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CORR
2004
Springer
103views Education» more  CORR 2004»
15 years 6 months ago
Online convex optimization in the bandit setting: gradient descent without a gradient
We study a general online convex optimization problem. We have a convex set S and an unknown sequence of cost functions c1, c2, . . . , and in each period, we choose a feasible po...
Abraham Flaxman, Adam Tauman Kalai, H. Brendan McM...
225
Voted
FOCS
2008
IEEE
16 years 1 months ago
Beating the Random Ordering is Hard: Inapproximability of Maximum Acyclic Subgraph
We prove that approximating the Max Acyclic Subgraph problem within a factor better than 1/2 is Unique-Games hard. Specifically, for every constant ε > 0 the following holds:...
Venkatesan Guruswami, Rajsekar Manokaran, Prasad R...
JACM
2006
99views more  JACM 2006»
15 years 7 months ago
Finding a maximum likelihood tree is hard
Abstract. Maximum likelihood (ML) is an increasingly popular optimality criterion for selecting evolutionary trees [Felsenstein 1981]. Finding optimal ML trees appears to be a very...
Benny Chor, Tamir Tuller
221
Voted
WCNC
2008
IEEE
16 years 1 months ago
Proportional Fair Scheduling: Analytical Insight under Rayleigh Fading Environment
—This paper provides analytical expressions to evaluate the performance of a random access wireless network in terms of user throughput and network throughput, subject to the con...
Erwu Liu, Kin K. Leung
COMPGEOM
2006
ACM
16 years 1 months ago
Plane embeddings of planar graph metrics
Embedding metrics into constant-dimensional geometric spaces, such as the Euclidean plane, is relatively poorly understood. Motivated by applications in visualization, ad-hoc netw...
MohammadHossein Bateni, Mohammad Taghi Hajiaghayi,...