# TOMLAB  
# REGISTER (TOMLAB)
# LOGIN  
# myTOMLAB
TOMLAB LOGO

« Previous « Start

References

[1]
A projected Lagrangian algorithm and its implementation for sparse nonlinear constraints. Math. Program., 16:84–117, 1982.

[2]
Maintaining LU factors of a general sparse matrix. Linear Algebra and its Applications, 88, 1987.

[3]
A practical anti-cycling procedure for linearly constrained optimization. Math. Program., 45:437–474, 1989.

[4]
MINOS 5.4 User's Guide. Journal of Global Optimization, 1995.

[5]
R. H. Bartels. A stabilization of the simplex method. Numerische Mathematik, 16:414–434, 1971.

[6]
R. H. Bartels and G. H. Golub. The simplex method of linear programming using the lu decomposition. Communications of the ACM, 12:266–268, 1969.

[7]
A. R. Conn. Constrained optimization using a nondifferentiable penalty function. SIAM J. Numer. Anal., 10:760–779, 1973.

[8]
G. B. Dantzig. Linear programming and extensions. 1963.

[9]
W. C. Davidon. Variable metric methods for minimization. A.E.C. Research and Development Report ANL-599, 1959.

[10]
S. K. Eldersveld. Large-scale sequential quadratic programming algorithms. 1991.

[11]
R. Fourer. Solving staircase linear programs by the simplex method. 1: Inversion. Math. Program., 23:274–313, 1982.

[12]
M. P. Friedlander. A Globally Convergent Linearly Constrained Lagrangian Method for Nonlinear Optimization. 2002.

[13]
P. E. Gill and W. Murray. Numerically stable methods for quadratic programming. Mathematical Programming, 14:349–372, 1978.

[14]
P. E. Gill and W. Murray. Numerically stable methods for quadratic programming. Math. Program., 14:349–372, 1978.

[15]
Philip E. Gill, Sven J. Hammarling, Walter Murray, Michael A. Saunders, and Margaret H. Wright. User's guide for LSSOL ((version 1.0): A Fortran package for constrained linear least-squares and convex quadratic programming. Technical Report SOL 86-1, Systems Optimization Laboratory, Department of Operations Research, Stanford University, Stanford, California 94305-4022, 1986.

[16]
Philip E. Gill, Walter Murray, and Michael A. Saunders. User's guide for QPOPT 1.0: A Fortran package for Quadratic programming. Technical Report SOL 95-4, Systems Optimization Laboratory, Department of Operations Research, Stanford University, Stanford, California 94305-4022, 1995.

[17]
Philip E. Gill, Walter Murray, and Michael A. Saunders. SNOPT: An SQP algorithm for Large-Scale constrained programming. Technical Report SOL 97-3, Systems Optimization Laboratory, Department of Operations Research, Stanford University, Stanford, California 94305-4022, 1997.

[18]
Philip E. Gill, Walter Murray, and Michael A. Saunders. User's guide for SQOPT 5.3: A Fortran package for Large-Scale linear and quadratic programming. Technical Report Draft October 1997, Systems Optimization Laboratory, Department of Operations Research, Stanford University, Stanford, California 94305-4022, 1997.

[19]
Philip E. Gill, Walter Murray, and Michael A. Saunders. User's guide for SNOPT 5.3: A Fortran package for Large-Scale nonlinear programming. Technical Report SOL 98-1, Systems Optimization Laboratory, Department of Operations Research, Stanford University, Stanford, California 94305-4022, 1998.

[20]
Philip E. Gill, Walter Murray, Michael A. Saunders, and Margaret H. Wright. User's guide for NPSOL 5.0: A Fortran package for nonlinear programming. Technical Report SOL 86-2, Revised July 30, 1998, Systems Optimization Laboratory, Department of Operations Research, Stanford University, Stanford, California 94305-4022, 1998.

[21]
Hans-Martin Gutmann. Inertia-controlling methods for general quadratic programming. SIAM Rev., 33:1–36, 1991.

