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Igor
Griva
Professor
of Computational Mathematics
Mailing
Address
Department of Computational and
Data Sciences
College of Science
George Mason University
4400
University Dr., MSN 6A2
Fairfax, Virginia 22030
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Current Research
Interests
Nonlinear
optimization and computational
analysis.
This research involves developing new
primal-dual algorithms
for nonlinear constrained optimization, their mathematical analysis,
efficient
implementation and application to problems in computational learning,
radiation
treatment planning, power generation and transmission. Optimization
based
computational analysis also includes investigation of light enhancement
and
propagation in nanostructures and estimation of electron transfer rates
in
proteins and molecular wires.
Education
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Ph.D. 2002 - George Mason
University,
Information Technology
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M.Sc.
1994 - Moscow State University, Applied
Mathematics
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B.Sc. 1993 - Moscow State
University,
Applied Mathematics
Courses Taught
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MATH 110: Introduction in
Probability and Statistics
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MATH 113: Analytic Geometry &
Calculus I
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MATH 125: Discrete Mathematics I
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MATH 213: Analytic Geometry &
Calculus III
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CSI 747 / MATH 689: Nonlinear
Optimization and Application
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CSI 749 / MATH 689: Element of
Computational Learning
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CSI 899: Colloquium in
Computational Sciences and
Informatics
Recent Publications
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R.
Polyak,
S. Ho, I. Griva, “Support Vector Machine
via Nonlinear Rescaling
Method,” Optimization Letters, 1, pp 367-378, 2007.
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N. Lebedev, S. Trammell, A. Spano,
E. Lukashev, I. Griva,
J. Schnur, “Conductive wiring of
immobilized photosynthetic
reaction center to electrode by cytochrome
c”,
Journal of The American Chemical Society 128 (37), pp.
12044-12045,
2006.
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I. Griva,
R. Polyak, “Primal-Dual Nonlinear
Rescaling Method
with Dynamic Scaling Parameter Update”, Mathematical Programming 106
(2), pp 237-259, 2006.
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I.
Griva, R. Vanderbei, “Case
Studies in Optimization: Catenary
Problem”,
Optimization and Engineering, 6 (4), 2005.
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I.
Griva,
“Numerical
Experiments with an
Interior-Exterior Point Method for Nonlinear Programming”,
Computational
Optimization and Applications, 29 (2), pp. 173-195, 2004.
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