Error bounds of two smoothing approximations for semi-infinite minimax problems

In the paper we investigate smoothing method for solving semi-infinite minimax problems. Not like most of the literature in semi-infinite minimax problems which are concerned with the continuous time version（i.e., the one dimensional semi-infinite minimax problems）, the primary focus of this paper is on multi- dimensional semi-infinite minimax problems. The global error bounds of two smoothing approximations for the objective function are given and compared. It is proved that the smoothing approximation given in this paper can provide a better error bound than the existing one in literature.     

Minimax Rates of ℓp-Losses for High-Dimensional Linear Errors-in-Variables Models over ℓq-Balls

In this paper, the high-dimensional linear regression model is considered, where the covariates are measured with additive noise. Different from most of the other methods, which are based on the assumption that the true covariates are fully obtained, results in this paper only require that the corrupted covariate matrix is observed. Then, by the application of information theory, the minimax rates of convergence for estimation are investigated in terms of the ℓp(1≤p<∞)-losses under the general sparsity assumption on the underlying regression parameter and some regularity conditions on the observed covariate matrix. The established lower and upper bounds on minimax risks agree up to constant factors when p=2, which together provide the information-theoretic limits of estimating a sparse vector in the high-dimensional linear errors-in-variables model. An estimator for the underlying parameter is also proposed and shown to be minimax optimal in the ℓ2-loss.     

二次损失下一般Gauss-Markov模型的线性预测的Minimax的可容许性

对不带约束的一般Gauss-Markov模型的线性预测在齐次线性预测类中的Minimax可容许性作了较深入的讨论，得出线性预测在齐次预测类中Minimax可容许的充要条件．     

关于极小极大原理

通过减弱条件,获得了一个新的结果。由此,我们给出一些新的极小极大定理和极小极大不等式。     

求解极小极大问题的一个新算法

提出一种新的求解极小极大问题的方法,在没有积极约束梯度线性独立的条件下,证明了算法的全局收敛性.     

极大极小问题极大熵方法的研究（Ⅱ）

对成员函数是可微的和Ｌｉｐｓｃｈｉｔｚ型的极大极小的问题,研究了极大熵方法得到的近似问题和原问题满足最优性一阶必要条件的解之间的关系;举出反例说明,在特殊情况下,近似问题的局部解未必收敛原问题的局部解;原问题有解,近似问题未必有解。     

最佳逼近和重合点定理

在本文中，首先证明了一个新的ＦＫＫＭ定理和一个极小极大不等式。作为应用，作者得到了几个涉及间断映象和两个不同拓扑矢量空间的最佳逼近定理和重合点定理。这些结果改进、统一和推广在最近文献中的某些已知结果。     

Lipschitz泛函的极小极大型本质临界点

Ｌｉｐｓｃｈｉｔｚ泛函的本质临点指的具有在Ｌｉｐｓｃｈｉｔｚ局部坐标变换下的不变性的临界点，本文中指出，对于Ｂａｎａｃｈ流形上的局部Ｌｉｐｓｃｈｉｔｚ使用极小极大原理可以获得本质临界点的存在性与重数结果。