近似平衡约束向量优化问题解集的上Painlevé-Kuratowski收敛性

主要研究近似平衡约束向量优化问题解集的稳定性.考虑了一类近似平衡约束向量优化问题,在目标函数列Gamma-收敛,而不必是连续收敛情形下,分别获得近似平衡约束向量优化问题有效解集、弱有效解集和强有效解集的上Painlevé-Kuratowski收敛性,所得结果推广和改进了最近文献的结果.     

低阶精确罚函数的一种二阶光滑逼近

给出了求解约束优化问题的低阶精确罚函数的一种二阶光滑逼近方法,证明了光滑后的罚优化问题的最优解是原约束优化问题的ε-近似最优解,基于光滑后的罚优化问题,提出了求解约束优化问题的一种新的算法,并证明了该算法的收敛性,数值例子表明该算法对于求解约束优化问题是有效的.     

非负正交约束优化问题的理论、算法及应用

非负正交约束优化问题是同时带有非负约束和正交约束的优化问题,该类问题在机器学习和数据科学中有着重要的应用。常见的非负正交约束优化问题包括二次指派问题、图匹配问题、非负正交矩阵分解问题、非负主成分分析和K-指示模型等。由于非负约束和正交约束的共同作用,该类问题具有一定的组合结构,一般是NP-难的。本文主要介绍非负正交约束优化问题的基本理论性质、求解算法以及相关的应用模型。     

广义凸空间内的拟平衡问题和约束多目标对策

在没有线性结构的广义凸空间内研究了一类拟平衡问题和一类约束我目标对策。首先在非紧广义凸空间内对拟平衡问题证明了两个解的存在性定理。然后作为拟平衡存在定理的应用,在广义凸空间内对约束多目标对策建立了几个加权Nash-平衡和帕雷多平衡存在定理。这些定理改进、统一和推广了最近文献中多目标对策的相应结果。     

一般互补约束问题的一种松弛逐步二次规划算法北大核心CSCD

1 引言 互补约束问题（简称MPCC）是一类具有特殊约束条件的约束最优化问题．不同于一般约束优化问题,其基本约束条件不仅包含等式约束和不等式约束,而且还包含比较复杂的互补约束．MPCC的一般形式如下：     

互补约束优化问题的乘子序列部分罚函数算法

利用互补问题的Lagrange函数,将互补约束优化问题(MPCC)转化为含参数的约束优化问题.给出Lagrange乘子的简单修正公式,并给出求解互补约束优化问题的部分罚函数法.无须假设二阶必要条件成立,只要算法产生的迭代点列的极限点满足互补约束优化问题的线性独立约束规范(MPCC-LICQ),且极限点是MPCC的可行点...     

约束优化问题中常用的约束规范及其相互关系

详细分析了约束优化问题中几种常见的约束规范,如L ICQ,SM FCQ,M FCQ,CRCQ,CPLD以及伪正规,拟正规和拟正则约束规范.针对等式和不等式约束问题讨论了它们与拉格朗日乘子的存在性及其性质之间的关系,给出了各种约束规范之间的关系图.特别通过反例,说明了WM FCQ在含等式约束的问题中不是一种约束规范.     

解有界变量凸规划问题的ODE方法

用微分方程的解曲线确定约束优化问题的解即ODE方法已受到人们广泛重视和研究.潘平奇对无约束和带等式约束优化问题提出了很好的ODE方法.该方法的主要优点之一是没有扩大问题的规模.关于带不等式约束的优化问题的ODE方法,尚待研究.另外,虽然问题(1)可以通过标准化处理变成等式约束情形,再用[3]中的ODE方法求解,但这样做会扩大问题规模,因此,本文将在不扩大问题规模的基础上     

存零约束优化问题的对偶问题

本文研究了近年提出的一类新优化问题存零约束优化问题,因存零约束的存在,使得求解最优解较困难.因此,本文针对存零约束优化问题,利用对偶理论提出了问题的Wolfe型对偶模型.在凸性和严格凸性假设下,获得了Wolfe对偶的弱、强、逆、限制逆和严格逆对偶结果.并进行了实例论证.     

