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APPROX
2005
Springer
122views Algorithms» more  APPROX 2005»
14 years 5 months ago
Finding a Maximum Independent Set in a Sparse Random Graph
We consider the problem of finding a maximum independent set in a random graph. The random graph G, which contains n vertices, is modelled as follows. Every edge is included inde...
Uriel Feige, Eran Ofek
APPROX
2005
Springer
95views Algorithms» more  APPROX 2005»
14 years 5 months ago
Approximation Algorithms for Requirement Cut on Graphs
Viswanath Nagarajan, R. Ravi
APPROX
2005
Springer
150views Algorithms» more  APPROX 2005»
14 years 5 months ago
A Primal-Dual Approximation Algorithm for Partial Vertex Cover: Making Educated Guesses
We study the partial vertex cover problem. Given a graph G = (V, E), a weight function w : V → R+ , and an integer s, our goal is to cover all but s edges, by picking a set of v...
Julián Mestre
APPROX
2005
Springer
114views Algorithms» more  APPROX 2005»
14 years 5 months ago
Finding Graph Matchings in Data Streams
Andrew McGregor
APPROX
2005
Springer
111views Algorithms» more  APPROX 2005»
14 years 5 months ago
A Lower Bound on List Size for List Decoding
A q-ary error-correcting code C ⊆ {1, 2, . . . , q}n is said to be list decodable to radius ρ with list size L if every Hamming ball of radius ρ contains at most L codewords o...
Venkatesan Guruswami, Salil P. Vadhan
APPROX
2005
Springer
71views Algorithms» more  APPROX 2005»
14 years 5 months ago
What Would Edmonds Do? Augmenting Paths and Witnesses for Degree-Bounded MSTs
Kamalika Chaudhuri, Satish Rao, Samantha Riesenfel...
APPROX
2005
Springer
105views Algorithms» more  APPROX 2005»
14 years 5 months ago
The Complexity of Making Unique Choices: Approximating 1-in- k SAT
We study the approximability of 1-in-kSAT, the variant of Max kSAT where a clause is deemed satisfied when precisely one of its literals is satisfied. We also investigate differ...
Venkatesan Guruswami, Luca Trevisan
APPROX
2005
Springer
111views Algorithms» more  APPROX 2005»
14 years 5 months ago
Sampling Bounds for Stochastic Optimization
A large class of stochastic optimization problems can be modeled as minimizing an objective function f that depends on a choice of a vector x ∈ X, as well as on a random external...
Moses Charikar, Chandra Chekuri, Martin Pál
APPROX
2005
Springer
104views Algorithms» more  APPROX 2005»
14 years 5 months ago
Bounds for Error Reduction with Few Quantum Queries
We consider the quantum database search problem, where we are given a function f : [N] → {0, 1}, and are required to return an x ∈ [N] (a
Sourav Chakraborty, Jaikumar Radhakrishnan, Nandak...