,且此参数在迭代过程中容易调节.分析了填充函数的理论性质,在此基础上设计了填充函数算法.数值计算验证了该算法的有效性.     

A second-order accurate method for solving the eikonal equation

We present a numerical method for computing the signed distance to a piecewise-smooth surface defined as the zero set of a function. It is based on a marching method by Kim and a hybrid discretization of first- and second-order discretizations of the eikonal equation. If the solution is smooth at a point and at all of the points in the domain of dependence of that point, the solution is second-order accurate; otherwise, the method is first-order accurate, and computes the computes the correct entropy solution in the presence of kinks in the initial surface. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

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The definition of Brownian distance is presented and it's proved that Brownian distance coincides with the energy distance with respect to Brownian motion. Energy distance for dependent random vectors is also given and the asymptotic distribution is derived under null hypothesis. A simple numerical simulation result shows that the method for paired-sample test based on energy distance can distinguish the distributions of the paired variables more effectively than the classical t-test and Wilcoxon signed rank test.     

Incremental-like bundle methods with application to energy planning

An important field of application of non-smooth optimization refers to decomposition of large-scale or complex problems by Lagrangian duality. In this setting, the dual problem consists in maximizing a concave non-smooth function that is defined as the sum of sub-functions. The evaluation of each sub-function requires solving a specific optimization sub-problem, with specific computational complexity. Typically, some sub-functions are hard to evaluate, while others are practically straightforward. When applying a bundle method to maximize this type of dual functions, the computational burden of solving sub-problems is preponderant in the whole iterative process. We propose to take full advantage of such separable structure by making a dual bundle iteration after having evaluated only a subset of the dual sub-functions, instead of all of them. This type of incremental approach has already been applied for subgradient algorithms. In this work we use instead a specialized variant of bundle methods and show that such an approach is related to bundle methods with inexact linearizations. We analyze the convergence properties of two incremental-like bundle methods. We apply the incremental approach to a generation planning problem over an horizon of one to three years. This is a large scale stochastic program, unsolvable by a direct frontal approach. For a real-life application on the French power mix, we obtain encouraging numerical results, achieving a significant improvement in speed without losing accuracy.     

Exact Worst-Case Convergence Rates of the Proximal Gradient Method for Composite Convex Minimization

We study the worst-case convergence rates of the proximal gradient method for minimizing the sum of a smooth strongly convex function and a non-smooth convex function, whose proximal operator is available. We establish the exact worst-case convergence rates of the proximal gradient method in this setting for any step size and for different standard performance measures: objective function accuracy, distance to optimality and residual gradient norm. The proof methodology relies on recent developments in performance estimation of first-order methods, based on semidefinite programming. In the case of the proximal gradient method, this methodology allows obtaining exact and non-asymptotic worst-case guarantees that are conceptually very simple, although apparently new. On the way, we discuss how strong convexity can be replaced by weaker assumptions, while preserving the corresponding convergence rates. We also establish that the same fixed step size policy is optimal for all three performance measures. Finally, we extend recent results on the worst-case behavior of gradient descent with exact line search to the proximal case.     

Steplengths in the extragradient type methods

The extragradient type methods are a class of efficient direct methods. For solving monotone variational inequalities, these methods only require function evaluation, and therefore are widely applied to black-box models. In this type of methods, the distance between the iterate and a fixed solution point decreases by iterations. Furthermore, in each iteration, the negative increment of such squared distance has a differentiable concave lower bound function without requiring any solution in its formula. In this paper, we investigate some properties for the lower bound. Our study reveals that the lower bound affords a steplength domain which guarantees the convergence of the entire algorithm. Based on these results, we present two new steplengths. One involves the projection onto the tangent cone without line search, while the other can be computed via searching the positive root of a one dimension concave lower bound function. Our preliminary numerical results confirm and illustrate the attractiveness of our contributions.     

Multiple criteria group decision-making with generalized interval-valued fuzzy numbers based on signed distances and incomplete weights

Decision-making information provided by decision makers is often imprecise or uncertain, due to lack of data, time pressure, or the decision makers’ limited attention and information-processing capabilities. Interval-valued fuzzy sets are associated with greater imprecision and more ambiguity than are ordinary fuzzy sets. For these reasons, this paper presents a signed distance-based method for handling fuzzy multiple-criteria group decision-making problems in which individual assessments are provided as generalized interval-valued trapezoidal fuzzy numbers, and the information about criterion weights are not precisely but partially known. First, concerning the relative importance of decision makers and the group consensus of fuzzy opinions, all individual decision opinions were aggregated into group opinions using a hybrid average with weighted averaging and signed distance-based ordered weighted averaging operations. Next, considering a decision situation with incomplete weight information of criteria, an integrated programming model was developed to estimate criterion weights and to order the priorities of various alternatives based on signed distances. In addition, several deviation variables were introduced to mitigate the effect of inconsistent evaluations on the importance of criteria. Finally, the feasibility of the proposed method is illustrated by a numerical example of a multi-criteria supplier selection problem. Furthermore, a comparative analysis with other methods was conducted to validate the effectiveness and applicability of the proposed methodology.     

A multigrid method for the Cahn-Hilliard equation with obstacle potential

We present a multigrid finite element method for the deep quench obstacle Cahn-Hilliard equation. The non-smooth nature of this highly nonlinear fourth order partial differential equation make this problem particularly challenging. The method exhibits mesh-independent convergence properties in practice for arbitrary time step sizes. In addition, numerical evidence shows that this behaviour extends to small values of the interfacial parameter γ. Several numerical examples are given, including comparisons with existing alternative solution methods for the Cahn-Hilliard equation.     

A robust finite difference scheme for pricing American put options with Singularity-Separating method

In this paper we present a stable numerical method for the linear complementary problem arising from American put option pricing. The numerical method is based on a hybrid finite difference spatial discretization on a piecewise uniform mesh and an implicit time stepping technique. The scheme is stable for arbitrary volatility and arbitrary interest rate. We apply some tricks to derive the error estimates for the direct application of finite difference method to the linear complementary problem. We use the Singularity-Separating method to remove the singularity of the non-smooth payoff function. It is proved that the scheme is second-order convergent with respect to the spatial variable. Numerical results support the theoretical results.     

Multiple subgradient descent bundle method for convex nonsmooth multiobjective optimization

The aim of this paper is to propose a new multiple subgradient descent bundle method for solving unconstrained convex nonsmooth multiobjective optimization problems. Contrary to many existing multiobjective optimization methods, our method treats the objective functions as they are without employing a scalarization in a classical sense. The main idea of this method is to find descent directions for every objective function separately by utilizing the proximal bundle approach, and then trying to form a common descent direction for every objective function. In addition, we prove that the method is convergent and it finds weakly Pareto optimal solutions. Finally, some numerical experiments are considered.     

An interior proximal linearized method for DC programming based on Bregman distance or second-order homogeneous kernels

Abstract

We present an interior proximal method for solving constrained nonconvex optimization problems where the objective function is given by the difference of two convex function (DC function). To this end, we consider a linearized proximal method with a proximal distance as regularization. Convergence analysis of particular choices of the proximal distance as second-order homogeneous proximal distances and Bregman distances are considered. Finally, some academic numerical results are presented for a constrained DC problem and generalized Fermat–Weber location problems.