平衡问题解的存在性

主要研究平衡问题解的存在性.通过对目标函数和可行集合的渐近分析,给出拟单调平衡问题解集非空的条件.进而用类似的方法研究了向量平衡问题解存在的条件,并将其应用到向量优化问题上.     

Mathematical Programming with System of Equilibrium Constraints

In this paper, we study the mathematical program with system of equilibrium constraints. This problem contains bilevel program with system of equilibrium constraints, semi-infinite program with system of equilibrium constraints, mathematical program with Nash equilibrium constraints, mathematical program with system of mixed variational like inequalities constraints. We establish the existence theorems of mathematical program with system of equilibrium constraints under various assumptions.     

Minimal representation of quadratically constrained convex feasible regions

In this paper we are concerned with characterizing minimal representations of feasible regions defined by both linear and convex quadratic constraints. We say that representation is minimal if every other representation has either more quadratic constraints, or has the same number of quadratic constraints and at least as many linear constraints. We will prove that a representation is minimal if and only if it contains no redundant constraints, no pseudo-quadratic constraints and no implicit equality constraints. We define a pseudo-quadratic constraint as a quadratic constraint that can be replaced by a finite number of linear constraints. In order to prove the minimal representation theorem, we also prove that if the surfaces of two quadratic constraints match on a ball, then they match everywhere.In this paper we also provide algorithms that can be used to detect implicit equalities and pseudoquadratic constraints. The redundant constraints can be identified using the hypersphere directions (HD) method.Corresponding author.     

An Exact Penalty Function Method for Continuous Inequality Constrained Optimal Control Problem

In this paper, we consider a class of optimal control problems subject to equality terminal state constraints and continuous state and control inequality constraints. By using the control parametrization technique and a time scaling transformation, the constrained optimal control problem is approximated by a sequence of optimal parameter selection problems with equality terminal state constraints and continuous state inequality constraints. Each of these constrained optimal parameter selection problems can be regarded as an optimization problem subject to equality constraints and continuous inequality constraints. On this basis, an exact penalty function method is used to devise a computational method to solve these optimization problems with equality constraints and continuous inequality constraints. The main idea is to augment the exact penalty function constructed from the equality constraints and continuous inequality constraints to the objective function, forming a new one. This gives rise to a sequence of unconstrained optimization problems. It is shown that, for sufficiently large penalty parameter value, any local minimizer of the unconstrained optimization problem is a local minimizer of the optimization problem with equality constraints and continuous inequality constraints. The convergent properties of the optimal parameter selection problems with equality constraints and continuous inequality constraints to the original optimal control problem are also discussed. For illustration, three examples are solved showing the effectiveness and applicability of the approach proposed.     

On the properties of solutions to the Goursat-Darboux problem with boundary and distributed controls

We consider a control system described by the Goursat-Darboux equation. The system is controlled by distributed and boundary controls. The controls are subject to the constraints given as multivalued mappings with closed, possibly nonconvex, values depending on the phase variable. Alongside the initial constraints, we consider the convexified constraints and the constraints whose values are the extreme points of the convexified constraints. We study the questions of existence of solutions and establish connections between the solutions under various constraints.     

Partial differential equations with differential constraints

A geometric setting for constrained exterior differential systems on fibered manifolds with n-dimensional bases is proposed. Constraints given as submanifolds of jet bundles (locally defined by systems of first-order partial differential equations) are shown to carry a natural geometric structure, called the canonical distribution. Systems of second-order partial differential equations subjected to differential constraints are modeled as exterior differential systems defined on constraint submanifolds. As an important particular case, Lagrangian systems subjected to first-order differential constraints are considered. Different kinds of constraints are introduced and investigated (Lagrangian constraints, constraints adapted to the fibered structure, constraints arising from a (co)distribution, semi-holonomic constraints, holonomic constraints).     

