NSF Org:	DMS Division Of Mathematical Sciences
Recipient:	UNIVERSITY OF MARYLAND BALTIMORE COUNTY
Initial Amendment Date:	August 29, 2003
Latest Amendment Date:	November 8, 2005
Award Number:	0238008
Award Instrument:	Standard Grant
Program Manager:	Junping Wang jwang@nsf.gov  (703)292-4488 DMS  Division Of Mathematical Sciences MPS  Directorate for Mathematical and Physical Sciences
Start Date:	September 1, 2003
End Date:	November 30, 2005 (Estimated)
Total Intended Award Amount:	$426,300.00
Total Awarded Amount to Date:	$83,136.00
Funds Obligated to Date:	FY 2003 = $83,136.00
History of Investigator:	Madhu Nayakkankuppam (Principal Investigator) madhu@math.umbc.edu
Recipient Sponsored Research Office:	University of Maryland Baltimore County 1000 HILLTOP CIR BALTIMORE MD  US  21250-0001 (410)455-3140
Sponsor Congressional District:	07
Primary Place of Performance:	University of Maryland Baltimore County 1000 HILLTOP CIR BALTIMORE MD  US  21250-0001
Primary Place of Performance Congressional District:	07
Unique Entity Identifier (UEI):	RNKYWXURFRL5
Parent UEI:
NSF Program(s):	COMPUTATIONAL MATHEMATICS
Primary Program Source:	app-0103
Program Reference Code(s):	1045, 9216, 9251, 9263, HPCC
Program Element Code(s):	127100
Award Agency Code:	4900
Fund Agency Code:	4900
Assistance Listing Number(s):	47.049

ABSTRACT

Nayakkankuppam
This project, which lies at the interface of optimization
theory, numerical analysis and parallel scientific computing,
focuses on the design, analysis and implementation of novel
algorithms for large-scale semidefinite programming. A growing
number of applications (for example, structural design,
communication networks, combinatorial optimization, quantum
chemistry, VLSI design) give rise to very large-scale problems
well beyond the capabilities of the interior-point algorithms
currently in use. The investigator studies (a) quasi-Newton
techniques in first and second order subgradient bundle methods,
and (b) parallel variable distribution in interior-point and
subgradient bundle methods. The goals of the project are
three-fold: (i) to develop convergence theories for these new
classes of algorithms, (ii) to develop fast, reliable and
parallel software implementations, and (iii) to apply these
algorithms and codes to specific problems in quantum chemistry
and numerical linear algebra. The educational plan, integrated
with the research goals of the project, includes (i) the
development of new graduate curricula in parallel computing,
numerical optimization and convex programming designed to train
graduate students to participate in, and contribute to, the
research program; (ii) a revision of undergraduate curricula in
linear and nonlinear programming to include significant modeling
and computational components.
Due to the wide-ranging applicability of semidefinite
programming, the tools and techniques developed in this project
can directly benefit society by providing improved,
cost-effective solutions to a variety of engineering design
problems. The specific problems targeted in numerical linear
algebra are relevant to aerospace applications, while the
problems in quantum chemistry are of a fundamental nature with
potential technological impact in, for example, semiconductor
design, magnetic storage media, rational drug design, etc. The
outreach activities of the project promote synergistic
collaborations with a national lab and industry. The educational
plan enriches special UMBC programs targeted at educating
minorities and historically underrepresented groups.

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