Performance Guarantees of Local Search for Multiprocessor Scheduling

Published Online:https://doi.org/10.1287/ijoc.1050.0152

References

  • Ahuja R. K., Orlin J. B., Sharma D. New neighborhood search structures for the capacitated minimum spanning tree problem. Math. Programming (2001) 91:71–97CrossrefGoogle Scholar
  • Arkin E. M., Hassin R. On local search for weighted k-set packing. Math. Oper. Res. (1998) 23:640–648LinkGoogle Scholar
  • Arya V., Garg N., Khandekar R., Meyerson A., Munagala K., Pandit V. Local search heuristic for k-median and facility location problems. SIAM J. Comput. (2004) 33:544–562CrossrefGoogle Scholar
  • Ausiello G., Protasi M. Local search, reducibility and approximability of NP optimization problems. Inform. Processing Lett. (1995) 54:73–79CrossrefGoogle Scholar
  • Boykov Y., Veksler O., Zabih R. A new algorithm for energy minimization with discontinuities. Internat. Workshop Energy Minimization Methods Comput. Vision and Pattern Recognition (1999) (Springer, Berlin, Germany) 205–220CrossrefGoogle Scholar
  • Brucker P., Hurink J., Werner F. Improving local search heuristics for some scheduling problems I. Discrete Appl. Math. (1996) 65:97–122CrossrefGoogle Scholar
  • Brucker P., Hurink J., Werner F. Improving local search heuristics for some scheduling problems II. Discrete Appl. Math. (1997) 72:47–69CrossrefGoogle Scholar
  • Brüggemann T., Monnot J., Woeginger G. J. Local search for the minimum label spanning tree problem with bounded color classes. Oper. Res. Lett. (2003) 31:195–201CrossrefGoogle Scholar
  • Charikar M., Guha S. Improved combinatorial algorithms for the facility location and k-median problems. Proc. 40th Annual IEEE Sympos. Foundations Comput. Sci. (1999) 378–388CrossrefGoogle Scholar
  • Cho Y., Sahni S. Bounds for list schedules on uniform processors. SIAM J. Comput. (1980) 9:91–103CrossrefGoogle Scholar
  • Chudak F. A., Williamson D. P. Improved approximation algorithms for capacitated facility location problems. Math. Programming (2005) 102:207–222CrossrefGoogle Scholar
  • De Bontridder K. M. J., Halldórsson B. V., Halldórsson M. M., Hurkens C. A. J., Lenstra J. K., Ravi R., Stougie L. Approximation algorithms for the test cover problem. Math. Programming (2003) 98:477–491CrossrefGoogle Scholar
  • Feige U., Karpinski M., Langberg M. Improved approximation of Max-Cut on graphs of bounded degree. J. Algorithms (2002) 43:201–219CrossrefGoogle Scholar
  • Finn G., Horowitz E. A linear time approximation algorithm for multiprocessor scheduling. BIT (1979) 19:312–320CrossrefGoogle Scholar
  • Frangioni A., Necciari E., Scutellà M. G. A multi-exchange neighborhood for minimum makespan parallel machine scheduling problems. J. Combin. Optim. (2004) 8:195–220CrossrefGoogle Scholar
  • Garey M. R., Johnson D. S.Computers and Intractability: A Guide to the Theory of NP-Completeness (1979) (Freeman, San Francisco, CA) Google Scholar
  • Goemans M. X., Williamson D. P. Improved approximation algorithms for maximum cut and satisfiability problems using semidefinite programming. J. ACM (1995) 42:1115–1145CrossrefGoogle Scholar
  • Graham R. L., Lawler E. L., Lenstra J. K., Rinnooy Kan A. H. G. Optimization and approximation in deterministic sequencing and scheduling: A survey. Ann. Discrete Math. (1979) 5:287–326CrossrefGoogle Scholar
  • Gupta A., Tardos É. A constant factor approximation algorithm for a class of classification problems. Proc. 32nd ACM Sympos. Theory Comput. (2000) 652–658CrossrefGoogle Scholar
  • Hochbaum D. S., Shmoys D. B. Using dual approximation algorithms for scheduling problems: Theoretical and practical results. J. ACM (1987) 34:144–162CrossrefGoogle Scholar
  • Hochbaum D. S., Shmoys D. B. A polynomial approximation scheme for machine scheduling on uniform processors: Using the dual approximation approach. SIAM J. Comput. (1988) 17:539–551CrossrefGoogle Scholar
  • Hurkens C. A. J., Schrijver A. On the size of systems of sets every t of which have an sdr, with an application to the worst-case ratio of heuristics for packing problems. SIAM J. Discrete Math. (1989) 2:68–72CrossrefGoogle Scholar
  • Hurkens C. A. J., Vredeveld T. Local search for multiprocessor scheduling: How many moves does it take to a local optimum? Oper. Res. Lett. (2003) 31:137–141CrossrefGoogle Scholar
  • Kernighan B. W., Lin S. An efficient heuristic procedure for partitioning graphs. Bell System Tech. J. (1970) 49:291–307CrossrefGoogle Scholar
  • Korupolu M. R., Plaxton C. G., Rajaraman R. Analysis of a local search heuristic for facility location problems. J. Algorithms (2000) 37:146–188CrossrefGoogle Scholar
  • Lenstra J. K., Shmoys D. B., Tardos É. Approximation algorithms for scheduling unrelated parallel machines. Math. Programming (1990) 46:259–271CrossrefGoogle Scholar
  • Lin S., Kernighan B. W. An effective heuristic for the traveling salesman problem. Oper. Res. (1973) 21:498–516LinkGoogle Scholar
  • Lu H., Ravi R. The power of local optimization: Approximation for maximum-leaf spanning tree. Proc. 13th Annual Allerton Conf. Communications, Control, and Computing (1992) 533–542Google Scholar
  • Pál M., Tardos Éva, Wexler T. Facility location with nonuniform hard capacities. Proc. 42nd Annual IEEE Sympos. the Foundations Comput. Sci. (2001) 329–338CrossrefGoogle Scholar
  • Schuurman P., Vredeveld T. Performance guarantees of local search for multiprocessor scheduling. Proc. 8th Integer Programming and Combinatorial Optimization Conf., Lecture Notes Computer Science (2001) 2081(Springer, Berlin, Germany) 370–382CrossrefGoogle Scholar
  • Vredeveld T., Lenstra J. K. On local search for the generalized graph coloring problem. Oper. Res. Lett. (2003) 31:28–34CrossrefGoogle Scholar
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