STTR Phase II: Integrated Software and Systems for Large-Scale Nonlinear Optimization

Award Information
Agency:
National Science Foundation
Branch
n/a
Amount:
$499,929.00
Award Year:
2004
Program:
STTR
Phase:
Phase II
Contract:
0422132
Award Id:
63840
Agency Tracking Number:
0232384
Solicitation Year:
n/a
Solicitation Topic Code:
n/a
Solicitation Number:
n/a
Small Business Information
2615 Hartzell Street, Evanston, IL, 60201
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
n/a
Principal Investigator:
RichardWaltz
PI
(847) 869-3269
waltz@ziena.com
Business Contact:
RobertFourer
(847) 491-3151
4er@ziena.com
Research Institute:
Northwestern University
Jorge Nocedal
633 Clark Street
Evanston, IL, 60208-1110
(847) 491-5038
Nonprofit college or university
Abstract
This Small Business Technology Transfer Phase II research project will address the design and creation of integrated nonlinear optimization software that combines complementary approaches to nonlinear optimization to achieve robust performance over a wide range of application requirements. The work will concentrate on the area of smooth nonlinearly constrained optimization, which arises directly in numerous applications and as a sub-problem in mixed-integer nonlinear programming and global optimization. The work will employ both mathematical convergence analyses and extensive testing on problems of practical interest. Results of the research will take nonlinear optimization software to a new level, based on an adaptive and versatile collection of algorithms in contrast to the single-algorithm approaches employed by current optimization packages.Nonlinear optimization models arise in diverse areas of science such as medical imaging, oceanography, crystallography, and climate modeling, and in almost all areas of engineering, chip feature placement for semiconductor manufacturers to energy management for electric and gas utilities. Nonlinear optimization is also rapidly becoming a key tool in decision analysis in such areas as finance and revenue management. By enabling optimization packages to be more fexible and more reliable, this research will lead to stronger support for current nonlinear optimization applications while making new, more ambitious applications possible.

* information listed above is at the time of submission.

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