A new global optimization algorithm for solving a class of nonconvex programming problems

A new two-part parametric linearization technique is proposed globally to a class of nonconvex programming problems (npp). firstly,a two-part parametric linearization method is adopted to construct the underestimator of objective and constraint functions,by utilizing a transformation and a parametric linear upper bounding function (lubf) and a linear lower bounding function (llbf) of a natural logarithm function and an exponential function with e as the base,respectively. then,a sequence of relaxation lower linear programming problems,which are embedded in a branch-and-bound algorithm,are derived in an initial nonconvex programming problem. the proposed algorithm is converged to global optimal solution by means of a subsequent solution to a series of linear programming problems. finally,some examples are given to illustrate the feasibility of the presented algorithm. © 2014 xue-gang zhou and bing-yuan cao.