二次规划逆问题的牛顿方法

针对二次规划逆问题,将其表达为带有互补约束的锥约束优化问题.借助于对偶理论,将问题转化为变量更少的线性互补约束非光滑优化问题.通过扰动的方法求解转化后的问题并证明了收敛性.采用非精确牛顿法求解扰动问题,给出了算法的全局收敛性与局部二阶收敛速度.最后通过数值实验验证了该算法的可行性.     

线性互补问题与多目标优化

本文研究了求解线性互补问题的一类新方法:把线性互补问题转化为多目标优化问题,利用多目标优化有效解的定义,给出了零有效解的概念;进而获得多目标优化问题的零有效解就是线性互补问题的最优解.最后给出了有解、无解线性互补问题,并分别把这些问题转化为多目标优化,采用极大极小方法求解转化后的多目标优化问题.数值实验结果表明了该方法的正确性和有效性,完善了文献[19]的数值结果.     

一类半定二次规划逆问题

本文求解了一类半定二次规划的逆问题.具体可描述为在保证一个可行的解是原半定二次规划问题的最优解的前提下,使目标函数中的参数以及约束条件中右端项参数与它们的估计值的距离最小.我们将该逆问题转换为具有线性约束和半正定锥互补约束的问题.再利用对偶理论,又将上述问题转化成只有半正定锥互补约束的问题,但此时也是一个难问题,通过引入一个非光滑的惩罚函数来惩罚互补约束,进而将原问题转化为一个DC问题.再采用序列凸规划方法来求解它,同时给出惩罚方法以及序列凸规划方法的收敛性分析.最后的数值实验表明我们采用的方法对于本文提出的问题求解还是非常有效的.     

一类广义多项式互补问题的光滑化共轭梯度法

本文研究了一类广义多项式互补问题,在一定条件下,证明了其有唯一解.通过极大极小转化技术,将此类广义多项式互补问题转化为光滑化无约束优化问题进行求解,并提出了一种新的光滑化共轭梯度法.在一定假设条件下,证明了该方法的全局收敛性.最后相关的数值实验表明了算法可以有效求解广义多项式互补问题.     

下层为凸标量优化的二层多目标规划问题的光滑化方法

以下层问题的最优性条件代替下层问题,将下层为凸标量优化的一类二层多目标规划问题转化为带互补约束的不可微多目标规划问题,采用扰动的Fischer-Burmeister函数对互补约束光滑化,得到了相应的光滑化多目标规划问题,分析了原问题的有效解与光滑化多目标规划问题有效解的关系,设计了求解该类二层多目标规划问题的光滑化算法,并分析了算法的收敛性.数值结果表明该光滑化方法是可行的.     

范数下无容量限制设施选址逆问题的求解方法

一个优化问题的逆问题是这样一类问题,在给定该优化问题的一个可行解时,通过最小化目标函数中参数的改变量(在某个范数下)使得该可行解成为改变参数后的该优化问题的最优解。对于本是NP-难问题的无容量限制设施选址问题,证明了其逆问题仍是NP-难的。研究了使用经典的行生成算法对无容量限制设施选址的逆问题进行计算,并给出了求得逆问题上下界的启发式方法。两种方法分别基于对子问题的线性松弛求解给出上界和利用邻域搜索以及设置迭代循环次数的方式给出下界。数值结果表明线性松弛法得到的上界与最优值差距较小,但求解效率提升不大;而启发式方法得到的下界与最优值差距极小,极大地提高了求解该逆问题的效率。     

线性互补约束规划问题的一种Filter算法

本文研究了求解线性互补约束规划问题的算法问题.首先基于广义互补函数和摄动技术将问题转化为带参数的非线性优化问题,利用SlQP-Filter算法方法,求解线性互补约束规划问题的一种Filter算法.在适当条件下,证明了该算法的全局收敛性.     

