DECOMPOSITION ALGORITHMS POTENTIALS FOR THE NON-HOMOGENEOUS GENERALIZED NETWORKED PROBLEMS OF LINEAR-FRACTIONAL PROGRAMMING

Decomposition Algorithms Potentials for the Non-homogeneous Generalized Networked Problems of Linear-fractional Programming

We use potentials for calculate a reduced costs in the increment of the objective function for the linear-fractional non-homogeneous flow programming optimization problem with additional constraints of general kind. The effective algorithm for solution of the system of potentials for a sparse matrix is considered. AMS Subject Classification: 65K05, 90C08, 90C35, 05C50, 15A03, 15A06

Fuzzy Primal Simplex Algorithms for Solving Fuzzy Linear Programming Problems

SENSITIVITY ANALYSIS IN LINEAR-PLUS-LINEAR FRACTIONAL PROGRAMMING PROBLEMS

In this paper, we study the classical sensitivity analysis when the right - hand – side vector, and the coefficients of the objective function are allowed to vary. 

Algorithms for generalized fractional programming

A generalized fractional programming problem is specified as a nonlinear program where a nonlinear function defined as the maximum over several ratios of functions is to be minimized on a feasible domain of ~n. The purpose of this paper is to outline basic approaches and basic types of algorithms available to deal with this problem and to review their convergence analysis. The conclusion includ...

Algorithms for Quadratic Fractional Programming Problems

Consider the nonlinear fractional programming problem max{f(x)lg(x)lxES}, where g(x»O for all XES. Jagannathan and Dinkelbach have shown that the maximum of this problem is equal to ~O if and only if max{f(x)-~g(x) IXES} is 0 for ~=~O. 1 t t Based on this result, we treat here a special case: f(x)=Zx Cx+r x+s, g(X)=~ xtDX+ptX+q and S is a polyhedron, where C is negative definite and D is positi...