MERIT FUNCTION AND GLOBAL ALGORITHM FOR BOX CONSTRAINED VARIATIONAL INEQUALITIES

1 IntroductionWe consider tlie variational inequality problelll, deuoted by VIP(X, F), wliicli is to find avector x* E X such thatF(X*)"(X -- X-) 2 0, VX E X, (1)where F: R" - R" is any vector-valued f11uction and X is a uonelllpty subset of R'.This problem has important applicatiolls. in equilibriun1 modeIs arising in fields such asecououtics, transportatioll scieuce alld operations research. See [1]. There exist mauy lllethodsfor solviug tlie variational li1equality problem VIP(X. …     

基于光滑化方法求解非线性l1问题

讨论了求解非线性l1问题的一种新的光滑函数法.通过对非线性l1问题模型的转化,将该问题化为一个不可微优化问题,据此提出了基于BFGS迭代的非线性l1问题的光滑函数法,介绍了非线性l1问题的光滑函数的有关性质、算法步骤及其收敛性.数值仿真显示了提出的光滑函数方法可以避免数值计算的溢出,具有一定的有效性.     

Value-Estimation Function Method for Constrained Global Optimization

A novel value-estimation function method for global optimization problems with inequality constraints is proposed in this paper. The value-estimation function formulation is an auxiliary unconstrained optimization problem with a univariate parameter that represents an estimated optimal value of the objective function of the original optimization problem. A solution is optimal to the original problem if and only if it is also optimal to the auxiliary unconstrained optimization with the parameter set at the optimal objective value of the original problem, which turns out to be the unique root of a basic value-estimation function. A logarithmic-exponential value-estimation function formulation is further developed to acquire computational tractability and efficiency. The optimal objective value of the original problem as well as the optimal solution are sought iteratively by applying either a generalized Newton method or a bisection method to the logarithmic-exponential value-estimation function formulation. The convergence properties of the solution algorithms guarantee the identification of an approximate optimal solution of the original problem, up to any predetermined degree of accuracy, within a finite number of iterations.     

Duality in nonlinear programs using augmented Lagrangian functions

A generally nonconvex optimization problem with equality constraints is studied. The problem is introduced as an “inf sup” of a generalized augmented Lagrangian function. A dual problem is defined as the “sup inf” of the same generalized augmented Lagrangian. Sufficient conditions are derived for constructing the augmented Lagrangian function such that the extremal values of the primal and dual problems are equal. Characterization of a class of augmented Lagrangian functions which satisfy the sufficient conditions for strong duality is presented. Finally, some examples of functions and primal-dual problems in the above-mentioned class are presented.     

基于不等式约束的一类新的增广lagrangian函数

本文提出了一类新的增广lagrangian函数,并证明了它的稳定点、整体极小点与原约束问题KKT点、整体极小点有1-1对应关系,增广lagrangian函数的局部极小点为原问题的局部极小点.     

Elimination of bounds in optimization problems by transforming variables

The problem of minimizing a nonlinear objective function ofn variables, with continuous first and second partial derivatives, subject to nonnegativity constraints or upper and lower bounds on the variables is studied. The advisability of solving such a constrained optimization problem by making a suitable transformation of its variables in order to change the problem into one of unconstrained minimization is considered. A set of conditions which guarantees that every local minimum of the new unconstrained problem also satisfies the first-order necessary (Kuhn—Tucker) conditions for a local minimum of the original constrained problem is developed. It is shown that there are certain conditions under which the transformed objective function will maintain the convexity of the original objective function in a neighborhood of the solution. A modification of the method of transformations which moves away from extraneous stationary points is introduced and conditions under which the method generates a sequence of points which converges to the solution at a superlinear rate are given.     

