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APPROX
2005
Springer
122views Algorithms» more  APPROX 2005»
13 years 10 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»
13 years 10 months ago
Approximation Algorithms for Requirement Cut on Graphs
Viswanath Nagarajan, R. Ravi
APPROX
2005
Springer
150views Algorithms» more  APPROX 2005»
13 years 10 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»
13 years 10 months ago
Finding Graph Matchings in Data Streams
Andrew McGregor
APPROX
2005
Springer
111views Algorithms» more  APPROX 2005»
13 years 10 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»
13 years 10 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»
13 years 10 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»
13 years 10 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»
13 years 10 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...