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Michael Saunders
Professor (Research)
Management Science and Engineering
Office: Terman 330 | Phone: 650-723-1875 | Fax: 650-723-1614
Email: saunders @ stanford.edu |
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Publications
Last modified: 16 Feb 2000
- C. C. Paige and M. A. Saunders, Solution
of sparse indefinite systems of linear equations, SINUM 12, 617-629
(1975).
- B. A. Murtagh and M. A. Saunders, Large-scale
linearly constrained optimization, Math. Prog. 14, 41-72 (1978).
- B. A. Murtagh and M. A. Saunders, A projected
Lagrangian algorithm and its implementation for sparse nonlinear
constraints, Math. Prog. Study 16 (Constrained Optimization),
84-117 (1982).
- C. C. Paige and M. A. Saunders, LSQR: An
algorithm for sparse linear equations and sparse least squares,
ACM TOMS 8(1), 43-71 (1982).
- C. C. Paige and M. A. Saunders, Algorithm
583; LSQR: Sparse linear equations and least-squares problems,
ACM TOMS 8(2), 195-209 (1982).
- P. E. Gill, W. Murray, M. A. Saunders and
M. H. Wright, Sparse matrix methods in optimization, SISSC 5,
562-589 (1984).
- P. E. Gill, W. Murray, M. A. Saunders, J.
A. Tomlin and M. H. Wright, On projected Newton barrier methods
for linear programming and an equivalence to Karmarkar's projective
method, Math. Prog. 36, 183-209 (1986).
- P. E. Gill, W. Murray, M. A. Saunders and
M. H. Wright, Maintaining LU factors of a general sparse matrix,
LAA 88/89, 239-270 (1987).
- S. K. Eldersveld and M. A. Saunders, A block-LU
update for large-scale linear programming, SIMAX 13, 191-201 (1992).
- P. E. Gill, W. Murray, D. B. Ponceleón
and M. A. Saunders, Preconditioners for indefinite systems arising
in optimization, SIMAX 13, 292-311 (1992).
- M. A. Saunders, Major Cholesky would feel
proud, ORSA J. on Computing 6, 23-27 (1994).
- B. A. Murtagh and M. A. Saunders, MINOS 5.5
User's Guide, Report SOL 83-20R, Dept of Operations Research,
Stanford University (Revised Jul 1998).
- M. A. Saunders, Solution of sparse rectangular
systems using LSQR and CRAIG, BIT 35, 588-604 (1995).
- P. E. Gill, M. A. Saunders and J. R. Shinnerl,
On the stability of Cholesky factorization for quasi-definite
systems, SIMAX 17(1), 35-46 (1996).
- M. A. Saunders,
Cholesky-based methods for sparse least squares: The benefits
of regularization, Report SOL 95-1, Dept of Operations Research,
Stanford University (1995). In L. Adams and J. L. Nazareth (eds.),
Linear and Nonlinear Conjugate Gradient-Related Methods, SIAM,
Philadelphia, 92-100 (1996).
- P. E. Gill, W. Murray and M. A. Saunders,
User's guide for QPOPT 1.0: A Fortran package for quadratic programming,
Report SOL 95-4, Dept of Operations Research, Stanford University
(1995).
- M. A. Saunders and J. A. Tomlin,
Stable reduction to KKT systems in barrier methods for linear
and quadratic programming, Report SOL 96-3, Dept of EESOR,
Stanford University (1996).
- M. A. Saunders and J. A. Tomlin,
Solving regularized linear programs using barrier methods and
KKT systems, Report SOL 96-4, Dept of EESOR, Stanford University
(1996).
- M. A. Saunders, Computing projections with
LSQR, BIT 37:1, 96-104 (1997).
- S. S. Chen, D. L. Donoho and M. A. Saunders,
Atomic
decomposition by Basis Pursuit, SISC 20(1), 33-61 (1998).
- P. E. Gill, W. Murray and M. A. Saunders,
SNOPT: An SQP algorithm for large-scale constrained optimization,
Report SOL 97-3, Dept of EESOR, Stanford University (1997), 37
pages.
- I. Bongartz, A. R. Conn, N. I. M. Gould,
M. A. Saunders and Ph. L. Toint,
A numerical comparison between the LANCELOT and MINOS packages
for large-scale constrained optimization, Report SOL 97-6,
Dept of EESOR, Stanford University (1997), 19 pages.
- I. Bongartz, A. R. Conn, N. I. M. Gould,
M. A. Saunders and Ph. L. Toint,
A numerical comparison between the LANCELOT and MINOS packages
for large-scale constrained optimization: the complete results,
Report SOL 97-7, Dept of EESOR, Stanford University (1997), 50
pages.
- P. E. Gill, W. Murray and M. A. Saunders,
User's guide for SNOPT 5.3: A Fortran package for large-scale
nonlinear programming, Report SOL 98-1, Dept of EESOR, Stanford
University (1997), 37 pages.
- M. A. Saunders,
Solution of sparse linear equations using Cholesky factors of
augmented systems, Report SOL 99-1, Dept of EESOR, Stanford
University (1999), 9 pages.
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