We found a match
Your institution may have rights to this item. Sign in to continue.
- Title
A New Global Optimization Algorithm for Solving a Class of Nonconvex Programming Problems.
- Authors
Xue-Gang Zhou; Bing-Yuan Cao
- Abstract
A new two-part parametric linearization technique is proposed globally to a class of nonconvex programming problems (NPP). Firstly, a two-part parametric linearizationmethod 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.
- Subjects
GLOBAL optimization; PROBLEM solving; NONCONVEX programming; MATHEMATICAL bounds; LOGARITHMS; EXPONENTIAL functions
- Publication
Journal of Applied Mathematics, 2014, p1
- ISSN
1110-757X
- Publication type
Article
- DOI
10.1155/2014/697321