{"id":9,"date":"2024-11-01T17:54:15","date_gmt":"2024-11-01T17:54:15","guid":{"rendered":"https:\/\/maxfs.deib.polimi.it\/?page_id=9"},"modified":"2024-11-02T06:58:41","modified_gmt":"2024-11-02T06:58:41","slug":"bibliography","status":"publish","type":"page","link":"https:\/\/maxfs.deib.polimi.it\/?page_id=9","title":{"rendered":"Bibliography"},"content":{"rendered":"\n
    \n
  1. E. Amaldi and R. Hauser. Boundedness theorems for the relaxation method. Under minor revision for Mathematics of Oper. Res., available from Optimization Online.<\/li>\n\n\n\n
  2. E. Amaldi and V. Kann. The complexity and approximability of finding maximum feasible subsystems of linear relations. Theoretical Computer Science, 147:181-210, 1995.<\/li>\n\n\n\n
  3. E. Amaldi, M. E. Pfetsch, and L. E. Trotter Jr. On the maximum feasible subsystem problem, IISs and IIS-hypergraphs.\u00a0 Math. Programming A, 95:533-554, 2003.<\/li>\n\n\n\n
  4. K. P. Bennett and E. Bredensteiner. A parametric optimization method for machine learning. INFORMS Journal on Computing, 9:311-318, 1997.<\/li>\n\n\n\n
  5. 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.<\/li>\n\n\n\n
  6. Y. Censor and S. A. Zenios. Parallel Optimization: Theory, algorithms and applications. Oxford University Press, 1997.<\/li>\n\n\n\n
  7. J. Chinneck. Fast heuristics for the maximum feasible subsystem problem. INFORMS Journal on Computing, 13:210-213, 2001.<\/li>\n\n\n\n
  8. G. Codato and M. Fischetti. Combinatorial Benders’ cuts. In Proceedings of IPCO, volume 3064 of LNCS, pages 178-195. Springer, 2004.<\/li>\n\n\n\n
  9. 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.<\/li>\n\n\n\n
  10. M. Frean. A “thermal” perceptron learning rule. Neural Comp., 4(6):946-957, 1992.<\/li>\n\n\n\n
  11. J. L. Goffin. The relaxation method for solving systems of linear inequalities. Mathematics of Oper. Res., 5:388-414, 1980.<\/li>\n\n\n\n
  12. H. J. Greenberg and F. H. Murphy. Approaches to diagnosing infeasible linear programs. ORSA Journal on Computing, 3:253-261, 1991.<\/li>\n\n\n\n
  13. 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.<\/li>\n\n\n\n
  14. 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.<\/li>\n\n\n\n
  15. 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.<\/li>\n\n\n\n
  16. 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.<\/li>\n\n\n\n
  17. M. L. Minsky and S. Papert. Perceptrons: An introduction to computational Geometry. MIT Press, Cambridge, MA, 1988. Expanded edition.<\/li>\n\n\n\n
  18. A. Nedic and D. Bertsekas. Incremental subgradient methods for nondifferentiable optimization. SIAM J. on Optimization, 12:109-138, 2001.<\/li>\n\n\n\n
  19. M. E. Pfetsch. The maximum feasible subsystem problem and vertex-facet incidences of polyhedra. PhD thesis, Dep. of Mathematics, Technische Universit\u00e4t Berlin, October 2002.<\/li>\n\n\n\n
  20. 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.<\/li>\n\n\n\n
  21. F. Rossi, A. Sassano, and S. Smriglio. Models and algorithms for terrestrial digital broadcasting. Ann. of Oper. Res., (107):267-283, 2001.<\/li>\n\n\n\n
  22. A. Schrijver. Theory of Linear and Integer Programming. Wiley & Sons, 1986.<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":2,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-9","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/maxfs.deib.polimi.it\/index.php?rest_route=\/wp\/v2\/pages\/9","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/maxfs.deib.polimi.it\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/maxfs.deib.polimi.it\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/maxfs.deib.polimi.it\/index.php?rest_route=\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/maxfs.deib.polimi.it\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=9"}],"version-history":[{"count":3,"href":"https:\/\/maxfs.deib.polimi.it\/index.php?rest_route=\/wp\/v2\/pages\/9\/revisions"}],"predecessor-version":[{"id":33,"href":"https:\/\/maxfs.deib.polimi.it\/index.php?rest_route=\/wp\/v2\/pages\/9\/revisions\/33"}],"wp:attachment":[{"href":"https:\/\/maxfs.deib.polimi.it\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=9"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}