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Using constraint partitioning and variable elimination, the authors have recently developed an efficient algorithm for solving linear goal programming problems. However, many goal programs require some or all of the decision variables to be integer valued. This paper shows how the new partitioning algorithm can be extended with a modified branch and bound strategy to solve both pure and mixed type integer goal programming problems. A potential problem in combining the partitioning algorithm and the branch and bound search scheme is presented and resolved. 相似文献
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This paper is concerned with a portfolio optimization problem under concave and piecewise constant transaction cost. We formulate
the problem as nonconcave maximization problem under linear constraints using absolute deviation as a measure of risk and
solve it by a branch and bound algorithm developed in the field of global optimization. Also, we compare it with a more standard
0–1 integer programming approach. We will show that a branch and bound method elaborating the special structure of the problem
can solve the problem much faster than the state-of-the integer programming code. 相似文献
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In this paper, a global optimization algorithm is proposed for solving sum of generalized polynomial ratios problem (P) which arises in various practical problems. Due to its intrinsic difficulty, less work has been devoted to globally solve the problem (P). For such problems, we present a branch and bound algorithm. In this method, by utilizing exponent transformation and new three-level linear relaxation method, a sequence of linear relaxation programming of the initial nonconvex programming problem (P) are derived which are embedded in a branch and bound algorithm. The proposed method need not introduce new variables and constraints and it is convergent to the global minimum of prime problem by means of the subsequent solutions of a series of linear programming problems. Several numerical examples in the literatures are tested to demonstrate that the proposed algorithm can systematically solve these examples to find the approximate ?-global optimum. 相似文献
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' 1 IntroductionWe collsider the fOllowi11g bilevel programndng problen1:max f(x, y),(BP) s.t.x E X = {z E RnIAx = b,x 2 0}, (1)y e Y(x).whereY(x) = {argmaxdTyIDx Gy 5 g, y 2 0}, (2)and b E R", d, y E Rr, g E Rs, A, D.and G are m x n1 s x n aild 8 x r matrices respectively. If itis not very difficult to eva1uate f(and/or Vf) at all iteration points, there are many algorithmeavailable fOr solving problem (BP) (see [1,2,3etc1). However, in some problems (see [4]), f(x, y)is too com… 相似文献
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In this paper, we prove that an optimal solution to the linear fractional bilevel programming problem occurs at a boundary feasible extreme point. Hence, the Kth-best algorithm can be proposed to solve the problem. This property also applies to quasiconcave bilevel problems provided that the first level objective function is explicitly quasimonotonic. 相似文献
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为求线性比试和问题的全局最优解,本文给出了一个分支定界算法.通过一个等价问题和一个新的线性化松弛技巧,初始的非凸规划问题归结为一系列线性规划问题的求解.借助于这一系列线性规划问题的解,算法可收敛于初始非凸规划问题的最优解.算法的计算量主要是一些线性规划问题的求解.数值算例表明算法是切实可行的. 相似文献
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The penalty function method, presented many years ago, is an important numerical method for the mathematical programming problems. In this article, we propose a dual-relax penalty function approach, which is significantly different from penalty function approach existing for solving the bilevel programming, to solve the nonlinear bilevel programming with linear lower level problem. Our algorithm will redound to the error analysis for computing an approximate solution to the bilevel programming. The error estimate is obtained among the optimal objective function value of the dual-relax penalty problem and of the original bilevel programming problem. An example is illustrated to show the feasibility of the proposed approach. 相似文献
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Le Thi Hoai An Pham Dinh Tao Nam Nguyen Canh Nguyen Van Thoai 《Journal of Global Optimization》2009,44(3):313-337
We propose a method for finding a global solution of a class of nonlinear bilevel programs, in which the objective function
in the first level is a DC function, and the second level consists of finding a Karush-Kuhn-Tucker point of a quadratic programming
problem. This method is a combination of the local algorithm DCA in DC programming with a branch and bound scheme well known
in discrete and global optimization. Computational results on a class of quadratic bilevel programs are reported. 相似文献
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Parametric global optimisation for bilevel programming 总被引:2,自引:2,他引:0
