P. E. Gill, G. H. Golub, W. Murray, and M. A. Saunders. Methods for modifying matrix factorizations, Mathematics of Computation 28, 505-535 (1974).
P. E. Gill, G. H. Golub, W. Murray, and M. A. Saunders. Methods for computing and modifying the LDV factors of a matrix, Mathematics of Computation 29, 1051-1077 (1975).
C. C. Paige and M. A. Saunders. Solution of sparse indefinite systems of linear equations, SINUM 12, 617-629 (1975).
C. C. Paige and M. A. Saunders. Least squares estimation of discrete linear dynamic systems using orthogonal transformations, SINUM 14, 180-193 (1977).
B. A. Murtagh and M. A. Saunders. Large-scale linearly constrained optimization, Math. Prog. 14, 41-72 (1978).
C. C. Paige and M. A. Saunders. Towards a generalized singular value decomposition, SINUM 18, 398-405 (1981).
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 Journal 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). Revised as SIGEST article Atomic decomposition by Basis Pursuit, SIAM Review 43(1), 129-159 (2001).
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 version 7: Software for large-scale nonlinear programming (2007), 116 pages.
A. George and 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. Revised Oct 26, 2000 (draft), 12 pages.
S. S. Chen, D. L. Donoho, and M. A. Saunders. Atomic decomposition by Basis Pursuit, SIAM Review 43(1), 129-159 (2001).
P. E. Gill, W. Murray and M. A. Saunders. SNOPT: An SQP algorithm for large-scale constrained optimization, SIOPT 12(4), 979-1006 (2002). Revised 2005 (see below).
M. Jacobsen, P. C. Hansen and M. A. Saunders. Subspace preconditioned LSQR for discrete ill-posed problems, BIT 43, 975-989 (2003).
P. E. Gill, W. Murray and M. A. Saunders. SNOPT: An SQP algorithm for large-scale constrained optimization, SIAM Review 47(1), 99-131 (2005).
R. Tibshirani, M. Saunders, S. Rosset, J. Zhu and K. Knight. Sparsity and smoothness via the fused lasso, Journal of the Royal Statistical Society B 67(1), 91-108 (2005).
M. P. Friedlander and M. A. Saunders. A globally convergent linearly constrained Lagrangian method for nonlinear optimization, SIAM Journal on Optimization 15(3), 863-897 (2005).
M. W. Carter, H. H. Jin, M. A. Saunders, and Y. Ye. SpaseLoc: An adaptive subproblem algorithm for scalable wireless sensor network localization, SIAM Journal on Optimization 17(4), 1102-1128 (2006).
M. P. Friedlander and M. A. Saunders. Discussion: The Dantzig selector: Statistical estimation when p is much larger than n, Annals of Statistics 35(6), 2385-2391 (2007).
P. E. Gill, W. Murray, M. A. Saunders, J. A. Tomlin, and M. H. Wright, George B. Dantzig and systems optimization, Journal on Discrete Optimization 5(2), 151-158 (2008), in memory of George B. Dantzig.
G. Chantas, N. Galatsanos, A. Likas, and M. A. Saunders, Variational Bayesian image restoration based on a product of t-distributions image prior, IEEE Trans. Image Processing 17(10), 1795–1805 (2008).
C.-M. Fransson, T. Wik, B. Lennartson, M. A. Saunders, and P.-O. Gutman, Nonconservative robust control: Optimized and constrained sensitivity functions, IEEE Trans. Contr. Sys. Tech. 17(2), 298–308 (2009).
M. J. O'Sullivan and M. A. Saunders, Stabilizing policy improvement for large-scale infinite-horizon dynamic programming, SIAM Journal on Matrix Analysis and Applications, 31(2), 434-459 (2009).
V. Pereyra, M. A. Saunders, and J. Castillo, Equispaced Pareto front construction for constrained bi-objective optimization, Mathematical and Computer Modelling (2011), 10 pp.
S.-C. Choi, C. C. Paige, and M. A. Saunders, MINRES-QLP: a Krylov subspace method for indefinite or singular symmetric systems, SIAM J. Scientific Computing 33:4 (2011) 1810-1836. (2012 Linear Algebra Prize, SIAM Activity Group on Linear Algebra.)
D. C.-L. Fong and M. A. Saunders, LSMR: An iterative algorithm for sparse least-squares problems, SIAM J. Scientific Computing 33:5 (2011) 2950-2971.
R. M. T. Fleming, C. M. Maes, M. A. Saunders, Y. Ye, and B. O. Palsson, A variational principle for computing nonequilibrium fluxes and potentials in genome-scale biochemical networks, J. Theoretical Biology 292 (2012) 71-77.
S. P. Ponnapalli, M. A. Saunders, C. F. Van Loan, and O. Alter, A higher-order generalized singular value decomposition for comparison of global mRNA expression from multiple organisms, PLoS ONE 6(12): e28072 (2011) 11 pp.
D. C.-L. Fong and M. A. Saunders, CG versus MINRES: An empirical comparison, SQU Journal for Science 17:1 (2012) 44-62.
J. D. Lee, Y. Sun, and M. A. Saunders, Proximal Newton-type methods for minimizing composite functions, in revision, 2013.
X. Meng, M. A. Saunders, and M. W. Mahoney, LSRN: a parallel iterative solver for strongly over- or under-determined systems, SIAM J. Sci. Comp., in revision, 2013, 20 pp.
P. Berman, A. Leshem, O. Etziony, O. Levi, Y. Parmet, M. Saunders, and Z. Wiesman, Novel 1H low field nuclear magnetic resonance applications for the field of biodiesel, Biotechnology for Biofuels 6:55 (2013) 20 pp.
P. Berman, O. Levi, Y. Parmet, M. Saunders, and Z. Wiesman, Laplace inversion of low-resolution NMR relaxometry data using sparse representation methods, Concepts in Magnetic Resonance Part A 42:3 (2013) 72-88.
Y. Sun, R. M. T. Fleming, I. Thiele, and M. A. Saunders, Robust flux balance analysis of multiscale biochemical reaction networks, BMC Bioinformatics 14:240 (2013) 6 pp.
S.-C. Choi and M. A. Saunders, Algorithm 937: MINRES-QLP for symmetric and Hermitian linear equations and least-squares problems, ACM Trans. Math. Softw. 40(2), Article 16, Feb 2014, 12 pp.
X. Meng, M. A. Saunders, and M. W. Mahoney, LSRN: a parallel iterative solver for strongly over- or underdetermined systems, SIAM J. Sci. Comput. 36:2 (2014) C95-C118.