Torsion of prismatic elastic bodies containing screw dislocations

The problem of the stressed state of a prismatic anisotropic rod containing screw dislocations, the axes of which are parallel to the rod axis, is considered. Such defects may arise during the growth of filamentary crystals (metal “whiskers”), and may also exist in multiply connected cylindrical structures. The torsion of an anisotropic elastic bar with a multiply connected cross-section is investigated initially, assuming that the stresses and strains are single-valued but dispensing with the requirement that the warping function should be single-valued. The boundary-value problem is formulated in terms of the Prandtl stress function, which, unlike the warping function, is single-valued in a multiply connected region. A variational formulation of the boundary-value problem for the stress function is given. From the variational principle obtained a torsion boundary-value problem is formulated when there are lumped or continuously distributed dislocations. A modification of the membrane analogy for the torsion problem is proposed which takes into account the presence of dislocations. General theorems of the theory of the torsion of a rod containing dislocations are formulated. An effective formula is derived for the angle of torsion of a bar due to a specified dislocation distribution. Problems on dislocations in a thin-walled rod and a rectangular anisotropic bar are solved.     

A Global Regularization Method for Solving the Finite Min-Max Problem

A method is presented for solving the finite nonlinear min-max problem. Quasi-Newton methods are used to approximately solve a sequence of differentiable subproblems where, for each subproblem, the cost function to minimize is a global regularization underestimating the finite maximum function. Every cluster point of the sequence generated is shown to be a stationary point of the min-max problem and therefore, in the convex case, to be a solution of the problem. Moreover, numerical results are given for a large set of test problems which show that the method is efficient in practice.     

A Certain Inversion Problem for the Ray Transform with Incomplete Data

The problem is considered of reconstructing a function in three-dimensional space from its integrals over a family of straight lines that are generators of cones, i.e., an inversion problem for the ray transform with incomplete data. This problem is connected with an auxiliary problem of analytic continuation and is strongly ill-posed. A uniqueness theorem and conditional stability estimates for a solution are obtained.     

One-machine job-scheduling with non-constant capacity — Minimizing weighted completion times

In this paper an n-job one-machine scheduling problem is considered, in which the machine capacity is time-dependent and jobs are characterized by their work content. The objective is to minimize the sum of weighted completion times. A necessary optimality condition is presented and we discuss some special cases where this condition is also sufficient. We prove that the problem is NP-complete. A branch-and-bound algorithm is developed for the case when the capacity function is a step function. Computational results for 1000 test problems are presented.     

Identification of a dissipation coefficient by a variational method

A generalized inverse problem for the identification of the absorption coefficient for a hyperbolic system is considered. The well-posedness of the problem is examined. It is proved that the regular part of the solution is an L ₂ function, which reduces the inverse problem to minimizing the error functional. The gradient of the functional is determined in explicit form from the adjoint problem, and approximate formulas for its calculation are derived. A regularization algorithm for the solution of the inverse problem is considered. Numerical results obtained for various excitation sources are displayed.     

Sensitivity function: Properties and applications

The sensitivity function induced by a convex programming problem is examined. Its monotonicity, subdifferentiability, and closure properties are analyzed. A relation to the Pareto optimal solution set of the multicriteria convex optimization problem is established. The role of the sensitivity function in systems describing optimization problems is clarified. It is shown that the solution of these systems can often be reduced to the minimization of the sensitivity function on a convex set. Numerical methods for solving such problems are proposed, and their convergence is proved.     

Directional differentiability of the optimal value function in convex semi-infinite programming

In this paper, directional differentiability properties of the optimal value function of a parameterized semi-infinite programming problem are studied. It is shown that if the unperturbed semi-infinite programming problem is convex, then the corresponding optimal value function is directionally differentiable under mild regularity assumptions. A max-min formula for the directional derivatives, well-known in the finite convex case, is given.     

