Attenuate Locally, Win Globally: Attenuation-Based Frameworks for Online Stochastic Matching with Timeouts

Author:

Brubach Brian,Sankararaman Karthik A.ORCID,Srinivasan Aravind,Xu Pan

Funder

Directorate for Computer and Information Science and Engineering

Adobe Systems

Publisher

Springer Science and Business Media LLC

Subject

Applied Mathematics,Computer Science Applications,General Computer Science

Reference28 articles.

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2. Agrawal, S., Devanur, N.R.: Fast algorithms for online stochastic convex programming. In: Proceedings of the 26th Annual ACM–SIAM Symposium on Discrete Algorithms (Philadelphia, PA, USA, 2015), SODA’15, pp. 1405–1424. Society for Industrial and Applied Mathematics, New York

3. Agrawal, S., Wang, Z., Ye, Y.: A dynamic near-optimal algorithm for online linear programming. Oper. Res. 62(4), 876–890 (2014)

4. Alaei, S., Hajiaghayi, M., Liaghat, V.: Online prophet-inequality matching with applications to ad allocation. In: Proceedings of the 13th ACM Conference on Electronic Commerce (New York, NY, USA, 2012), EC’12, pp. 18–35. ACM, New York

5. Alaei, S., Hajiaghayi, M., Liaghat, V.: The online stochastic generalized assignment problem. In: Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques: 16th International Workshop, APPROX 2013, and 17th International Workshop, RANDOM 2013, Berkeley, CA, USA, August 21–23: Proceedings (Berlin, Heidelberg, 2013), pp. 11–25. Springer, Berlin (2013)

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