Search

To globally solve linear multiplicative programming problem (LMP), this paper presents a practicable branch-and-bound method based on the framework of branch-and-bound algorithm. In this method, a new linear relaxation technique is proposed firstly. Then, the branch-and-bound algorithm is developed for solving problem LMP. The proposed algorithm is proven that it is convergent to the global minimum by means of the subsequent solutions of a series of linear programming problems. Some experiments are reported to show the feasibility and efficiency of this algorithm.

This paper presents a linear decomposition approach for a class of nonconvex programming problems by dividing the input space into polynomially many grids. It shows that under certain assumptions the original problem can be transformed and decomposed into a polynomial number of equivalent linear programming subproblems. Based on solving a series of liner programming subproblems corresponding to those grid points we can obtain the near-optimal solution of the original problem. Compared to existing results in the literature, the proposed algorithm does not require the assumptions of quasi-concavity and differentiability of the objective function, and it differs significantly giving an interesting approach to solving the problem with a reduced running time.

This paper presents a deterministic global optimization algorithm for solving generalized linear multiplicative programming (GLMP). In this algorithm, a new linearization method is proposed, which applies more information of the function of (GLMP) than some other methods. By using this new linearization technique, the initial nonconvex problem is reduced to a sequence of linear programming problems. A deleting rule is presented to improve the convergence speed of this algorithm. The convergence of this algorithm is established, and some experiments are reported to show the feasibility and efficiency of this algorithm.

In this paper, we present an efficient branch and bound method for general linear fractional problem (GFP). First, by using a transformation technique, an equivalent problem (EP) of GFP is derived, then by exploiting structure of EP, a linear relaxation programming (LRP) of EP is obtained. To implement the algorithm, the main computation involve solving a sequence of linear programming problem, which can be solved efficiently. The proposed algorithm is convergent to the global maximum through the successive refinement of the solutions of a series of linear programming problems. Numerical experiments are reported to show the feasibility of our algorithm.

The global optimization of the sum of linear ratios problem has attracted the interest of researchers and practitioners for a number of years. Since these types of optimization problems are nonconvex, various specialized algorithms have been proposed for globally solving these problems. However, these algorithms are only for the case that sum of linear ratios problem without coefficients, and may be difficult to implement. In this paper, a branch and bound algorithm is proposed for globally solving the sum of linear ratios problem with coefficients. By utilizing an equivalent problem and linearization technique, the initial nonconvex programming problem is reduced to a sequence of linear programming problems. The proposed algorithm is convergent to the global optimal solution by means of the subsequent solutions of a series of linear programming problems. Numerical results are given to show the feasibility and effectiveness of the proposed algorithm.

We consider a branch-and-reduce approach for solving generalized linear multiplicative programming. First, a new lower approximate linearization method is proposed; then, by using this linearization method, the initial nonconvex problem is reduced to a sequence of linear programming problems. Some techniques at improving the overall performance of this algorithm are presented. The proposed algorithm is proved to be convergent, and some experiments are provided to show the feasibility and efficiency of this algorithm.