Details of Grant 

EPSRC Reference:	EP/E035760/1
Title:	Novel line search procedure for very large scale optimisation
Principal Investigator:	Vassiliadis, Dr V
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Department:	Chemical Engineering and Biotechnology
Organisation:	University of Cambridge
Scheme:	Standard Research
Starts:	01 May 2007	Ends:	30 April 2009	Value (£):	168,390
EPSRC Research Topic Classifications:	Artificial Intelligence Mathematical Aspects of OR Software Engineering
EPSRC Industrial Sector Classifications:	No relevance to Underpinning Sectors
Related Grants:
Panel History:
Summary on Grant Application Form
In this work we are interested in the solution of general optimisation models comprised of a nonlinear objective function and subject to general nonlinear equality and inequality constraints. Of particular interest is the solution of very large scale problems arising in industrial and scientific research models, such as optimal control problems, molecular design, protein folding and a host of other areas requiring the availability of advanced solvers. Engineering models, particularly ones arising from process modelling, contain a very large proportion of equality constraints as well as operational inequality constraints and bounds, encapsulating limits in control parameter values and quality and operability satisfaction. The field of nonlinear optimisation has enjoyed many inspiring ideas in its many years of existence, and engineering practice as a result has benefited greatly by the availability of such methods within standard commercial simulation packages.This proposal intends to develop further on a new concept for very large scale optimisation developed in our research group. This methodology involves a novel line search procedure proposed originally by the principal investigator and co-workers, which for the first time presents a true non-monotone aspect over both objective and constraint values. Early results indicate that such attributes allow the optimisation solver to take fewer steps towards an optimum point and even to escape from local minima in some cases.
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Organisation Website:	http://www.cam.ac.uk