线性变参数系统事件触发插值预测控制

针对带有不确定性有界扰动的线性变参数约束系统,提出了一种事件触发插值模型预测控制方法.引入插值策略令优化问题的解将表现为当前时刻系统预设的反馈控制律的参数化形式,以降低优化问题求解变量的数量进而减少系统的计算负担.通过将来自系统调度参数变化的乘性扰动和每一时刻输入的加性扰动叠加构建有限步紧缩约束集,求解名义系统的优化问题得到最优解,并以名义系统和实际系统出现足够大的偏差作为触发条件,与插值系数和鲁棒约束集相关的触发阈值将在线计算.证明了所提出方法的递归可行性以及区域输入-状态稳定性,最后以一个数值例子验证了该方法.     

求解互补问题的一种序列二次规划方法

通过将互补问题转化为一种带非负约束的极小化问题 ,给出了求解互补问题的一种序列二次规划方法 .该方法中每一个子问题都是可解的 ,迭代产生的序列是非负的 ,在适当的条件下 ,分别证明了算法的全局收敛性、局部超线收敛性以及局部二次收敛性 .     

求线性二层规划∈-全局最优解的一种方法

本文研究了线性二层规划问题.利用下层问题的KKT最优性条件将其转化为一个具有互补约束的数学规划问题,提出了一种新的求解方法.该方法仅仅需要求解若干个双线性规划问题,便可以获得原问题的∈-全局最优解.最后,通过一个算例说明了所提出方法的可行性.     

A Perturbation approach for an inverse quadratic programming problem

We consider an inverse quadratic programming (QP) problem in which the parameters in both the objective function and the constraint set of a given QP problem need to be adjusted as little as possible so that a known feasible solution becomes the optimal one. We formulate this problem as a linear complementarity constrained minimization problem with a positive semidefinite cone constraint. With the help of duality theory, we reformulate this problem as a linear complementarity constrained semismoothly differentiable (SC¹) optimization problem with fewer variables than the original one. We propose a perturbation approach to solve the reformulated problem and demonstrate its global convergence. An inexact Newton method is constructed to solve the perturbed problem and its global convergence and local quadratic convergence rate are shown. As the objective function of the problem is a SC¹ function involving the projection operator onto the cone of positively semi-definite symmetric matrices, the analysis requires an implicit function theorem for semismooth functions as well as properties of the projection operator in the symmetric-matrix space. Since an approximate proximal point is required in the inexact Newton method, we also give a Newton method to obtain it. Finally we report our numerical results showing that the proposed approach is quite effective.     

A new smoothing scheme for mathematical programs with complementarity constraints

In this paper, we consider a mathematical program with complementarity constraints (MPCC). We present a new smoothing scheme for this problem, which makes the primal structure of the complementarity part unchanged mostly. For the new smoothing problem, we show that the linear independence constraint qualification (LICQ) holds under some conditions. We also analyze the convergence behavior of the smoothing problem, and get some sufficient conditions such that an accumulation point of stationary points of the smoothing problems is C (M, B)-stationarity respectively. Based on the smoothing problem, we establish an algorithm to solve the primal MPCC problem. Some numerical experiments are given in the paper.     

A majorization–minimization algorithm for split feasibility problems

The classical multi-set split feasibility problem seeks a point in the intersection of finitely many closed convex domain constraints, whose image under a linear mapping also lies in the intersection of finitely many closed convex range constraints. Split feasibility generalizes important inverse problems including convex feasibility, linear complementarity, and regression with constraint sets. When a feasible point does not exist, solution methods that proceed by minimizing a proximity function can be used to obtain optimal approximate solutions to the problem. We present an extension of the proximity function approach that generalizes the linear split feasibility problem to allow for non-linear mappings. Our algorithm is based on the principle of majorization–minimization, is amenable to quasi-Newton acceleration, and comes complete with convergence guarantees under mild assumptions. Furthermore, we show that the Euclidean norm appearing in the proximity function of the non-linear split feasibility problem can be replaced by arbitrary Bregman divergences. We explore several examples illustrating the merits of non-linear formulations over the linear case, with a focus on optimization for intensity-modulated radiation therapy.     

A log-exponential smoothing method for mathematical programs with complementarity constraints

In this paper a log-exponential smoothing method for mathematical programs with complementarity constraints (MPCC) is analyzed, with some new interesting properties and convergence results provided. It is shown that the stationary points of the resulting smoothed problem converge to the strongly stationary point of MPCC, under the linear independence constraint qualification (LICQ), the weak second-order necessary condition (WSONC), and some reasonable assumption. Moreover, the limit point satisfies the weak second-order necessary condition for MPCC. A notable fact is that the proposed convergence results do not restrict the complementarity constraint functions approach to zero at the same order of magnitude.     

