CONSTRAINT QUALIFICATIONS AND DUAL PROBLEMS FOR QUASI-DIFFERENTIABLE PROGRAMMING

In classical nonlinear programming, it is a general method of developing optimality conditions that a nonlinear programming problem is linearized as a linear programming problem by using first order approximations of the functions at a given feasible point. The linearized procedure for differentiable nonlinear programming problems can be naturally generalized to the quasi differential case. As in classical case so called constraint qualifications have to be imposed on the constraint functions to guarantee that for a given local minimizer of the original problem the nullvector is an optimal solution of the corresponding 'quasilinearized' problem. In this paper, constraint qualifications for inequality constrained quasi differentiable programming problems of type min {f(x)|g(x)≤0} are considered, where f and g are qusidifferentiable functions in the sense of Demyanov. Various constraint qualifications for this problem are presented and a new one is proposed. The relations among these conditions are investigated. Moreover, a Wolf dual problem for this problem is introduced, and the corresponding dual theorems are given.

Global convergent algorithm for the bilevel linear fractional-linear programming based on modified convex simplex method

A global convergent algorithm is proposed to solve bilevel linear fractional-linear programming, which is a special class of bilevel programming. In our algorithm, replacing the lower level problem by its dual gap equaling to zero, the bilevel linear fractional-linear programming is transformed into a traditional sin- gle level programming problem, which can be transformed into a series of linear fractional programming problem. Thus, the modi- fied convex simplex method is used to solve the infinite linear fractional programming to obtain the global convergent solution of the original bilevel linear fractional-linear programming. Finally, an example demonstrates the feasibility of the proposed algorithm.

A DUAL COUPLED METHOD FOR BOUNDARY VALUE PROBLEMS OF PDE WITH COEFFICIENTS OF SMALL PERIOD

In this paper the homogenization method is improved to develop one kind of dual coupled approximate method, which reflects both the macro-scope properties of whole structure and its loadings, and micro-scope configuration properties of composite materials. The boundary value problem of woven membrane is considered, the dual asymptotic expression of the exact solution is given, and its approximation and error estimation are discussed. Finally the numerical example shows the effectiveness of this dual coupled method.

Research on Cell Manufacturing Facility Layout Problem Based on Improved NSGA-II

With the rapid development of the individualized demand market,the demand for manufacturing flexibility has increased over time.As a result,a cell manufacturing system suitable for many varieties and small batches has been produced.With the goal of minimizing the area and logistics handling volume,and considering the arrangement order of facilities and channel constraints,a mathematical model was established,and the problem was solved by improved NSGA-II.After non-dominated sorting,traditional NSGA-II will cross-operate the individuals with the best sorting to generate new individuals.Such a selection strategy is extremely easy to fall into the local optimal solution.The improved NSGA-II is to improve the original selection operation,which is to select the first half of the excellent individuals in the non-dominated sorting into the cross operation,and then select the last sorted ones of the remaining individuals into the cross operation,and combine the best and the worst ones into the cross operation.Finally,an example is given to simulate and improve the solution of NSGA-II and NSGA-II.The simulation results indicate that the improved NSGA-II population shows more obvious diversity,it is easier to jump out of the local optimal solution than NSGA-II,and the satisfactory layout scheme of manufacturing cells is obtained.Therefore,it is more effective to use improved NSGA-II to solve the problem of manufacturing cell layout.

SOME INVERSE OPTIMIZATION PROBLEMS ON NETWORK

In this paper,the authors consider some inverse problems on network,such as the inverse transport problems with gains(IGTP) and the inverse linear fractional minimum cost flow problem(IFFP).Firstly,the authors give the mathematics model of(IGTP) and an efficient method of solving it under l_1 norm;Secondly,taking advantage of the optimality conditions,the authors consider the(IFFP) and give a simple method of solving it.Finally,an numerical example test is also developed.

A NEW DUAL PROBLEM FOR NONDIFFERENTIABLE CONVEX PROGRAMMING

This paper gives a new dual problem for nondifferentiable convex programming and provesthe properties of weak duality and strong duality and offers a necessary and sufficient condition ofstrong duality.

The planar L_(p) dual Minkowski problem

In this paper we study the L_(p) dual Minkowski problem for the case p<0<q.We prove for any positive smooth function f on S^(1),there exists an F:R^(+)→R^(-),such that if F(q)<p<0 or 0<q<-F(-p)then there is a smooth and strictly convex body solving the planar L_(p) dual Minkowski problem.

Load-balance cell association for improving network capacity in heterogeneous cellular networks

Heterogeneous cellular networks improve the spectrum efficiency and coverage of wireless communication networks by deploying low power base station （BS） overlapping the conventional macro cell. But due to the disparity between the transmit powers of the macro BS and the low power BS, cell association strategy developed for the conventional homogeneous networks may lead to a highly unbalanced traffic loading with most of the traffic concentrated on the macro BS. In this paper, we propose a load-balance cell association scheme for heterogeneous cellular network aiming to maximize the network capacity. By relaxing the association constraints, we can get the upper bound of optimal solution and convert the primal problem into a convex optimization problem. Furthermore we propose a Lagrange multipliers based distributed algorithm by using Lagrange dual theory to solve the convex optimization, which converges to an optimal solution with a theoretical performance guarantee. With the proposed algorithm, mobile terminals （MTs） need to jointly consider their traffic type, received signal-to-interference-noise-ratios （SINRs） from BSs, and the load of BSs when they choose server BS. Simulation results show that the load balance between macro and pico BS is achieved and network capacity is improved significantly by our proposed cell association algorithm.