Approximation algorithms for stochastic and risk-averse optimization

Proceedings of the Annual ACM-SIAM Symposium on Discrete Algorithms

Volumn 07-09-January-2007, Issue , 2007, Pages 1305-1313

  Srinivasan, Aravind ^a  

^a University of Maryland   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; INTEGER PROGRAMMING; OPTIMIZATION; STAGES; STOCHASTIC SYSTEMS;

APPROXIMABILITY; COVERING INTEGER PROGRAM; FACILITY LOCATIONS; MULTI STAGE; NON-STOCHASTIC; RISK AVERSE; STOCHASTIC OPTIMIZATIONS; VERTEX COVER;

APPROXIMATION ALGORITHMS;

EID: 84969286364     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (34)

1. E. M. L. Beale. On minimizing a convex function subject to linear inequalities. Journal of the Royal Statistical Society, Series B, 17:173-184; discussion 194-203, 1955.
2. M. Charikar, C. Chekuri, and M. Pál. Sampling bounds for stochastic optimization. Proc. 9th RANDOM, pages 257-269, 2005.
3. F. Chudak and D. B. Shmoys. Improved approximation algorithms for the uncapacitated facility location problem. SIAM Journal on Computing, 33(1):1-25, 2003.
4. G. B. Dantzig. Linear programming under uncertainty. Mgmt. Sc., 1:197-206, 1955.
5. B. Dean, M. Goemans, and J. Vondrak. Approximating the stochastic knapsack problem: the benefit of adaptivity. Proc. 45th Annual IEEE Symposium on Foundations of Computer Science, pages 208-217, 2004.
6. B. Dean, M. Goemans, and J. Vondrak. Adaptivity and approximation for stochastic packing problems. In Proc. Annual ACM-SIAM Symposium on Discrete Algorithms, pages 395-404, 2005.
7. K. Dhamdhere, V. Goyal, R. Ravi, and M Singh. How to pay, come what may: approximation algorithms for demand-robust covering problems. Proc. 46th Annual IEEE Symposium on Foundations of Computer Science, pages 367-378, 2005.
8. M. Dyer and L. Stougie. Computational complexity of stochastic programming problems. SPOR-Report 2005-11, Dept. of Mathematics and Computer Science, Eindhoven Technical University, Eindhoven, 2005.
9. S. Guha and S. Khuller. Greedy strikes back: Improved facility location algorithms. Journal of Algorithms, 31(1):228-248, 1999.
10. A. Gupta, M. Pál, R. Ravi, and A. Sinha. Boosted sampling: approximation algorithms for stochastic optimization. Proc. 36th Annual ACM Symposium on Theory of Computing, pages 417-426, 2004.
11. A. Gupta, M. Pál, R. Ravi, and A. Sinha. What about Wednesday? Approximation algorithms for multistage stochastic optimization. Proc. 8th APPROX, pages 86-98, 2005.
12. A. Gupta, R. Ravi, and A. Sinha. An edge in time saves nine: LP rounding approximation algorithms for stochastic network design. Proc. 45th Annual IEEE Symposium on Foundations of Computer Science, pages 218-227, 2004.
13. N. Immorlica, D. Karger, M. Minkoff, and V. Mirrokni. On the costs and benefits of procrastination: approximation algorithms for stochastic combinatorial optimization problems. In Proc. Annual ACM-SIAM Symposium on Discrete Algorithms, pages 684-693, 2004.
14. K. Jain, M. Mahdian, E. Markakis, A. Saberi, and V. Vazirani. Greedy facility location algorithms analyzed using dual-fitting with factor-revealing LP. Journal of the ACM, 50(6):795-824, 2003.
15. K. Jain, M. Mahdian, and A. Saberi. A new greedy approach for facility location problems. In Proc. 34th Annual ACM Symposium on Theory of Computing, pages 731-740, 2002.
16. J. Linderoth, A. Shapiro, and R. K. Wright. The empirical behavior of sampling methods for stochastic programming. Annals of Operations Research, 142:215-241, 2006.
17. M. Mahdian, Y. Ye, and J. Zhang. Approximation algorithms for metric facility location problems. SIAM Journal on Computing, 36:411-432, 2006.
18. R. Möhring, A. Schulz, and M. Uetz. Approximation in stochastic scheduling: the power of LP based priority policies. JACM, 46:924-942, 1999.
19. M. Naor and R. M. Roth. Optimal file sharing in distributed networks. SIAM J. Comput., 24:158-183, 1995.
20. A. Panconesi and A. Srinivasan. Randomized distributed edge coloring via an extension of the Chernoff-Hoeffding bounds. SIAM J. Comput., 26:350-368, 1997.
21. P. Raghavan and C. D. Thompson. Randomized rounding: a technique for provably good algorithms and algorithmic proofs. Combinatorica, 7:365-374, 1987.
22. R. Ravi and A. Sinha. Hedging uncertainty: approximation algorithms for stochastic optimization problems. In Proc. 10th IPCO, pages 101-115, 2004.
23. A. Ruszczynski and A. Shapiro. Editors, Stochastic Programming, Volume 10 of Handbooks in Operations Research and Mgmt. Sc., North-Holland, Amsterdam, 2003.
24. D. B. Shmoys and C. Swamy. An approximation scheme for stochastic linear programming and its application to stochastic integer programs. JACM, to appear. Preliminary version appeared as \Stochastic optimization is (almost) as easy as deterministic optimization" in Proc. 45th Annual IEEE FOCS, pages 228-237, 2004.
25. D. B. Shmoys, É. Tardos, and K. I. Aardal. Approximation algorithms for facility location problems. In Proc. 29th Annual ACM Symposium on Theory of Computing, pages 265-274, 1997.
26. A. M-C. So, J. Zhang, and Y. Ye. Stochastic combinatorial optimization with controllable risk aversion level. In Proc. 9th APPROX, pages 224-235, 2006.
27. A. Srinivasan. Improved approximation guarantees for packing and covering integer programs. SIAM J. Comput., 29:648-670, 1999.
28. A. Srinivasan. New approaches to covering and packing problems. In Proc. ACM-SIAM Symposium on Discrete Algorithms, pages 567-576, 2001.
29. M. Sviridenko. An improved approximation algorithm for the metric uncapacitated facility location problem. In Proc. 9th IPCO, pages 240-257, 2002.
30. C. Swamy. Approximation Algorithms for Clustering Problems. Ph.D. thesis, Cornell University, Ithaca, NY, 2004.
31. C. Swamy. Approximation Algorithms for Clustering Problems. Updated version of Ph.D. thesis, 2006. Available at http://www.math.uwaterloo.ca/~cswamy.
32. C. Swamy and D. B. Shmoys. Sampling-based approximation algorithms for multi-stage stochastic optimization. In Proc. 46th Annual IEEE Symposium on Foundations of Computer Science, pages 357-366, 2005.
33. C. Swamy and D. B. Shmoys. Algorithms Column: Approximation Algorithms for 2-stage Stochastic Optimization Problems. SIGACT News, 37:33-46, 2006.
34. B. Verweij, S. Ahmed, A. J. Kleywegt, G. L. Nemhauser, and A. Shapiro. The sample average approximation method applied to stochastic routing problems: a computational study. Computational Optimization and Applications, 24:289-333, 2003.