We consider two fundamental problems in stochastic optimization: approximation algorithms for stochastic matching, and sampling bounds in the black-box model. For the former, we improve the current-best bound of 3.709 due to Adamczyk et al. (2015), to 3.224; we also present improvements on Bansal et al. (2012) for hypergraph matching and for relaxed versions of the problem. In the context of stochastic optimization, we improve upon the sampling bounds of Charikar et al. (2005).
@InProceedings{baveja_et_al:LIPIcs.APPROX-RANDOM.2015.124, author = {Baveja, Alok and Chavan, Amit and Nikiforov, Andrei and Srinivasan, Aravind and Xu, Pan}, title = {{Improved Bounds in Stochastic Matching and Optimization}}, booktitle = {Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2015)}, pages = {124--134}, series = {Leibniz International Proceedings in Informatics (LIPIcs)}, ISBN = {978-3-939897-89-7}, ISSN = {1868-8969}, year = {2015}, volume = {40}, editor = {Garg, Naveen and Jansen, Klaus and Rao, Anup and Rolim, Jos\'{e} D. P.}, publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik}, address = {Dagstuhl, Germany}, URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.APPROX-RANDOM.2015.124}, URN = {urn:nbn:de:0030-drops-52991}, doi = {10.4230/LIPIcs.APPROX-RANDOM.2015.124}, annote = {Keywords: stochastic matching, approximation algorithms, sampling complexity} }
Feedback for Dagstuhl Publishing