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FOCS
2004
IEEE
15 years 10 months ago
Approximating the Stochastic Knapsack Problem: The Benefit of Adaptivity
We consider a stochastic variant of the NP-hard 0/1 knapsack problem in which item values are deterministic and item sizes are independent random variables with known, arbitrary d...
Brian C. Dean, Michel X. Goemans, Jan Vondrá...
STOC
1995
ACM
145views Algorithms» more  STOC 1995»
15 years 10 months ago
Polynomial time approximation schemes for dense instances of NP-hard problems
We present a unified framework for designing polynomial time approximation schemes (PTASs) for “dense” instances of many NP-hard optimization problems, including maximum cut,...
Sanjeev Arora, David R. Karger, Marek Karpinski
AAIM
2010
Springer
181views Algorithms» more  AAIM 2010»
15 years 10 months ago
Efficient Exact and Approximate Algorithms for the Complement of Maximal Strip Recovery
Given two genomic maps G and H represented by a sequence of n gene markers, a strip (syntenic block) is a sequence of distinct markers of length at least two which appear as subseq...
Binhai Zhu
EWRL
2008
15 years 8 months ago
New Error Bounds for Approximations from Projected Linear Equations
We consider linear fixed point equations and their approximations by projection on a low dimensional subspace. We derive new bounds on the approximation error of the solution, whi...
Huizhen Yu, Dimitri P. Bertsekas
ACL
2006
15 years 8 months ago
Approximation Lasso Methods for Language Modeling
Lasso is a regularization method for parameter estimation in linear models. It optimizes the model parameters with respect to a loss function subject to model complexities. This p...
Jianfeng Gao, Hisami Suzuki, Bin Yu