[22]
J. A. J. Hall and K. I. M. McKinnon. The simplest examples where the simplex method cycles and conditions where EXPAND fails to prevent cycling. Tech. Report MS 96-010, 19:201–227, 1996.

[23]
Jr. J. E. Dennis and R. B. Schnabel. Numerical Methods for Unconstrained Optimization. 1983.

[24]
B. A. Murtagh and M. A. Saunders. Large-scale linearly constrained optimization. Mathematical Programming, 14:41–72, 1978.

[25]
B. A. Murtagh and M. A. Saunders. Large-scale linearly constrained optimization. Math. Program., 14:41–72, 1978.

[26]
B. A. Murtagh and M. A. Saunders. A projected lagrangian algorithm and its implementation for sparse nonlinear constraints. Mathematical Programming Study, 16:84–117, 1982.

[27]
Bruce A. Murtagh and Michael A. Saunders. MINOS 5.5 USER'S GUIDE. Technical Report SOL 83-20R, Revised July 1998, Systems Optimization Laboratory, Department of Operations Research, Stanford University, Stanford, California 94305-4022, 1998.

[28]
M. A. Saunders P. E. Gill, W. Murray and M. H. Wright. Two step-length algorithms for numerical optimization. 1979.

[29]
M. A. Saunders P. E. Gill, W. Murray and M. H. Wright. Procedures for optimization problems with a mixture of bounds and general linear constraints. ACM Transactions on Mathematical Software, 10:282–298, 1984.

[30]
M. A. Saunders P. E. Gill, W. Murray and M. H. Wright. User's guide for NPSOL (Version 4.0): a Fortran package for nonlinear programming. 1986.

[31]
M. A. Saunders P. E. Gill, W. Murray and M. H. Wright. Maintaining lu factors of a general sparse matrix. Linear Algebra and its Applications, 88, 1987.

[32]
M. A. Saunders P. E. Gill, W. Murray and M. H. Wright. A practical anti-cycling procedure for linearly constrained optimization. Mathematical Programming, 45:437–474, 1989.

[33]
M. A. Saunders P. E. Gill, W. Murray and M. H. Wright. A practical anti-cycling procedure for linearly constrained optimization. Mathematical Programming, 45:437–474, 1989.

[34]
M. A. Saunders P. E. Gill, W. Murray and M. H. Wright. Inertia-controlling methods for general quadratic programming. SIAM Review, 33:1–36, 1991.

[35]
W. Murray P. E. Gill and M. A. Saunders. User's guide for npopt: a fortran package for nonlinear programming.

[36]
W. Murray P. E. Gill and M. A. Saunders. SNOPT: An SQP algorithm for large-scale constrained optimization. SIAM J. Optim., 12:979–1006, 2002.

[37]
W. Murray P. E. Gill and M. H. Wright. Practical Optimization. 1981.

[38]
ed. P. M. Pardalos. Some theoretical properties of an augmented Lagrangian merit function. Advances in Optimization and Parallel Computing, North Holland, pages 101–128, 1992.

[39]
P. M. Pardalos and G. Schnitger. Checking local optimality in constrained quadratic programming is NP-hard. Operations Research Letters, 7:33–35, 1988.

[40]
R. H. Byrd R. Fletcher, P. T. Boggs and R. B. Schnabel. An ℓ1 penalty method for nonlinear constraints. Numerical Optimization 1984, eds., Philadelphia, pages 26–40, 1985.

[41]
J. K. Reid. Fortran subroutines for handling sparse linear programming bases. Report R8269, 1976.

[42]
J. K. Reid. A sparsity-exploiting variant of the bartels-golub decomposition for linear programming bases. Mathematical Programming, 24:55–69, 1982.

[43]
S. M. Robinson. A quadratically convergent algorithm for general nonlinear programming problems. Mathematical Programming, 3:145–156, 1972.

[44]
P. Wolfe. The reduced-gradient method. 1962.

« Previous « Start