凹面光栅衍射效率测量值的影响因素及补偿方法研究

通过仪器获得的凹面光栅的衍射效率是相对值,其测量值的精度受测量过程和补偿方法的影响。为使效率测量值的测量精度得到进一步提高,有必要对测量值产生影响的主要因素进行深入研究。该文进一步考察了影响衍射效率测量精度的主要因素,针对待测凹面光栅根据待测波长的测试要求进行旋转造成光束截面变化而产生的问题,适当补充原理性方案,并在此基础上推导出了适用于凹面光栅的光束截面变化因子k(θ)的解析表达式。基于影响测量精度因素与理论值之间的非线性关系,提出采用二次非线性回归分析的方法对测量结果进行补偿,给出了提高衍射效率测量精度的补偿公式。实验结果表明,对光栅衍射效率的测量值进行进一步补偿后,使得补偿值与理论值之间的整体误差范围由±2.5%缩小为±0.3%以内,与线性回归分析方法相比,显著缩小了补偿值与理论值之间的差距,进一步保证了衍射效率的准确测量。将补偿过程嵌入测量程序,该方法能够实时补偿测量结果,满足仪器准确测量的要求。     

具有不定位势和凹非线性项的Robin问题的解的多重性

研究非线性项由凹项λ|u|q-2(其中1＜q＜2)和在无穷远处以超线性或渐近线性增长的连续项f(x,u)组成的半线性不定位势的Robin问题,在不同情形下,运用变分法、截断技巧和Morse理论,得到了该问题的多重解的存在性结果.     

Global minimum test problem construction

A method is presented for the construction of test problems for which the global minimum point is known.Given a bounded convex polyhedron inR ⁿ , and a selected vertex, a concave quadratic function is constructed which attains its global minimum at the selected vertex. In general, this function will also have many other local minima.This research was supported in part by NSF Grant MCS 81-01214.     

Penalty for zero–one integer equivalent problem

The equivalence of zero–one integer programming and a concave quadratic penalty function problem has been shown by Raghavachari, for a sufficiently large value of the penalty. A lower bound for this penalty is obtained here, which in specific cases cannot be reduced.This research was supported in part by the Computer Science Section of the National Science Foundation under Research Grant MCS 8101214.     

Multiple positive solutions for a Kirchhoff type problem with a critical nonlinearity

In this paper, we study the multiplicity results of positive solutions for a Kirchhoff type problem with critical growth, with the help of the concentration compactness principle, and we prove that problem admits two positive solutions, and one of the solutions is a positive ground state solution.     

Global Solution Approach for a Nonconvex MINLP Problem in Product Portfolio Optimization

The rigorous and efficient determination of the global solution of a nonconvex MINLP problem arising from product portfolio optimization introduced by Kallrath (2003) is addressed. The objective of the optimization problem is to determine the optimal number and capacity of reactors satisfying the demand and leading to a minimal total cost. Based on the model developed by Kallrath (2003), an improved formulation is proposed, which consists of a concave objective function and linear constraints with binary and continuous variables. A variety of techniques are developed to tighten the model and accelerate the convergence to the optimal solution. A customized branch and bound approach that exploits the special mathematical structure is proposed to solve the model to global optimality. Computational results for two case studies are presented. In both case studies, the global solutions are obtained and proved optimal very efficiently in contrast to available commercial MINLP solvers.     

A generalized karush-kuhn-tucki optimality condition without constraint qualification using tl approximate subdifferential

A generalized Karush-Kuhn-Tucker first order optimality condition is established for an abstract cone-constrained programming problem involving locally Lipschitz functions using the approximate subdifferential. This result is obtained without recourse to a constraint qualification by imposing additional generalized convexity conditions on the constraint functions. A new Fritz John optimality condition is developed as a precursor to the main result. Several examples are provided to illustrate the results along with a discussion of applications to concave minimization problems and to stochastic programming problems with nonsmooth data.     

On Nash–Cournot oligopolistic market equilibrium models with concave cost functions

We consider Nash–Cournot oligopolistic market equilibrium models with concave cost functions. Concavity implies, in general, that a local equilibrium point is not necessarily a global one. We give conditions for existence of global equilibrium points. We then propose an algorithm for finding a global equilibrium point or for detecting that the problem is unsolvable. Numerical experiments on some randomly generated data show efficiency of the proposed algorithm.     

An effective linear approximation method for separable programming problems

Among the numerous applications of piecewise linearization methods include data fitting, network analysis, logistics, and statistics. In the early 1950s, a concave function was found to be able to be linearized by introducing 0-1 variables. Most textbooks in Operations Research offer such methods for expressing linear approximations. Various methods of linearization have also been developed in recent literature. Nevertheless, the transformed linear scheme has a severe shortcoming: most standard procedures for linearizing typically involve a large increase in the number of binary variables. Consequently, the gains to be derived from dealing with linear functions are quite likely to be nullified by the increase in the size of the problem.Conventional methods for linearizing a concave function with m break points require m-1 binary variables. However, when m becomes large, the computation will be very time-consuming and may cause a heavy computational burden.This study proposes an effective approach in which only ⌈log₂(m-1)⌉ binary variables are used. The proposed method has the following features: (i) it offers more convenient and efficient means of expressing a piecewise linear function; (ii) fewer 0-1 variables are used; (iii) the computational results show that the proposed method is much more efficient and faster than the conventional one, especially when the number of break points becomes large.