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A quadratic programming algorithm for large and sparse problems
Kybernetika, Tome 27 (1991) no. 2, pp. 155-167 Cet article a éte moissonné depuis la source Czech Digital Mathematics Library

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Classification : 90-08, 90C06, 90C20 
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     language = {en},
     url = {http://geodesic.mathdoc.fr/item/KYB_1991_27_2_a6/}
}
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Tůma, Miroslav. A quadratic programming algorithm for large and sparse problems. Kybernetika, Tome 27 (1991) no. 2, pp. 155-167. http://geodesic.mathdoc.fr/item/KYB_1991_27_2_a6/

[1] R. G. Bland: New finite pivoting rules for the simplex method. Math. Oper. Res. 2 (1977), 103-107. | MR | Zbl

[2] P. H. Calamai, J. J. More: Projected gradient methods for linearly constrained problems. Math. Programming 39 (1987), 93-116. | MR | Zbl

[3] T. F. Coleman: Large Sparse Numerical Optimization. (Lecture Notes in Computer Science 165.) Springer-Verlag, Berlin-Heidelberg-New York-Tokyo 1984. | MR | Zbl

[4] R. S. Dembo: NLPNET - User's Guide and System Documentation. School of Organiza- tion and Management Working Paper Series B # 70, Yale University, New Haven, CT 1983.

[5] R. S. Dembo: A primal truncated Newton algorithm with application to large-scale nonlinear network optimization. Math. Programming Study 31 (1987), 43-71. | MR | Zbl

[6] R. S. Dembo S. C. Eisenstat, T. Steihaug: Inexact Newton methods. SIAM J. Numer. Anal. 79(1982), 400-408. | MR

[7] R. S. Dembo, J. G. Klincewicz: Dealing with degeneracy in reduced gradient algorithms. Math. Programming 31 (1985), 357-363. | MR | Zbl

[8] R. S. Dembo, T. Sahi: A convergent active-set strategy for linearly-constrained optimization. School of Organization and Management Working Paper Series B # 80, Yale University 1984.

[9] R. S. Dembo, T. Steihaug: Truncated-Newton algorithms for large-scale unconstrained optimization. Math. Programming 26 (1983), 190-212. | MR | Zbl

[10] A. Drud: CONOPT: A GRG code for large sparse dynamic nonlinear optimization problems. Math. Programming 31 (1985), 153-191. | MR | Zbl

[11] L. F. Escudero: An Algorithm for Large-scale Quadratic Programming and its Extensions to the Linearly Constrained Case. IBM Scientic Centre Report SCR-01.81, Madrid 1981.

[12] Y. Fan L. Lasdon, S. Sarkar: Experiments with successive quadratic programming algorithms. J. Optim. Theory Appl. 56 (1988), 359-383. | MR

[13] R. Fletcher: Practical Methods of Optimization. Vol. 2: Constrained Optimization. J. Wiley, New York-Chichester-Brisbane-Toronto 1981. | MR | Zbl

[14] P. E. Gill, W. Murray: Newton-type methods for unconstrained and linearly constrained optimization. Math. Programming 28 (1974), 311 - 350. | MR | Zbl

[15] P. E. Gill, W. Murray: The Computation of Lagrange Multiplier Estimates for Constrained Minimization. Rep. NAC 77, National Physical Laboratory, England 1976.

[16] W. Hock, K. Schittkowski: Test Examples for NLP Codes. Springer-Verlag, Berlin-Heidelberg-New York 1981. | MR

[17] L. Luksan: System UFO. User's Guide-version 1989(in Czech). Res. Rep. V-441, SVT CSAV, Prague, 1989.

[18] H. M. Markowitz: The elimination form of the inverse and its applications to linear programming. Management Sci. 3 (1957), 225-269. | MR

[19] B. A. Murtagh: Advanced Linear Programming: Computation and Practice. McGraw Hill New York 1981. | MR | Zbl

[20] B. A. Murtagh, M. A. Saunders: Large-scale linearly constrained optimization. Math. Programming 14 (1978), 41 - 72. | MR | Zbl

[21] B. A. Murtagh, M. A. Saunders: A projected Lagrangian algorithm and its implementation for sparse nonlinear constraints. Math. Programming Study 16 (1982), 84-117. | MR | Zbl

[22] B. A. Murtagh, M. A. Saunders: MINOS 5.1 User's Guide. Tech. Rep. SOL 83-20 R, Dept. Oper. Res., Stanford University, Stanford, CA, 1983, revised 1987.

[23] O. Osterby, Z. Zlatev: Direct methods for sparse matrices. (Lecture Notes in Computer Science 157.) Springer-Verlag, Berlin -Heidelberg-New York-Tokyo 1983. | MR | Zbl

[24] S. Pissanetzky: Sparse Matrix Technology. Academic Press, London-Orlando-San Die- go-New York-Austin-Toronto-Montreal-Sydney-Tokyo 1984. | MR | Zbl

[25] J. K. Reid: On the method of conjugate gradients for the solution of large sparse systems of linear equations. In: Large Sparse Sets of Linear Equations (J. K. Reid, ed.), Academic Press, London 1971, pp. 231 - 254. | MR

[26] J. K. Reid: Fortran Subroutines for Handling Sparse Linear Programming Bases. Rep. AERE Harwell, R. 8269. Harwell 1976.

[27] J. K. Reid: A sparsity-exploiting variant of the Bartels-Golub decomposition for linear programming bases. Math. Programming 24 (1982), 55-69. | MR | Zbl

[28] P. S. Ritch: Discrete optimal control with multiple constraints I: Constraint separation and transformation technique. Automatica 9 (1973), 415-429. | MR | Zbl

[29] M. Tůma: Large and Sparse Quadratic Programming (in Czech). Ph.D. Thesis, SVT CSAV, Prague 1989.

[30] M. Tůma: SPOPT-Program System for the Solution of Large Sparse Problems of Linear and Quadratic Programming (in Czech). Res. Rep. V-391, SVT CSAV, Praha 1989.

[31] Z. Zlatev: A survey of the advances in the exploitation of the sparsity in the solution of large problems. Internat. Congress on Comp. and Appl. Math., University of Leuven, Belgium 1986. | MR