Variable parameter Uzawa method for solving a class of block three-by-three saddle point problems

In this work, we consider numerical methods for solving a class of block three-by-three saddle point problems, which arise from finite element methods for solving time-dependent Maxwell equations and a class of quadratic programs. We present a variant of Uzawa method with two variable parameters for the saddle point problems. These two parameters can be updated easily in each iteration, similar to the evaluation of the two iteration parameters in the conjugate gradient method. We show that the new iterative method converges to the unique solution of the saddle point problems under a reasonable condition. Numerical experiments highlighting the performance of the proposed method for problems are presented.

     

Successive approximation technique for a class of large-scale NLP problems and its application to dynamic programming

In this paper, two successive approximation techniques are presented for a class of large-scale nonlinear programming problems with decomposable constraints and a class of high-dimensional discrete optimal control problems, respectively. It is shown that: (a) the accumulation point of the sequence produced by the first method is a Kuhn-Tucker point if the constraint functions are decomposable and if the uniqueness condition holds; (b) the sequence converges to an optimum solution if the objective function is strictly pseudoconvex and if the constraint functions are decomposable and quasiconcave; and (c) similar conclusions for the second method hold also for a class of discrete optimal control problems under some assumptions.     

A numerical method for solving three-dimensional elliptic interface problems with triple junction points

Elliptic interface problems with multi-domains have wide applications in engineering and science. However, it is challenging for most existing methods to solve three-dimensional elliptic interface problems with multi-domains due to local geometric complexity, especially for problems with matrix coefficient and sharp-edged interface. There are some recent work in two dimensions for multi-domains and in three dimensions for two domains. However, the extension to three dimensional multi-domain elliptic interface problems is non-trivial. In this paper, we present an efficient non-traditional finite element method with non-body-fitting grids for three-dimensional elliptic interface problems with multi-domains. Numerical experiments show that this method achieves close to second order accurate in the L ^∞ norm for piecewise smooth solutions.     

Optimal control with a cost on changing control

In this paper, we consider a class of optimal control problems in which the cost functional is the sum of the terminal cost, the integral cost, and the full variation of control. The term involving the full variation of control is to measure the changes on the control action. A computational method based on the control parametrization technique is developed for solving this class of optimal control problems. This computational method is supported by a convergence analysis. For illustration, two numerical examples are solved using the proposed method.This project was partially supported by an Australian Research Grant.This paper is dedicated to Professor L. Cesari on the occasion of his 80th birthday.     

A modified combined relaxation method for non-linear convex variational inequalities

We consider a class of non-linear problems which is intermediate between equilibrium and variational inequality ones and has many applications. Unlike the usual variational inequality it involves two non-linear mappings, which need not be differentiable. We propose a class of iterative methods for this problem, which converge to a solution under weakened monotonicity type assumptions. This method is simpler essentially in comparison with those for the corresponding non-linear equilibrium problems.     

具有双参数拟线性微分方程的奇摄动Robin边值问题(英文)

主要研究一类具有双参数的拟线性微分方程的奇摄动Robin边值问题.利用微分不等式理论,对两参数分三种不同情形对解的构造进行分析.并得到相应问题在各情形下的渐近解和余项估计.     

Two block hybrid projection method for solving a common solution for a system of generalized equilibrium problems and fixed point problems for two countable families

In this paper, we introduce a new iterative sequence which is constructed by using the new modified two block hybrid projection method for solving the common solution problem for a system of generalized equilibrium problems of inverse strongly monotone mappings and a system of bifunctions satisfying certain conditions, and the common fixed point problems for families of uniformly quasi - ${\phi}$ - asymptotically nonexpansive and locally uniformly Lipschitz continuous. Strong convergence theorems are proved on approximating a common solution of a system of generalized equilibrium problems and fixed point problems for two countable families in Banach spaces. Our results presented in this paper improve and extend many recent results in this area.     

