The Possibilities of Using the Minimax Method to Diagnose the State of the Atmosphere

The article is devoted to the discussion of the possibilities of approbation of one of the probabilistic methods of verification of evaluation works-the minimax method or the method of establishing the minimum risk of making erroneous diagnoses of the instability of the planetary boundary layer of air.Within the framework of this study,the task of probabilistic forecasting of diagnostic parameters and their combinations,leading in their totality to the formation of an unstable state of the planetary boundary layer of the atmosphere,was carried out.It is this state that,as shown by previous studies,a priori contribution to the development of a number of weather phenomena dangerous for society(squalls,hail,heavy rains,etc.).The results of applying the minimax method made it possible to identify a number of parameters,such as the intensity of circulation,the activity of the Earth’s magnetosphere,and the components of the geostrophic wind velocity,the combination of which led to the development of instability.In the future,it is possible to further expand the number of diagnosed parameters to identify more sensitive elements.In this sense,the minimax method,the usefulness of which is shown in this study,can be considered as one of the preparatory steps for the subsequent more detailed method for forecasting individual hazardous weather phenomena.

NONMONOTONE LOCAL MINIMAX METHODS FOR FINDING MULTIPLE SADDLE POINTS

In this paper,by designing a normalized nonmonotone search strategy with the BarzilaiBorwein-type step-size,a novel local minimax method(LMM),which is a globally convergent iterative method,is proposed and analyzed to find multiple(unstable)saddle points of nonconvex functionals in Hilbert spaces.Compared to traditional LMMs with monotone search strategies,this approach,which does not require strict decrease of the objective functional value at each iterative step,is observed to converge faster with less computations.Firstly,based on a normalized iterative scheme coupled with a local peak selection that pulls the iterative point back onto the solution submanifold,by generalizing the Zhang-Hager(ZH)search strategy in the optimization theory to the LMM framework,a kind of normalized ZH-type nonmonotone step-size search strategy is introduced,and then a novel nonmonotone LMM is constructed.Its feasibility and global convergence results are rigorously carried out under the relaxation of the monotonicity for the functional at the iterative sequences.Secondly,in order to speed up the convergence of the nonmonotone LMM,a globally convergent Barzilai-Borwein-type LMM(GBBLMM)is presented by explicitly constructing the Barzilai-Borwein-type step-size as a trial step-size of the normalized ZH-type nonmonotone step-size search strategy in each iteration.Finally,the GBBLMM algorithm is implemented to find multiple unstable solutions of two classes of semilinear elliptic boundary value problems with variational structures:one is the semilinear elliptic equations with the homogeneous Dirichlet boundary condition and another is the linear elliptic equations with semilinear Neumann boundary conditions.Extensive numerical results indicate that our approach is very effective and speeds up the LMMs significantly.

Normalized Wolfe-Powell-type local minimax method for finding multiple unstable solutions of nonlinear elliptic PDEs

The local minimax method(LMM)proposed by Li and Zhou(2001,2002)is an efficient method to solve nonlinear elliptic partial differential equations(PDEs)with certain variational structures for multiple solutions.The steepest descent direction and the Armijo-type step-size search rules are adopted in Li and Zhou(2002)and play a significant role in the performance and convergence analysis of traditional LMMs.In this paper,a new algorithm framework of the LMMs is established based on general descent directions and two normalized(strong)Wolfe-Powell-type step-size search rules.The corresponding algorithm framework,named the normalized Wolfe-Powell-type LMM(NWP-LMM),is introduced with its feasibility and global convergence rigorously justified for general descent directions.As a special case,the global convergence of the NWP-LMM combined with the preconditioned steepest descent(PSD)directions is also verified.Consequently,it extends the framework of traditional LMMs.In addition,conjugate-gradient-type(CG-type)descent directions are utilized to speed up the NWP-LMM.Finally,extensive numerical results for several semilinear elliptic PDEs are reported to profile their multiple unstable solutions and compared with different algorithms in the LMM’s family to indicate the effectiveness and robustness of our algorithms.In practice,the NWP-LMM combined with the CG-type direction performs much better than its known LMM companions.

