Paper Info
Title
An Ellipsoidal Branch and Bound Algorithm for Global Optimization
Abstract
A branch and bound algorithm is developed for global optimization. Branching in the algorithm is accomplished by subdividing the feasible set using ellipses. Lower bounds are obtained by replacing the concave part of the objective function by an affine underestimate. A ball approximation algorithm, obtained by generalizing of a scheme of Lin and Han, is used to solve the convex relaxation of the original problem. The ball approximation algorithm is compared to SEDUMI as well as to gradient projection algorithms using randomly generated test problems with a quadratic objective and ellipsoidal constraints.
Year
DOI
Venue
2009
10.1137/080729165
SIAM Journal on Optimization
Keywords
Field
DocType
concave part,affine underestimate,ball approximation,weakly convex,objective function,quadratic objective,convex relaxation,ellipsoidal constraint,bound algorithm,branch and bound,feasible set,ball approximation algorithm,global optimization,ellipsoidal branch,affine underestimation,lower bound,branch and bound algorithm
Affine transformation,Approximation algorithm,Branch and bound,Mathematical optimization,Global optimization,Branch and cut,Frank–Wolfe algorithm,Feasible region,Linear programming relaxation,Mathematics
Journal
Volume
Issue
ISSN
20
2
SIAM Journal on Optimization, vol. 20 (2009), p.740-758
Citations 
PageRank 
References 
5
0.52
17
Authors
2
Name
Order
Citations
PageRank
William W. Hager11603214.67
Dzung T. Phan26110.32