用非线性方程组求解等式约束非线性规划问题的降维算法

本文研究线性和非线性等式约束非线性规划问题的降维算法.首先,利用一般等式约束问题的降维方法,将线性等式约束非线性规划问题转换成一个非线性方程组,解非线性方程组即得其解;然后,对线性和非线性等式约束非线性规划问题用Lagrange乘子法,将非线性约束部分和目标函数构成增广的Lagrange函数,并保留线性等式约束,这样便得到一个线性等式约束非线性规划序列,从而,又将问题转化为求解只含线性等式约束的非线性规划问题.     

Modeling disjunctive constraints with a logarithmic number of binary variables and constraints

Many combinatorial constraints over continuous variables such as SOS1 and SOS2 constraints can be interpreted as disjunctive constraints that restrict the variables to lie in the union of a finite number of specially structured polyhedra. Known mixed integer binary formulations for these constraints have a number of binary variables and extra constraints linear in the number of polyhedra. We give sufficient conditions for constructing formulations for these constraints with a number of binary variables and extra constraints logarithmic in the number of polyhedra. Using these conditions we introduce mixed integer binary formulations for SOS1 and SOS2 constraints that have a number of binary variables and extra constraints logarithmic in the number of continuous variables. We also introduce the first mixed integer binary formulations for piecewise linear functions of one and two variables that use a number of binary variables and extra constraints logarithmic in the number of linear pieces of the functions. We prove that the new formulations for piecewise linear functions have favorable tightness properties and present computational results showing that they can significantly outperform other mixed integer binary formulations.     

Efficient approximate linear programming for factored MDPs

Factored Markov Decision Processes (MDPs) provide a compact representation for modeling sequential decision making problems with many variables. Approximate linear programming (LP) is a prominent method for solving factored MDPs. However, it cannot be applied to models with large treewidth due to the exponential number of constraints. This paper proposes a novel and efficient approximate method to represent the exponentially many constraints. We construct an augmented junction graph from the factored MDP, and represent the constraints using a set of cluster constraints and separator constraints, where the cluster constraints play the role of reducing the number of constraints, and the separator constraints enforce the consistency of neighboring clusters so as to improve the accuracy. In the case where the junction graph is tree-structured, our method provides an equivalent representation to the original constraints. In other cases, our method provides a good trade-off between computation and accuracy. Experimental results on different models show that our algorithm performs better than other approximate linear programming algorithms on computational cost or expected reward.     

An Analysis of the Role of Positivity and Mixture Model Constraints in Poisson Deconvolution Problems

We consider a class of estimation problems in which data of a Poisson character are related by a linear model to a target function that satisfies certain physical constraints. The classic example of this situation is the reconstruction problem of positron emission tomography (PET). There the function of interest satisfies positivity constraints. This article examines the impact of such constraints by comparing simple unconstrained reconstruction methods with constrained alternatives based on maximum likelihood (ML) and least squares (LS) formulations. Data from a series of numerical experiments are presented to quantify the significance of constraints. Although these experiments show that constraints are important, the differences between ML and LS based implementations of constraints are quite small. Thus, in order to evaluate the impact of constraints, it appears to be sufficient to focus on comparing constrained versus unconstrained implementations of LS. This simplifies the analysis of constraints considerably. A perturbation analysis technique is proposed to summarize the impact of constraints in terms of a single relative efficiency measure. The predictions obtained by this analysis are found to be in good agreement with experimental data.     

Binary,unrelaxable and hidden constraints in blackbox optimization

This work proposes strategies to handle three types of constraints in the context of blackbox optimization: binary constraints that simply indicate if they are satisfied or not; unrelaxable constraints that are required to be satisfied to trust the output of the blackbox; hidden constraints that are not explicitly known by the user but are triggered unexpectedly. Using tools from classification theory, we build surrogate models of those constraints to guide the Mads algorithm. Numerical results are conducted on three engineering problems.