## Global Optimization of Mixed-Integer Nonlinear Problems (0)

Venue: | AIChE J |

Citations: | 14 - 2 self |

### BibTeX

@ARTICLE{Adjiman_globaloptimization,

author = {C. S. Adjiman and I. P. Androulakis and C. A. Floudas},

title = {Global Optimization of Mixed-Integer Nonlinear Problems},

journal = {AIChE J},

year = {},

volume = {46},

pages = {176--9}

}

### Years of Citing Articles

### OpenURL

### Abstract

Two novel deterministic global optimization algorithms for nonconvex mixed-integer problems (MINLPs) are proposed, using the advances of the ffBB algorithm for nonconvex NLPs Adjiman et al. (1998a). The Special Structure Mixed-Integer ffBB algorithm (SMIN-ffBB addresses problems with nonconvexities in the continuous variables and linear and mixed-bilinear participation of the binary variables. The General Structure Mixed-Integer ffBB algorithm (GMIN-ffBB), is applicable to a very general class of problems for which the continuous relaxation is twice continuously differentiable. Both algorithms are developed using the concepts of branch-and-bound, but they differ in their approach to each of the required steps. The SMIN-ffBB algorithm is based on the convex underestimation of the continuous functions while the GMIN-ffBB algorithm is centered around the convex relaxation of the entire problem. Both algorithms rely on optimization or interval based variable bound updates to enhance effici...