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

Abstract

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 themore » performance of the algorithm.« less

Authors:
 [1];  [1];  [1];  [1]
  1. ORNL
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Center for Computational Sciences
Sponsoring Org.:
USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE Office of Science (SC)
OSTI Identifier:
930749
DOE Contract Number:
DE-AC05-00OR22725
Resource Type:
Journal Article
Resource Relation:
Journal Name: Mathematical and Computer Modelling; Journal Volume: 45; Journal Issue: 3-4
Country of Publication:
United States
Language:
English
Subject:
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

Citation Formats

D'Helon, Cassius, Protopopescu, Vladimir A, Wells, Jack C, and Barhen, Jacob. GMG - A guaranteed global optimization algorithm: Application to remote sensing. United States: N. p., 2007. Web. doi:10.1016/j.mcm.2006.06.005.
D'Helon, Cassius, Protopopescu, Vladimir A, Wells, Jack C, & Barhen, Jacob. GMG - A guaranteed global optimization algorithm: Application to remote sensing. United States. doi:10.1016/j.mcm.2006.06.005.
D'Helon, Cassius, Protopopescu, Vladimir A, Wells, Jack C, and Barhen, Jacob. Mon . "GMG - A guaranteed global optimization algorithm: Application to remote sensing". United States. doi:10.1016/j.mcm.2006.06.005.
@article{osti_930749,
title = {GMG - A guaranteed global optimization algorithm: Application to remote sensing},
author = {D'Helon, Cassius and Protopopescu, Vladimir A and Wells, Jack C and Barhen, Jacob},
abstractNote = {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.},
doi = {10.1016/j.mcm.2006.06.005},
journal = {Mathematical and Computer Modelling},
number = 3-4,
volume = 45,
place = {United States},
year = {Mon Jan 01 00:00:00 EST 2007},
month = {Mon Jan 01 00:00:00 EST 2007}
}