Monolithic Convex Limiting for Legendre-Gauss-Lobatto Discontinuous Galerkin Spectral-Element Methods

We extend the monolithic convex limiting(MCL)methodology to nodal discontinuous Galerkin spectral-element methods(DGSEMS).The use of Legendre-Gauss-Lobatto(LGL)quadrature endows collocated DGSEM space discretizations of nonlinear hyperbolic problems with properties that greatly simplify the design of invariant domain-preserving high-resolution schemes.Compared to many other continuous and discontinuous Galerkin method variants,a particular advantage of the LGL spectral operator is the availability of a natural decomposition into a compatible subcellflux discretization.Representing a highorder spatial semi-discretization in terms of intermediate states,we performflux limiting in a manner that keeps these states and the results of Runge-Kutta stages in convex invariant domains.In addition,local bounds may be imposed on scalar quantities of interest.In contrast to limiting approaches based on predictor-corrector algorithms,our MCL procedure for LGL-DGSEM yields nonlinearflux approximations that are independent of the time-step size and can be further modified to enforce entropy stability.To demonstrate the robustness of MCL/DGSEM schemes for the compressible Euler equations,we run simulations for challenging setups featuring strong shocks,steep density gradients,and vortex dominatedflows.

Relaxed Stability Criteria for Delayed Generalized Neural Networks via a Novel Reciprocally Convex Combination

Dear Editor,This letter examines the stability issue of generalized neural networks(GNNs) with time-varying delay based on a novel reciprocally convex combination(RCC). By considering a new matrix polynomial, the proposed novel reciprocally convex method leads to a tight bound for integral inequality combination and encompasses several existing approaches as special cases.

K-凸函数的性质

借助集值映射的K-回收映射和K-雅可比映射得到了K-凸函数的两个性质。

A generalized nonlinear three-dimensional Hoek‒Brown failure criterion

To understand the strengths of rocks under complex stress states,a generalized nonlinear threedimensional(3D)Hoek‒Brown failure(NGHB)criterion was proposed in this study.This criterion shares the same parameters with the generalized HB(GHB)criterion and inherits the parameter advantages of GHB.Two new parameters,b,and n,were introduced into the NGHB criterion that primarily controls the deviatoric plane shape of the NGHB criterion under triaxial tension and compression,respectively.The NGHB criterion can consider the influence of intermediate principal stress(IPS),where the deviatoric plane shape satisfies the smoothness requirements,while the HB criterion not.This criterion can degenerate into the two modified 3D HB criteria,the Priest criterion under triaxial compression condition and the HB criterion under triaxial compression and tension condition.This criterion was verified using true triaxial test data for different parameters,six types of rocks,and two kinds of in situ rock masses.For comparison,three existing 3D HB criteria were selected for performance comparison research.The result showed that the NGHB criterion gave better prediction performance than other criteria.The prediction errors of the strength of six types of rocks and two kinds of in situ rock masses were in the range of 2.0724%-3.5091%and 1.0144%-3.2321%,respectively.The proposed criterion lays a preliminary theoretical foundation for prediction of engineering rock mass strength under complex in situ stress conditions.

基于自适应特征选择k子凸包的滚动轴承故障诊断

滚动轴承故障诊断中往往将特征选择和分类器的设计分别进行研究,从而难以获得满意的分类精度。将特征选择和分类器寻优结合起来,提出了一种自适应特征选择k子凸包(Adaptive Feature Selection K-sub Convex Hull, AFSKCH)的分类模型,从而实现了故障特征自适应选择和分类的一体化。首先,利用凸包距离函数保持数据流形上的局部邻域结构,通过交替构造k子凸包得到特征权值矩阵;其次,采用线性规划接近度方法求解k子凸包距离,利用乘子交替方向法得到自适应特征空间;最后,根据测试点到k子凸包的最小重构距离进行分类。滚动轴承故障振动信号分析结果表明,该方法特征选择性能优于其他特征选择方法,且具有较高的分类精度。

Partially-Observed Maximum Principle for Backward Stochastic Differential Delay Equations

Dear Editor,This letter investigates a partially-observed optimal control problem for backward stochastic differential delay equations(BSDDEs).By utilizing Girsanov’s theory and convex variational method,we obtain a maximum principle on the assumption that the state equation contains time delay and the control domain is convex.The adjoint processes can be represented as the solutions of certain time-advanced stochastic differential equations in finite-dimensional spaces.Linear backward stochastic differential equation(BSDE)was first introduced by Bismut in[1],while general BSDE was given by Pardoux and Peng[2].Since then,the theory of BSDEs developed rapidly.The corresponding optimal control problems,whose states are driven by BSDEs,have also been widely studied by some authors,see[3]-[5].

CONVEXITY OF THE FREE BOUNDARY FOR AN AXISYMMETRIC INCOMPRESSIBLE IMPINGING JET

This paper is devoted to the study of the shape of the free boundary for a threedimensional axisymmetric incompressible impinging jet.To be more precise,we will show that the free boundary is convex to the fluid,provided that the uneven ground is concave to the fluid.

