A branch and bound algorithm for the global optimization of Hessian Lipschitz continuous functions

ePubs

The open archive for STFC research publications

DOI	10.5286/raltr.2011020
Persistent URL	http://purl.org/net/epubs/work/61295
Record Status	Checked
Record Id	61295
Title	A branch and bound algorithm for the global optimization of Hessian Lipschitz continuous functions
Contributors	JM Fowkes (Edinburgh U.), NIM Gould (STFC Rutherford Appleton Lab.), CL Farmer (Oxford U.)
Abstract	We present a branch and bound algorithm for the global optimization of a twice differentiable nonconvex objective function with a Lipschitz continuous Hessian over a compact, convex set.The algorithm is based on applying cubic regularisation techniques to the objective function within an overlapping branch and bound algorithm for convex constrained global optimization. Unlike other branch and bound algorithms, lower bounds are obtained via nonconvex underestimators of the function. For a numerical example, we apply the proposed branch and bound algorithm to radial basis function approximations.
Organisation	CSE , CSE-NAG , STFC
Keywords
Funding Information
Related Research Object(s):	66310
Licence Information:
Language	English (EN)

Type	Details	URI(s)	Local file(s)	Year
Report	RAL Technical Reports RAL-TR-2011-020. 2011.		RAL-TR-2011-020.pdf	2011