Generalized linear complementarity problems treated without fixed-point theory

We study the (monotone) linear complementarity problem in reflexive Banach space. The problem is treated as a quadratic program and shown to satisfy appropriate constraint qualifications. This leads to a theory of the generalized monotone linear complementarity problem which is independent of Brouwer's fixed-point theorem. Certain related results on linear systems are given. The final section concerns copositive operators.This research was partially supported by NSERC Grant No. A-5516.The author thanks the referee for his painstaking and thorough comments on this paper.     

The augmented Lagrangian method for a type of inverse quadratic programming problems over second-order cones

The focus of this paper is on studying an inverse second-order cone quadratic programming problem, in which the parameters in the objective function need to be adjusted as little as possible so that a known feasible solution becomes the optimal one. We formulate this problem as a minimization problem with cone constraints, and its dual, which has fewer variables than the original one, is a semismoothly differentiable (SC ¹) convex programming problem with both a linear inequality constraint and a linear second-order cone constraint. We demonstrate the global convergence of the augmented Lagrangian method with an exact solution to the subproblem and prove that the convergence rate of primal iterates, generated by the augmented Lagrangian method, is proportional to 1/r, and the rate of multiplier iterates is proportional to $1/\sqrt{r}$ , where r is the penalty parameter in the augmented Lagrangian. Furthermore, a semismooth Newton method with Armijo line search is constructed to solve the subproblems in the augmented Lagrangian approach. Finally, numerical results are reported to show the effectiveness of the augmented Lagrangian method with both an exact solution and an inexact solution to the subproblem for solving the inverse second-order cone quadratic programming problem.     

Robustness and nondegenerateness for linear complementarity problems

In this paper the main focus is on a stability concept for solutions of a linear complementarity problem. A solution of such a problem is robust if it is stable against slight perturbations of the data of the problem. Relations are investigated between the robustness, the nondegenerateness and the isolatedness of solutions. It turns out that an isolated nondegenerate solution is robust and also that a robust nondegenerate solution is isolated. Since the class of linear complementarity problems with only robust solutions or only nondegenerate solutions is not an open set, attention is paid to Garcia's classG _n of linear complementarity problems. The nondegenerate problems inG _n form an open set.     

Applying a Power Penalty Method to Numerically Pricing American Bond Options

In this paper, we aim to develop a numerical scheme to price American options on a zero-coupon bond based on a power penalty approach. This pricing problem is formulated as a variational inequality problem (VI) or a complementarity problem (CP). We apply a fitted finite volume discretization in space along with an implicit scheme in time, to the variational inequality problem, and obtain a discretized linear complementarity problem (LCP). We then develop a power penalty approach to solve the LCP by solving a system of nonlinear equations. The unique solvability and convergence of the penalized problem are established. Finally, we carry out numerical experiments to examine the convergence of the power penalty method and to testify the efficiency and effectiveness of our numerical scheme.     

New Relaxation Method for Mathematical Programs with Complementarity Constraints

In this paper, we present a new relaxation method for mathematical programs with complementarity constraints. Based on the fact that a variational inequality problem defined on a simplex can be represented by a finite number of inequalities, we use an expansive simplex instead of the nonnegative orthant involved in the complementarity constraints. We then remove some inequalities and obtain a standard nonlinear program. We show that the linear independence constraint qualification or the Mangasarian–Fromovitz constraint qualification holds for the relaxed problem under some mild conditions. We consider also a limiting behavior of the relaxed problem. We prove that any accumulation point of stationary points of the relaxed problems is a weakly stationary point of the original problem and that, if the function involved in the complementarity constraints does not vanish at this point, it is C-stationary. We obtain also some sufficient conditions of B-stationarity for a feasible point of the original problem. In particular, some conditions described by the eigenvalues of the Hessian matrices of the Lagrangian functions of the relaxed problems are new and can be verified easily. Our limited numerical experience indicates that the proposed approach is promising.