On the Application of Mixed Models of Probability and Convex Set for Time-Variant Reliability Analysis

In time-variant reliability problems,there are a lot of uncertain variables from different sources.Therefore,it is important to consider these uncertainties in engineering.In addition,time-variant reliability problems typically involve a complexmultilevel nested optimization problem,which can result in an enormous amount of computation.To this end,this paper studies the time-variant reliability evaluation of structures with stochastic and bounded uncertainties using a mixed probability and convex set model.In this method,the stochastic process of a limit-state function with mixed uncertain parameters is first discretized and then converted into a timeindependent reliability problem.Further,to solve the double nested optimization problem in hybrid reliability calculation,an efficient iterative scheme is designed in standard uncertainty space to determine the most probable point(MPP).The limit state function is linearized at these points,and an innovative random variable is defined to solve the equivalent static reliability analysis model.The effectiveness of the proposed method is verified by two benchmark numerical examples and a practical engineering problem.

Distributed Stochastic Optimization with Compression for Non-Strongly Convex Objectives

We are investigating the distributed optimization problem,where a network of nodes works together to minimize a global objective that is a finite sum of their stored local functions.Since nodes exchange optimization parameters through the wireless network,large-scale training models can create communication bottlenecks,resulting in slower training times.To address this issue,CHOCO-SGD was proposed,which allows compressing information with arbitrary precision without reducing the convergence rate for strongly convex objective functions.Nevertheless,most convex functions are not strongly convex(such as logistic regression or Lasso),which raises the question of whether this algorithm can be applied to non-strongly convex functions.In this paper,we provide the first theoretical analysis of the convergence rate of CHOCO-SGD on non-strongly convex objectives.We derive a sufficient condition,which limits the fidelity of compression,to guarantee convergence.Moreover,our analysis demonstrates that within the fidelity threshold,this algorithm can significantly reduce transmission burden while maintaining the same convergence rate order as its no-compression equivalent.Numerical experiments further validate the theoretical findings by demonstrating that CHOCO-SGD improves communication efficiency and keeps the same convergence rate order simultaneously.And experiments also show that the algorithm fails to converge with low compression fidelity and in time-varying topologies.Overall,our study offers valuable insights into the potential applicability of CHOCO-SGD for non-strongly convex objectives.Additionally,we provide practical guidelines for researchers seeking to utilize this algorithm in real-world scenarios.

Relaxed Stability Criteria for Time-Delay Systems:A Novel Quadratic Function Convex Approximation Approach

This paper develops a quadratic function convex approximation approach to deal with the negative definite problem of the quadratic function induced by stability analysis of linear systems with time-varying delays.By introducing two adjustable parameters and two free variables,a novel convex function greater than or equal to the quadratic function is constructed,regardless of the sign of the coefficient in the quadratic term.The developed lemma can also be degenerated into the existing quadratic function negative-determination(QFND)lemma and relaxed QFND lemma respectively,by setting two adjustable parameters and two free variables as some particular values.Moreover,for a linear system with time-varying delays,a relaxed stability criterion is established via our developed lemma,together with the quivalent reciprocal combination technique and the Bessel-Legendre inequality.As a result,the conservatism can be reduced via the proposed approach in the context of constructing Lyapunov-Krasovskii functionals for the stability analysis of linear time-varying delay systems.Finally,the superiority of our results is illustrated through three numerical examples.

The UNIFORM C^(0)ESTIMATE AND WEIGHTED ESTIMATE OF GENERALIZED CHRISTOFFEL-MINKOWSKI PROBLEMS

In this paper,we consider generalized Christo®el-Minkowski problems as followsσ_(k)(u_(ij)+uδ_(ij))/σ_(l)(u_(ij)+uδ_(ij))=|u^(p-1)f(x),x∈S^(n),where 0≤l≤k≤n,p-1>0 and f is positive,and we establish the weighted gradient estimate and uniform C^(0)estimate for the positive convex even solutions,which is a generalization of Guan-Xia[1]and Guan[2].

Accelerated Primal-Dual Projection Neurodynamic Approach With Time Scaling for Linear and Set Constrained Convex Optimization Problems

The Nesterov accelerated dynamical approach serves as an essential tool for addressing convex optimization problems with accelerated convergence rates.Most previous studies in this field have primarily concentrated on unconstrained smooth con-vex optimization problems.In this paper,on the basis of primal-dual dynamical approach,Nesterov accelerated dynamical approach,projection operator and directional gradient,we present two accelerated primal-dual projection neurodynamic approaches with time scaling to address convex optimization problems with smooth and nonsmooth objective functions subject to linear and set constraints,which consist of a second-order ODE(ordinary differential equation)or differential conclusion system for the primal variables and a first-order ODE for the dual vari-ables.By satisfying specific conditions for time scaling,we demonstrate that the proposed approaches have a faster conver-gence rate.This only requires assuming convexity of the objective function.We validate the effectiveness of our proposed two accel-erated primal-dual projection neurodynamic approaches through numerical experiments.