线性比式和规划问题的输出空间分支定界算法

本文旨在针对线性比式和规划这一NP-Hard非线性规划问题提出新的全局优化算法.首先,通过引入p个辅助变量把原问题等价的转化为一个非线性规划问题,这个非线性规划问题的目标函数是乘积和的形式并给原问题增加了p个新的非线性约束,再通过构造凸凹包络的技巧对等价问题的目标函数和约束条件进行相应的线性放缩,构成等价问题的一个下界线性松弛规划问题,从而提出了一个求解原问题的分支定界算法,并证明了算法的收敛性.最后,通过数值结果比较表明所提出的算法是可行有效的.     

Modified Ratio Objective Approach in Mathematical Programming

A new approach to obtaining the optimality conditions for fractional mathematical programming problems involving one objective ratio in the objective function is considered. Using this approach, an equivalent optimization problem is constructed by a modification of the single-ratio objective function in the fractional programming problem. Furthermore, an η-Lagrange function is introduced for a constructed optimization problem and modified saddle-point results are presented.     

A discrete filled function algorithm embedded with continuous approximation for solving max-cut problems

In this paper, a discrete filled function algorithm embedded with continuous approximation is proposed to solve max-cut problems. A new discrete filled function is defined for max-cut problems, and properties of the function are studied. In the process of finding an approximation to the global solution of a max-cut problem, a continuation optimization algorithm is employed to find local solutions of a continuous relaxation of the max-cut problem, and then global searches are performed by minimizing the proposed filled function. Unlike general filled function methods, characteristics of max-cut problems are used. The parameters in the proposed filled function need not to be adjusted and are exactly the same for all max-cut problems that greatly increases the efficiency of the filled function method. Numerical results and comparisons on some well known max-cut test problems show that the proposed algorithm is efficient to get approximate global solutions of max-cut problems.     

A modified objective function method for solving nonlinear multiobjective fractional programming problems

A new method is used for solving nonlinear multiobjective fractional programming problems having V-invex objective and constraint functions with respect to the same function η. In this approach, an equivalent vector programming problem is constructed by a modification of the objective fractional function in the original nonlinear multiobjective fractional problem. Furthermore, a modified Lagrange function is introduced for a constructed vector optimization problem. By the help of the modified Lagrange function, saddle point results are presented for the original nonlinear fractional programming problem with several ratios. Finally, a Mond-Weir type dual is associated, and weak, strong and converse duality results are established by using the introduced method with a modified function. To obtain these duality results between the original multiobjective fractional programming problem and its original Mond-Weir duals, a modified Mond-Weir vector dual problem with a modified objective function is constructed.     

Coefficient identification in Euler-Bernoulli equation from over-posed data

A method for solving the inverse problem for coefficient identification in the Euler-Bernoulli equation from over-posed data is presented. The original inverse problem is replaced by a minimization problem. The method is applied to the problem for identifying the coefficient in the case when it is a piece-wise polynomial function. Several examples are elaborated and the numerical results confirm that the solution of the imbedding problem coincides with the direct simulation of the original problem within the second order of approximation.     

总体优化一类双参数填充函数算法的改进

求解无约束总体优化问题的一类双参数填充函数算法需要假设该问题的局部极小解的个数只有有限个，而且填充函数中参数的选取与局部极小解的谷域的半径有关．该文对其填充函数作了适当改进，使得新的填充函数算法不仅无需对问题的局部极小解的个数作假设，而且填充函数中参数的选取与局部极小解的谷域的半径无关．数值试验表明算法是有效的．     

Students’ quantitative reasoning about an absolute extrema optimization problem in a profit maximization context

A relative extrema optimization problem is one in which the domain of the objective function (i.e. the function whose maximum or minimum value is to be found) is an open interval. An absolute extrema optimization problem is one in which the domain of the objective function is a closed interval. Analysis of task-based interviews conducted with 12 pairs of business calculus students while reasoning about an absolute extrema optimization problem situated in a profit maximization context revealed that solving this task was particularly difficult for a majority of the students. Specifically, setting up the objective function, interpreting critical numbers and extrema, verifying extrema, and distinguishing between relative extrema and absolute extrema was problematic for some of the students. Implications for instruction are included.