一类四阶非线性奇摄动方程的边值问题

利用匹配渐近展开法,讨论了一类四阶非线性方程的具有两个边界层的奇摄动边值问题．引进伸长变量,根据边界条件与匹配原则,在一定的可解性条件下,给出了外部解和左右边界层附近的内层解,得到了该问题的二阶渐近解,并举例说明了这类非线性问题渐近解的存在性．     

Improved smoothing Newton methods for symmetric cone complementarity problems

There recently has been much interest in smoothing Newton method for solving nonlinear complementarity problems. We extend such method to symmetric cone complementarity problems (SCCP). In this paper, we first investigate a one-parametric class of smoothing functions in the context of symmetric cones, which contains the Fischer–Burmeister smoothing function and the CHKS smoothing function as special cases. Then we propose a smoothing Newton method for the SCCP based on the one-parametric class of smoothing functions. For the proposed method, besides the classical step length, we provide a new step length and the global convergence is obtained. Finally, preliminary numerical results are reported, which show the effectiveness of the two step lengthes in the algorithm and provide efficient domains of the parameter for the complementarity problems.     

Enlarging the domain of convergence for multiple shooting by the homotopy method

Summary The homotopy method is a frequently used technique in overcoming the local convergence nature of multiple shooting. In this paper sufficient conditions are given that guarantee the homotopy process to be feasible. The results are applicable to a class of two-point boundary value problems. Finally, the numerical solution of two practical problems arising in physiology is described.     

Accelerated PMHSS iteration methods for complex symmetric linear systems

In this paper, we introduce and analyze an accelerated preconditioning modification of the Hermitian and skew-Hermitian splitting (APMHSS) iteration method for solving a broad class of complex symmetric linear systems. This accelerated PMHSS algorithm involves two iteration parameters α,β and two preconditioned matrices whose special choices can recover the known PMHSS (preconditioned modification of the Hermitian and skew-Hermitian splitting) iteration method which includes the MHSS method, as well as yield new ones. The convergence theory of this class of APMHSS iteration methods is established under suitable conditions. Each iteration of this method requires the solution of two linear systems with real symmetric positive definite coefficient matrices. Theoretical analyses show that the upper bound σ₁(α,β) of the asymptotic convergence rate of the APMHSS method is smaller than that of the PMHSS iteration method. This implies that the APMHSS method may converge faster than the PMHSS method. Numerical experiments on a few model problems are presented to illustrate the theoretical results and examine the numerical effectiveness of the new method.     

Fast algorithm for singly linearly constrained quadratic programs with box-like constraints

This paper focuses on a singly linearly constrained class of convex quadratic programs with box-like constraints. We propose a new fast algorithm based on parametric approach and secant approximation method to solve this class of quadratic problems. We design efficient implementations for our proposed algorithm and compare its performance with two state-of-the-art standard solvers called Gurobi and Mosek. Numerical results on a variety of test problems demonstrate that our algorithm is able to efficiently solve the large-scale problems with the dimension up to fifty million and it substantially outperforms Gurobi and Mosek in terms of the running time.     

Direct and iterative methods for the numerical solution of mixed integral equations

In this paper we analyze and compare two classical methods to solve Volterra–Fredholm integral equations. The first is a collocation method; the second one is a fixed point method. Both of them are proposed on a particular class of approximating functions. Precisely the first method is based on a linear spline class approximation and the second one on Schauder linear basis. We analyze some problems of convergence and we propose some remarks about the peculiarities and adaptability of both methods. Numerical results complete the work.     

Comparison of Two Sets of First-Order Conditions as Bases of Interior-Point Newton Methods for Optimization with Simple Bounds

In this paper, we compare the behavior of two Newton interior-point methods derived from two different first-order necessary conditions for the same nonlinear optimization problem with simple bounds. One set of conditions was proposed by Coleman and Li; the other is the standard KKT set of conditions. We discuss a perturbation of the CL conditions for problems with one-sided bounds and the difficulties involved in extending this to problems with general bounds. We study the numerical behavior of the Newton method applied to the systems of equations associated with the unperturbed and perturbed necessary conditions. Preliminary numerical results for convex quadratic objective functions indicate that, for this class of problems, the Newton method based on the perturbed KKT formulation appears to be the more robust.     

A generalized conditional gradient method and its connection to an iterative shrinkage method

This article combines techniques from two fields of applied mathematics: optimization theory and inverse problems. We investigate a generalized conditional gradient method and its connection to an iterative shrinkage method, which has been recently proposed for solving inverse problems. The iterative shrinkage method aims at the solution of non-quadratic minimization problems where the solution is expected to have a sparse representation in a known basis. We show that it can be interpreted as a generalized conditional gradient method. We prove the convergence of this generalized method for general class of functionals, which includes non-convex functionals. This also gives a deeper understanding of the iterative shrinkage method.     

