Brian Sutton
Assistant Professor
Department of Mathematics
Randolph-Macon College, Ashland, Virginia
Brian Sutton grew up in Roanoke, Virginia, and completed his university work at Virginia Tech.
He earned his Ph. D. from the Massachusetts Institute of Technology in
2005 and joined the Randolph-Macon College faculty the same year. His primary research
areas are numerical linear algebra and random matrix theory.
He was awarded First Place in the Fourteenth
Leslie Fox Prize competition and has received NSF grant DMS-0914559 for work on the CS
decomposition. The stochastic operator approach to random matrix theory, the subject of his
Ph. D. work, has opened a new avenue to the study of random eigenvalues, much as Schroedinger's
introduction of quantum mechanics reformulated and expanded the earlier matrix mechanics approach.
In the classroom, he has taught courses on numerical analysis, differential equations, probability,
and statistics, as well as an interdisciplinary course on epidemiology.
Curriculum vitae
CV (PDF)
Contact
| Email | bsutton at rmc dot edu
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| Web | http://faculty.rmc.edu/bsutton/
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| Office | Copley 235
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| U.S. Mail | Department of Mathematics, Randolph-Macon College, PO Box
5005, Ashland, VA 23005
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Current courses
Please visit Moodle.
Software
Click on the links for manuscripts, PowerPoint-style presentations, and computer code.
Numerical linear algebra, random matrices, and stochastic differential operators
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Computing the complete CS decomposition
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Brian D. Sutton. Computing the complete CS decomposition. Numer. Algorithms. 50 (2009),
no. 1, 33–65.
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The beta-Jacobi matrix model, the CS decomposition, and generalized singular
value problems
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Alan Edelman and Brian D. Sutton. The beta-Jacobi matrix model, the CS decomposition, and generalized singular
value problems. Found. Comput. Math. 8 (2008), no. 2, 259–285.
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From random matrices to stochastic operators
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Alan Edelman and Brian D. Sutton. From random matrices to stochastic operators. J. Stat. Phys.
127 (2007), no. 6, 1121–1165.
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The Stochastic Operator Approach to Random Matrix Theory
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Brian D. Sutton. The Stochastic Operator Approach to Random Matrix Theory. Ph.D. thesis,
Massachusetts Institute of Technology, 2005.
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Tails of condition number distributions
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Alan Edelman and Brian D. Sutton. Tails of condition number distributions. SIAM J. Matrix
Anal. Appl. 27 (2005), no. 2, 547–560.
Combinatorial matrix theory
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Implicit construction of multiple eigenvalues for trees
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Charles R. Johnson, Brian D. Sutton, and Andrew J. Witt. Implicit construction of multiple
eigenvalues for trees. Linear Multilinear Algebra. 57 (2009),
no. 4, 409–420.
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On the minimum rank of a positive semidefinite matrix with a given
graph
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Matthew Booth, Philip Hackney, Benjamin Harris, Charles R. Johnson,
Margaret Lay, Lon H. Mitchell, Sivaram K. Narayan, Amanda Pascoe, Kelly Steinmetz,
Brian D. Sutton, and Wendy Wang. On the minimum rank of a positive semidefinite matrix
with a given graph. SIAM J. Matrix Anal. Appl. 30 (2008), no. 2, 731–740.
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Hermitian matrices, eigenvalue multiplicities, and
eigenvector components
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Charles R. Johnson and Brian D. Sutton. Hermitian matrices, eigenvalue multiplicities, and
eigenvector components. SIAM J. Matrix Anal. Appl. 26 (2004/05), no. 2, 390–399.
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On the relative position of multiple eigenvalues in the spectrum of an
Hermitian matrix with a given graph
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Charles R. Johnson, António Leal Duarte, Carlos M. Saiago, Brian D. Sutton, and Andrew J. Witt.
On the relative position of multiple eigenvalues in the spectrum of an
Hermitian matrix with a given graph. Special issue on nonnegative matrices,
M-matrices and their generalizations (Oberwolfach, 2000). Linear Algebra Appl. 363 (2003),
147–159.
PDE's
See my CV for venue information.
Copyright 2009, Brian Sutton.