High-Order Decoupled and Bound Preserving Local Discontinuous Galerkin Methods for a Class of Chemotaxis Models

In this paper,we explore bound preserving and high-order accurate local discontinuous Galerkin(LDG)schemes to solve a class of chemotaxis models,including the classical Keller-Segel(KS)model and two other density-dependent problems.We use the convex splitting method,the variant energy quadratization method,and the scalar auxiliary variable method coupled with the LDG method to construct first-order temporal accurate schemes based on the gradient flow structure of the models.These semi-implicit schemes are decoupled,energy stable,and can be extended to high accuracy schemes using the semi-implicit spectral deferred correction method.Many bound preserving DG discretizations are only worked on explicit time integration methods and are difficult to get high-order accuracy.To overcome these difficulties,we use the Lagrange multipliers to enforce the implicit or semi-implicit LDG schemes to satisfy the bound constraints at each time step.This bound preserving limiter results in the Karush-Kuhn-Tucker condition,which can be solved by an efficient active set semi-smooth Newton method.Various numerical experiments illustrate the high-order accuracy and the effect of bound preserving.

半拟可微拟凸规划的KKT型最优性条件

最优性条件在优化问题中起着重要的作用,它为优化算法的研究提供了重要的理论依据。众所周知,凸规划方面最优性条件已比较完善。然而,由于拟凸函数性质的特殊性,对于拟凸规划问题解的Karush-Kuhn-Tucker(KKT)类型最优性条件的研究相对较少。本文利用半拟可微刻画了拟凸规划的最优性条件,同时研究了可行集法锥与带半拟可微性质的约束函数之间的关系,并证明了上述两个结果与Greenberg-Pierskalla次微分的关系。

基于改进Forword-Backword Spliting算法的ECT图像重建算法

针对电容层析成像技术图像重建问题的不适定性和实时性要求,提出了一种基于改进前后向分裂(Forword-Backword Spliting, FBS)算法的ECT图像重建算法。首先,建立基于FBS算法的ECT逆问题求解模型,提升ECT图像重建的速度;其次将GMC惩罚项代替L1范数应用于ECT图像重建中,提高重建解的稀疏度以获得更准确的特征信号;最后建立基于GMC惩罚项的FBS图像重建模型并求解。采用COMSOL5.3和MATLAB2014软件搭建联合仿真平台,仿真结果显示,改进的算法最高将核心流模型成像相关系数提升至0.934 5,成像时间最快达到0.094 5 s,相较于Tikhonov正则化算法和Landweber算法,在成像速度和质量两方面均有明显提高。并通过实际实验验证了改进算法具有更好的可靠性和实时性。

广义的Ky Fan定理

矩阵行列式是矩阵分析学中一个很重要的分支,利用矩阵外代数,可以把行列式的计算转换成矩阵的迹算子。首先,从典型的Ky Fan定理出发,给出了该定理的一个等价形式,并且利用Brunn-Minkowski不等式给出了定理的证明。其次,利用凸函数的性质,得到了Ky Fan定理的一个更一般的表示形式。

各向异性Banach空间值Musielak-Orlicz空间的嵌入性、紧性和一致凸性

本文给出一个各向异性的Banach空间值Musielak-Orlicz空间嵌入到另一个各向异性的Banach空间值Musielak-Orlicz空间的充分必要条件,各向异性的Banach空间值Musielak-Orlicz空间的子集为相对紧的充分必要条件以及各向异性的Banach空间值Musielak-Orlicz空间为一致凸的刻画.

R^(3)中一类带有凹凸非线性项的Schrödinger-Kirchhoff方程的无穷多解

研究一类带有凹凸非线性项的Schrödinger-Kirchhoff方程,其中位势函数不必满足强制性条件,凹项满足次线性增长性条件,并且凸项在无穷远处满足超三次线性增长性条件和在原点处满足超线性增长性条件。利用Bartsch的喷泉定理证明了对任意的μ∈R,凹凸非线性项的Schrödinger-Kirchhoff方程都存在无穷多个高能量解,丰富和推广了已有的结论。

拟从属单叶函数逆的k-次根变换相关的Fekete-Szegö不等式

本文研究了一类单叶函数逆的k-次根变换[f^(-1)(ω^(k))]^(1/k)的Fekete-Szegö不等式,所得结果改进或推广了部分作者的结论.

Convergence Theorms of Ishikawa Iterative for Asymptotically Non—expansive Mapping in a Uniformly Convex Banach space

文［４］把文［３］的主要结果从 Ｈｉｌｂｅｒｔ空间推广到一致凸Ｂａｎａｃｈ空间，证明了一致凸 Ｂａｎａｃｈ空间 上从有界闭凸集到自身的渐近非扩张映象的迭代序列收敛定理。本文将有界闭凸集的条件减弱为闭 凸集，从而推广了文［４］的相应结果。