A Convergent Variant of the Nelder–Mead Algorithm

The Nelder–Mead algorithm (1965) for unconstrained optimization has been used extensively to solve parameter estimation and other problems. Despite its age, it is still the method of choice for many practitioners in the fields of statistics, engineering, and the physical and medical sciences because it is easy to code and very easy to use. It belongs to a class of methods which do not require derivatives and which are often claimed to be robust for problems with discontinuities or where the function values are noisy. Recently (1998), it has been shown that the method can fail to converge or converge to nonsolutions on certain classes of problems. Only very limited convergence results exist for a restricted class of problems in one or two dimensions. In this paper, a provably convergent variant of the Nelder–Mead simplex method is presented and analyzed. Numerical results are included to show that the modified algorithm is effective in practice.     

一类多值多导数方法的收敛性与稳定性

1 引言 对于多值多导数方法,由于其多值多导的结构特点有利于提高解的精度,以及其包容性大,它包含了当今常用的多种常微数值方法,诸如:线性多步法,单支方法,多步多导方法,多(单)步Runge—Kutta方法,多导Runge-Kutta方法以及混合方法等.因此收敛性与稳定性的研究具有重要的实践意义和广泛的理论指导意义,也正因如此,这方面的研究工作引起了众多数值工作者们的兴趣,近年来,多值多导法求解刚性问题的B—收敛及其非线性稳定性的研究工作巳获得较大进展,其相应成果可参见文献[1—3],在文献[4,5]中笔者则针对Banach空间中一类非刚性问题-K~((p))类问题,分别探讨了多步多导法及单支方法的收敛性     

Successive convex approximations to cardinality-constrained convex programs: a piecewise-linear DC approach

In this paper we consider cardinality-constrained convex programs that minimize a convex function subject to a cardinality constraint and other linear constraints. This class of problems has found many applications, including portfolio selection, subset selection and compressed sensing. We propose a successive convex approximation method for this class of problems in which the cardinality function is first approximated by a piecewise linear DC function (difference of two convex functions) and a sequence of convex subproblems is then constructed by successively linearizing the concave terms of the DC function. Under some mild assumptions, we establish that any accumulation point of the sequence generated by the method is a KKT point of the DC approximation problem. We show that the basic algorithm can be refined by adding strengthening cuts in the subproblems. Finally, we report some preliminary computational results on cardinality-constrained portfolio selection problems.     

Interpolation problems,extensions of symmetric operators and reproducing kernel spaces II

The aim of part I and this paper is to study interpolation problems for pairs of matrix functions of the extended Nevanlinna class using two different approaches and to make explicit the various links between them. In part I we considered the approach via the Kreîn-Langer theory of extensions of symmetric operators. In this paper we adapt Dym's method to solve interpolation problems by means of the de Branges theory of Hilbert spaces of analytic functions. We also show here how the two solution methods are connected.     

A first-order block-decomposition method for solving two-easy-block structured semidefinite programs

In this paper, we consider a first-order block-decomposition method for minimizing the sum of a convex differentiable function with Lipschitz continuous gradient, and two other proper closed convex (possibly, nonsmooth) functions with easily computable resolvents. The method presented contains two important ingredients from a computational point of view, namely: an adaptive choice of stepsize for performing an extragradient step; and the use of a scaling factor to balance the blocks. We then specialize the method to the context of conic semidefinite programming (SDP) problems consisting of two easy blocks of constraints. Without putting them in standard form, we show that four important classes of graph-related conic SDP problems automatically possess the above two-easy-block structure, namely: SDPs for $\theta $ -functions and $\theta _{+}$ -functions of graph stable set problems, and SDP relaxations of binary integer quadratic and frequency assignment problems. Finally, we present computational results on the aforementioned classes of SDPs showing that our method outperforms the three most competitive codes for large-scale conic semidefinite programs, namely: the boundary point (BP) method introduced by Povh et al., a Newton-CG augmented Lagrangian method, called SDPNAL, by Zhao et al., and a variant of the BP method, called the SPDAD method, by Wen et al.