Nuno P. Faísca Vivek Dua Berç Rustem Pedro M. Saraiva Efstratios N. Pistikopoulos 《Journal of Global Optimization》2007,38(4):609-623
We propose a global optimisation approach for the solution of various classes of bilevel programming problems (BLPP) based
on recently developed parametric programming algorithms. We first describe how we can recast and solve the inner (follower’s)
problem of the bilevel formulation as a multi-parametric programming problem, with parameters being the (unknown) variables
of the outer (leader’s) problem. By inserting the obtained rational reaction sets in the upper level problem the overall problem
is transformed into a set of independent quadratic, linear or mixed integer linear programming problems, which can be solved
to global optimality. In particular, we solve bilevel quadratic and bilevel mixed integer linear problems, with or without
right-hand-side uncertainty. A number of examples are presented to illustrate the steps and details of the proposed global
optimisation strategy. 相似文献
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In this paper, we present a new trust region algorithm for a nonlinear bilevel programming problem by solving a series of its linear or quadratic approximation subproblems. For the nonlinear bilevel programming problem in which the lower level programming problem is a strongly convex programming problem with linear constraints, we show that each accumulation point of the iterative sequence produced by this algorithm is a stationary point of the bilevel programming problem. 相似文献
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《European Journal of Operational Research》1988,34(3):393-398
The solution of large scale integer linear programming models is generally dependent, in some way, upon the branch and bound technique, which can be quite time consuming. This paper describes a parallel branch and bound algorithm which achieves super linear efficiency in solving integer linear programming models on a multiprocessor computer. The algorithm is used to solve the Haldi and IBM test problems as well as a system design model. 相似文献
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Multilevel programming is characterized as mathematical programming to solve decentralized planning problems. The models partition control over decision variables among ordered levels within a hierarchical planning structure of which the linear bilevel form is a special case of a multilevel programming problem. In a system with such a hierarchical structure, the high-level decision making situations generally require inclusion of zero-one variables representing ‘yes-no’ decisions. We provide a mixed-integer linear bilevel programming formulation in which zero-one decision variables are controlled by a high-level decision maker and real-value decision variables are controlled by a low-level decision maker. An algorithm based on the short term memory component of Tabu Search, called Simple Tabu Search, is developed to solve the problem, and two supplementary procedures are proposed that provide variations of the algorithm. Computational results disclose that our approach is effective in terms of both solution quality and efficiency. 相似文献
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In this paper, a branch and bound approach is proposed for global optimization problem (P) of the sum of generalized polynomial
fractional functions under generalized polynomial constraints, which arises in various practical problems. Due to its intrinsic
difficulty, less work has been devoted to globally solving this problem. By utilizing an equivalent problem and some linear
underestimating approximations, a linear relaxation programming problem of the equivalent form is obtained. Consequently,
the initial non-convex nonlinear problem (P) is reduced to a sequence of linear programming problems through successively
refining the feasible region of linear relaxation problem. The proposed algorithm is convergent to the global minimum of the
primal problem by means of the solutions to a series of linear programming problems. Numerical results show that the proposed
algorithm is feasible and can successfully be used to solve the present problem (P). 相似文献
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Audet C. Hansen P. Jaumard B. Savard G. 《Journal of Optimization Theory and Applications》1997,93(2):273-300
We study links between the linear bilevel and linear mixed 0–1 programming problems. A new reformulation of the linear mixed 0–1 programming problem into a linear bilevel programming one, which does not require the introduction of a large finite constant, is presented. We show that solving a linear mixed 0–1 problem by a classical branch-and-bound algorithm is equivalent in a strong sense to solving its bilevel reformulation by a bilevel branch-and-bound algorithm. The mixed 0–1 algorithm is embedded in the bilevel algorithm through the aforementioned reformulation; i.e., when applied to any mixed 0–1 instance and its bilevel reformulation, they generate sequences of subproblems which are identical via the reformulation. 相似文献