GMG - A guaranteed global optimization algorithm: Application to remote sensing

Journal Article · Mon Jan 01 00:00:00 EST 2007 · Mathematical and Computer Modelling

DOI:https://doi.org/10.1016/j.mcm.2006.06.005· OSTI ID:930749

D'Helon, Cassius ^[1]; Protopopescu, Vladimir A ^[1]; Wells, Jack C ^[1]; Barhen, Jacob ^[1]

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1. ORNL

We investigate the role of additional information in reducing the computational complexity of the global optimization problem (GOP). Following this approach, we develop GMG -- an algorithm to find the Global Minimum with a Guarantee. The new algorithm breaks up an originally continuous GOP into a discrete (grid) search problem followed by a descent problem. The discrete search identifies the basin of attraction of the global minimum after which the actual location of the minimizer is found upon applying a descent algorithm. The algorithm is first applied to the golf course problem, which serves as a litmus test for its performance in the presence of both complete and degraded additional information. GMG is further assessed on a set of standard benchmark functions. We then illustrate the performance of the the validated algorithm on a simple realization of the monocular passive ranging (MPR) problem in remote sensing, which consists of identifying the range of an airborne target (missile, plane, etc.) from its observed radiance. This inverse problem is set as a GOP whereby the difference between the observed and model predicted radiances is minimized over the possible ranges and atmospheric conditions. We solve the GOP using GMG and report on the performance of the algorithm.

• Research Organization: Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). National Center for Computational Sciences (NCCS)
• Sponsoring Organization: USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE Office of Science (SC)
• DOE Contract Number: DE-AC05-00OR22725
• OSTI ID: 930749
• Journal Information: Mathematical and Computer Modelling, Vol. 45, Issue 3-4; ISSN 0895-7177
• Country of Publication: United States
• Language: English

Related Subjects

97
99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE
ALGORITHMS
G CODES
PERFORMANCE
REMOTE SENSING
DATA ANALYSIS
RANGE FINDERS
ILLUMINANCE
TARGETS
global optimization
parameter identification
additional information
guaranteed global minimum
discrete search
remote sensing
monocular passive ranging