Approximation Algorithms

One of the methods of dealing with hard optimization problems is applying approximation algorithms. An approximation algorithm is a suboptimal procedure, which provably works fast and provably returns a feasible solution of a high quality. Denote by OPT t

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1. Adlakha, V., Gladysz, B., Kamburowski, J.: Minimum Flows in (s,t) planar networks. Networks 21, 767–773 (1991) 2. Ahuja, R.K., Magnanti, T.L., Orlin, J.B.: Network flows, theory, algorithms and applications. Prentice Hall, New Jersey (1993) 3. Ahuja, R.K., Ergun, O., Orlin, J., Punnen, A.P.: A survey of very large-scale neighborhood search techniques. Discrete Applied Mathematics 123, 75–102 (2002) 4. Aissi, H., Bazgan, C., Vanderpooten, D.: Complexity of the min-max and min-max regret assignment problems. Operations Research Letters 33(6), 634–640 (2005) 5. Aissi, H., Bazgan, C., Vanderpooten, D.: Complexity of the Min-Max (Regret) Versions of Cut Problems. In: Deng, X., Du, D.-Z. (eds.) ISAAC 2005. LNCS, vol. 3827, pp. 789–798. Springer, Heidelberg (2005) 6. Aissi, H., Bazgan, C., Vanderpooten, D.: Pseudo-polynomial time algorithms for min-max and min-max regret problems. The 5th International Symposium on Operations Research and Its Applications (ISORA 2005), LNOR 5, 171–178 (2005) 7. Aissi, H., Bazgan, C., Vanderpooten, D.: Approximation Complexity of min-max (Regret) Versions of Shortest Path, Spanning Tree, and Knapsack. In: Brodal, G.S., Leonardi, S. (eds.) ESA 2005. LNCS, vol. 3669, pp. 862–873. Springer, Heidelberg (2005) 8. Aissi, H., Bazgan, C., Vanderpooten, D.: Approximation of min-max and min-max regret versions of some combinatorial optimization problems. European Journal of Operational Research 179(2), 281–290 (2007) 9. Aissi, H., Bazgan, C., Vanderpooten, D.: Approximating Min-Max (Regret) Versions of Some Polynomial Problems. In: Chen, D.Z., Lee, D.T. (eds.) COCOON 2006. LNCS, vol. 4112, pp. 428–438. Springer, Heidelberg (2006) 10. Amoia, A., Cottafava, G.: Invariance properties of central trees. IEEE Trans. Circuit Theory CT-18, 465–467 (1971) 11. Armon, A., Zwick, U.: Multicriteria Global Minimum Cuts. In: Fleischer, R., Trippen, G. (eds.) ISAAC 2004. LNCS, vol. 3341, pp. 65–76. Springer, Heidelberg (2004) 12. Aron, I., van Hentenryck, P.: A constraint satisfaction approach to the robust spanning tree problem with interval data. In: Proceedings of the 18th Conference on Uncertainty in Artificial Intelligence, Edmonton, Canada, pp. 18–25 (2002) 13. Aron, I., van Hentenryck, P.: On the complexity of the robust spanning tree problem with interval data. Operations Research Letters 32(1), 36–40 (2004)

210

References

14. Ausiello, G., Crescenzi, P., Gambosi, G., Kann, V., Marchetti-Spaccamela, A., Protasi, M.: Complexity and approximation. In: Combinatorial optimization problems and their approximability properties, Springer, Heidelberg (1999) 15. Averbakh, I.: Minmax regret solutions for minimax optimization problems with uncertainty. Operations Research Letters 27(2), 57–65 (2000) 16. Averbakh, I.: On the complexity of a class of combinatorial optimization problems with uncertainty. Math. Program. A 90, 263–272 (2001) 17. Averbakh, I., Lebedev, V.: Interval data minmax regret network optimization problems. Discrete Applied Mathematics 138(3), 289–301 (2004) 18. Averbakh, I.: Minmax regret line

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