Exactness of penalization for exact minimax penalty function method in nonconvex programming

The exact minimax penalty function method is used to solve a noncon- vex differentiable optimization problem with both inequality and equality constraints. The conditions for exactness of the penalization for the exact minimax penalty function method are established by assuming that the functions constituting the considered con- strained optimization problem are invex with respect to the same function η （with the exception of those equality constraints for which the associated Lagrange multipliers are negative these functions should be assumed to be incave with respect to η）. Thus, a threshold of the penalty parameter is given such that, for all penalty parameters exceeding this threshold, equivalence holds between the set of optimal solutions in the considered constrained optimization problem and the set of minimizer in its associated penalized problem with an exact minimax penalty function. It is shown that coercivity is not suf- ficient to prove the results.

A Nonmonotone Filter Method for Minimax Problems

In this paper, we propose a modified trust-region filter method algorithm for Minimax problems, which based on the framework of SQP-filter method and associated with the technique of nonmonotone method. We use the SQP subproblem to acquire an attempt step, and use the filter to weigh the effect of the attempt step so as to avoid using penalty function. The algorithm uses the Lagrange function as a merit function and the nonmonotone filter to improve the effect of the algorithm. Under some mild conditions, we prove the global convergence.

Solving constrained minimax problem via nonsmooth equations method

A new nonsmooth equations model of constrained minimax problem was de-rived. The generalized Newton method was applied for solving this system of nonsmooth equations system. A new algorithm for solving constrained minimax problem was established. The local superlinear and quadratic convergences of the algorithm were discussed.

解一类非线性Minimax问题

本文利用区间方法有效地解决了如下一类特殊的非线性minimax问题: F~*= F(x~*)=min max{f_1(y),f_2(y),…,f_m(y)},其中Ω_(x,η)={y|x_i-ηδ_i≤y_i≤x_i+ηδ_i,η≥0,i=1,2,…,n},公差向量δ=(δ_1,δ_2,…,δ_n)~T,δ_i>0,i=1,2,…,n。

基于稀疏约束的低复杂度可变分数时延滤波器

针对基于Farrow结构的可变分数时延(Variable fractional delay,VFD)滤波器需求解大量子滤波器系数这一关键问题,本文将稀疏约束理论引入滤波器的权系数优化中,研究具有稀疏系数的Farrow结构滤波器。在极大极小(Minimax)准则下,通过添加L1正则化约束项改进权系数优化模型,在系数(反)对称性基础上进一步增加系数的稀疏度。然后,采用交替方向乘子法(Alternating direction method of multipliers,ADMM)进行权系数迭代求解。仿真实验表明,本文提出的基于稀疏约束的VFD滤波器在保证高延迟精度的同时,乘法器和加法器分别减少了47.69%和58.60%,极大地降低了系统运算量以及复杂度。

一类无约束离散minimax问题的区间算法

讨论了一类由二阶连续可微函数构成的无约束离散 minimax问题 .通过建立极大函数的区间扩张 ,并将无解区域的凹凸性检验原则和区间 Newton迭代法引入这类特殊不可微优化问题中 ,建立了区间算法 ,证明了算法的收敛性及相关结论 ,给出了数值算例 .用该方法可以同时求出问题的 minimax值和全部 minimax点 。

一个关于流动能量耗散率的minimax变分原理

流动耗散率是湍流理论的核心概念之一.Doering-Constantin变分原理刻画了流动耗散率的上确界(最大值).在该文的研究中,首先基于优化理论的视角,Doering-Constantin的变分原理被改写为一个不可压缩剪切流耗散率的minimax型的变分原理.其次,博弈论中的Kakutanim inimax定理给出该变分原理中minimizing和maximizing计算过程可交换的一个充分条件.这个结果不仅从一个新的角度揭示了谱约束的内涵,也为Doering-Constantin变分原理和Howard-Busse统计理论的等价性从博弈论的角度提供了理论基础.

