Randomized relaxation methods for the maximum feasible subsystem problem

Lecture Notes in Computer Science

Volumn 3509, Issue , 2005, Pages 249-264

  Amaldi, Edoardo ^a   Belotti, Pietro ^a   Hauser, Raphael ^b  

^a POLITECNICO DI MILANO   (Italy)

^b UNIVERSITY OF OXFORD   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

BROADCASTING; COMPUTATION THEORY; COMPUTER SIMULATION; FUNCTIONS; ITERATIVE METHODS; LINEAR SYSTEMS; PROBABILITY; PROBLEM SOLVING; PROTEINS; TELECOMMUNICATION;

CLASSICAL RELAXATION METHOD; FEASIBLE SUBSYSTEMS; OPTIMAL SOLUTIONS; RANDOMIZED RELAXATION METHODS;

RANDOM PROCESSES;

EID: 24944496727     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/11496915_19     Document Type: Conference Paper

References (23)

1. E. Amaldi. The maximum feasible subsystem problem and some applications. In A. Agnetis and G. Di Pillo, editors, Modelli e Algoritmi per l'ottimizzazione di sistemi complessi. Pitagora Editrice Bologna, 2003.
2. E. Amaldi and R. Hauser. Boundedness theorems for the relaxation method. Under minor revision for Mathematics of Oper. Res., available from Optimization Online.
3. E. Amaldi and V. Kann. The complexity and approximability of finding maximum feasible subsystems of linear relations. Theoretical Computer Science, 147:181-210, 1995.
4. E. Amaldi, M. E. Pfetsch, and L. E. Trotter Jr. On the maximum feasible subsystem problem, IISs and IIS-hypergraphs. Math. Programming A, 95:533-554, 2003.
5. K. P. Bennett and E. Bredensteiner. A parametric optimization method for machine learning. INFORMS Journal on Computing, 9:311-318, 1997.
6. H. D. Block and S. A. Levin. On the boundedness of an iterative procedure for solving a system of linear inequalities. In Proceedings of AMS, pages 229-235, 1970.
7. Y. Censor and S. A. Zenios. Parallel Optimization: Theory, algorithms and applications. Oxford University Press, 1997.
8. J. Chinneck. Fast heuristics for the maximum feasible subsystem problem. INFORMS Journal on Computing, 13:210-213, 2001.
9. G. Codato and M. Fischetti. Combinatorial Benders' cuts. In Proceedings of IPCO, volume 3064 of LNCS, pages 178-195. Springer, 2004.
10. J. Dunagan and S. Vempala. A simple polynomial-time rescaling algorithm for solving linear programs. In Proceedings of STOC, pages 315-320. ACM Press, 2004.
11. M. Frean. A "thermal" perceptron learning rule. Neural Comp., 4(6):946-957, 1992.
12. J. L. Coffin. The relaxation method for solving systems of linear inequalities. Mathematics of Oper. Res., 5:388-414, 1980.
13. H. J. Greenberg and F. H. Murphy. Approaches to diagnosing infeasible linear programs. ORSA Journal on Computing, 3:253-261, 1991.
14. E. K. Lee, R. J. Gallagher, and M. Zaider. Planning implants of radionuclides for the treatment of prostate cancer: An application of MIP. Optima, 61:1-7, 1999.
15. O. Mangasarian. Machine learning via polyhedral concave minimization. In H. Fischer et al., editor, Applied Mathematics and Parallel Computing, pages 175 - 188. Physica-Verlag, 1996.
16. M. Mattavelli, V. Noel, and E. Amaldi. Fast line detection algorithms based on combinatorial optimization. In Proceedings of IWVF, LCNS 2059, pages 410-419. Springer, 2001.
17. J. Meller, M. Wagner, and R. Elber. Solving huge linear programming problems for the design of protein folding potentials. Math. Programming B, 101:301-318, 2004.
18. M. L. Minsky and S. Papert. Perceptrons: An introduction to computational Geometry. MIT Press, Cambridge, MA, 1988. Expanded edition.
19. A. Nedić and D. Bertsekas. Incremental subgradient methods for nondifferentiable optimization. SIAM J. on Optimization, 12:109-138, 2001.
20. M. E. Pfetsch. The maximum feasible subsystem problem and vertex-facet incidences of polyhedra. PhD thesis, Dep. of Mathematics, Technische Universität Berlin, October 2002.
21. B. T. Polyak. Random algorithms for solving convex inequalities. In D. Butnariu et al., editor, Inherently parallel algorithms in feasibility and other applications. Elsevier, 2001.
22. F. Rossi, A. Sassano, and S. Smriglio. Models and algorithms for terrestrial digital broadcasting. Ann. of Oper. Res., 107(3):267-283, 2001.
23. A. Schrijver. Theory of Linear and Integer Programming. Wiley & Sons, 1986.