A method for global optimization of large systems of quadratic constraints

Authors:
Nitin Lamba;Mark Dietz;Daniel P. Johnson;Mark S. Boddy
Affiliations:
Honeywell Laboratories, Adventium Labs;Honeywell Laboratories, Adventium Labs;Honeywell Laboratories, Adventium Labs;Honeywell Laboratories, Adventium Labs
Venue:
COCOS'03 Proceedings of the Second international conference on Global Optimization and Constraint Satisfaction
Year:
2003

Citing 1
Cited 2

A collection of test problems for constrained global optimization algorithms

A collection of test problems for constrained global optimization algorithms

Modelling mixed discrete-continuous domains for planning

Journal of Artificial Intelligence Research
COLIN: planning with continuous linear numeric change

Journal of Artificial Intelligence Research

Quantified Score

Hi-index	0.00

Visualization

Abstract

In previous work, we have presented a novel global feasibility solver for the large system of quadratic constraints that arise as subproblems in the solving of hard hybrid problems, such as the scheduling of refineries. In this paper we present the Gradient Optimal Constraint Equation Subdivision (GOCES) algorithm, which incorporates a standard NLP solver and the global feasibility solver to find and establish global optimums for systems of quadratic equations, and present benchmarks.