约束Minimax问题的SQP-Filter算法及收敛性

提出了一个求解带等式和不等式约束的Minimax问题的SQP-Filter算法,每步通过求解2个二次规划子问题来得到搜索方向,并沿该方向做线搜索。该算法避免了较难的罚因子的选取,克服了Maratos效应,并在适当的假设条件下,得到了算法的全局收敛性。

求解约束Minimax问题一种改进的可行下降束方法

通过引入一个新型下降测试准则,提出了求解不等式约束极大极小(Minimax)问题一种改进的可行下降束方法。该方法不仅克服了现有方法下降参数选取的困难,而且简化了理论分析。算法具备全局收敛性,且数值试验结果具有明显优势。

单边相对光滑非凸-凹极小极大问题的镜像梯度算法

本文提出了一种镜像梯度下降梯度上升算法来求解单边相对光滑的非凸-凹极小极大问题。在算法的每次迭代中,我们采用镜像梯度下降步来更新相对光滑的变量,采用梯度上升投影步来更新目标函数中光滑的变量。本文在理论上证明了算法收敛到ε-近似一阶稳定点的迭代复杂度是O(ε^(-4))。

Minimax问题的一个滤子算法

本文提出一个求解不等式约束的Minimax问题的滤子算法,结合序列二次规划方法,并利用滤子以避免罚函数的使用.在适当的条件下,证明了此方法的全局收敛性及超线性收敛性.数值实验表明算法是有效的.

约束Minimax问题的拟可行点方法

将非线性规划的拟下降方法［３］和无约束优化渐近一维精确搜索规则［４］推广到约束Ｍｉｎｉｍａｘ问题，给出二类拟可行点算法模型及相应的较弱的全局收敛性条件．结果扩充了非线性规划解法的使用范围，为构造求解约束Ｍｉｎｉｍａｘ问题的新的有效方法提供统一的理论依据．

多目标minimax问题的极大熵方法及其收敛性

研究了多目标ｍｉｎｉｍａｘ问题的极大熵方法的构成．

求解约束连续型minimax问题的双极大熵函数法

研究了一类带约束连续型minimax问题的数值求解方法,其中目标函数和约束函数都是连续可微的.利用离散型极大熵函数和连续型极大熵函数以及罚函数将带约束连续型minimax问题转化为无约束可微优化问题,建立了基本算法,给出了数值算例,表明算法是可靠和有效的.

Minimax principle on energy dissipation of incompressible shear flow

The energy dissipation rate is an important concept in the theory of turbulence. Doering-Constantin＇s variational principle characterizes the upper bounds （maxi- mum） of the time-averaged rate of viscous energy dissipation. In the present study, an optimization theoretical point of view was adopted to recast Doering-Constantin＇s formu- lation into a minimax principle for the energy dissipation of an incompressible shear flow. Then, the Kakutani minimax theorem in the game theory is applied to obtain a set of conditions, under which the maximization and the minimization in the minimax principle are commutative. The results explain the spectral constraint of Doering-Constantin, and confirm the equivalence between Doering-Constantin＇s variational principle and Howard- Busse＇s statistical turbulence theory.

Nonsmooth Equations of K-T Systems for a Constrained Minimax Problem

Using K-T optimality condition of nonsmooth optimization, we establish two equivalent systems of the nonsmooth equations for the constrained minimax problem directly. Then generalized Newton methods are applied to solve these systems of the nonsmooth equations. Thus a new approach to solving the constrained minimax problem is developed.

解含多个复杂分量函数无约束minimax问题的积极集光滑化算法

本文利用分段三次多项式方程构造了一种积极集策略的二次连续可微的光滑化max函数,给出积极集及稳定的光滑化max函数的计算方法.基于该光滑化max函数,结合Armijo线搜索,负梯度和牛顿方向及光滑化参数的更新策略,给出一种解含多个复杂分量函数无约束minimax问题的积极集光滑化算法.初步的数值实验表明了该算法的有效性.