Semicontinuity of the solution set of parametric multivalued vector quasiequilibrium problems

In this paper we establish sufficient conditions for the solution set of parametric multivalued vector quasiequilibrium problems to be semicontinuous. All kinds of semicontinuity are considered: lower semicontinuity, upper semicontinuity, Hausdorff upper semicontinuity and closedness. Moreover, we investigate both the “weak” and “strong” solutions of quasiequilibrium problems.     

Robust family of exponential attractors for isotropic crystal models

Our aim in this paper is to study, in term of finite dimensional exponential attractors, the Willmore regularization, (depending on a small regularization parameter β > 0), of two phase‐field equations, namely, the Allen–Cahn and the Cahn–Hilliard equations. In both cases, we construct robust families of exponential attractors, that is, attractors that are continuous with respect to the perturbation parameter. Copyright © 2015 John Wiley & Sons, Ltd.     

Functional variational principles and coercivity over normed spaces

Using Karlin’s [4] weak game values, a ‘weakly determined’ game is introduced. Conditions for weak determinateness of games and for strict determinateness of weakly determined games are presented. Finally, generalizations of some well-known minimax theorems are derived from these results.     

三维复杂温度场高精度声学测量方法

声学温度场检测技术通过多路径声波传播时间数据,反演被测区域的温度分布。提供了一种高精度的三维复杂温度场的声学测量方法。首先从射线声学角度给出了三维非均匀温度场中声波传播路径的数学模型。在此基础上,将三维温度场的重建问题转化为声波传播路径的求解和温度场的反演问题,建立了基于多项式修正径向基函数(RBF-PR)和改进的Tikhonov正则化三维温度场重建算法。采用两种典型的炉膛三维温度场模型,在信噪比SNR=35 dB下进行了数值模拟,分析了声波传播路径在非均匀温度场中的弯曲特性、算法的重建质量和抗噪性,同时进行了实际炉膛内二维温度场的重建。结果表明了提出的考虑声线弯曲的温度场重建算法具有精度高,抗噪性强、适用性好的特点,为实现高精度的复杂温度场的声学测量提供了有效方法。     

Conical algorithm for the global minimization of linearly constrained decomposable concave minimization problems

In this paper, we are concerned with the linearly constrained global minimization of the sum of a concave function defined on ap-dimensional space and a linear function defined on aq-dimensional space, whereq may be much larger thanp. It is shown that a conical algorithm can be applied in a space of dimensionp + 1 that involves only linear programming subproblems in a space of dimensionp +q + 1. Some computational results are given.This research was accomplished while the second author was a Fellow of the Alexander von Humboldt Foundation, University of Trier, Trier, Germany.     

Orthogonal projection regularization operators

Tikhonov regularization often is applied with a finite difference regularization operator that approximates a low-order derivative. This paper proposes the use of orthogonal projections as regularization operators, e.g., with the same null space as commonly used finite difference operators. Applications to iterative and SVD-based methods for Tikhonov regularization are described. Truncated iterative and SVD methods are also considered. Research of L. Reichel was supported in part by an OBR Research Challenge Grant. Research of F. Sgallari was supported in part by PRIN 2004 grant 2004014411-005.     

Numerical differentiation by radial basis functions approximation

Based on radial basis functions approximation, we develop in this paper a new com-putational algorithm for numerical differentiation. Under an a priori and an a posteriori choice rules for the regularization parameter, we also give a proof on the convergence error estimate in reconstructing the unknown partial derivatives from scattered noisy data in multi-dimension. Numerical examples verify that the proposed regularization strategy with the a posteriori choice rule is effective and stable to solve the numerical differential problem. *The work described in this paper was partially supported by a grant from CityU (Project No. 7001646) and partially supported by the National Natural Science Foundation of China (No. 10571079).     

DISCRETE REGULARIZATION FOR OPERATOR EQUATION OF HAMMERSTEIN'S TYPE

The purpose of tthis note is to study a convergence for a method in form of combination of discrete approximations with regularization for solving operator equations of Hammerstein's type in Banach spaces. For illustration, an example in the theory of nonlinear integral equations is given.     

Concave Programming in Control Theory

We show in the present paper that many open and challenging problems in control theory belong the the class of concave minimization programs. More precisely, these problems can be recast as the minimization of a concave objective function over convex LMI (Linear Matrix Inequality) constraints. As concave programming is the best studied class of problems in global optimization, several concave programs such as simplicial and conical partitioning algorithms can be used for the resolution. Moreover, these global techniques can be combined with a local Frank and Wolfe feasible direction algorithm and improved by the use of specialized stopping criteria, hence reducing the overall computational overhead. In this respect, the proposed hybrid optimization scheme can be considered as a new line of attack for solving hard control problems.Computational experiments indicate the viability of our algorithms, and that in the worst case they require the solution of a few LMI programs. Power and efficiency of the algorithms are demonstrated for a realistic inverted-pendulum control problem.Overall, this dedication reflects the key role that concavity and LMIs play in difficult control problems.     

Entropy-Regularized Optimal Transport on Multivariate Normal and q-normal Distributions

The distance and divergence of the probability measures play a central role in statistics, machine learning, and many other related fields. The Wasserstein distance has received much attention in recent years because of its distinctions from other distances or divergences. Although computing the Wasserstein distance is costly, entropy-regularized optimal transport was proposed to computationally efficiently approximate the Wasserstein distance. The purpose of this study is to understand the theoretical aspect of entropy-regularized optimal transport. In this paper, we focus on entropy-regularized optimal transport on multivariate normal distributions and q-normal distributions. We obtain the explicit form of the entropy-regularized optimal transport cost on multivariate normal and q-normal distributions; this provides a perspective to understand the effect of entropy regularization, which was previously known only experimentally. Furthermore, we obtain the entropy-regularized Kantorovich estimator for the probability measure that satisfies certain conditions. We also demonstrate how the Wasserstein distance, optimal coupling, geometric structure, and statistical efficiency are affected by entropy regularization in some experiments. In particular, our results about the explicit form of the optimal coupling of the Tsallis entropy-regularized optimal transport on multivariate q-normal distributions and the entropy-regularized Kantorovich estimator are novel and will become the first step towards the understanding of a more general setting.