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Fast Approximation Algorithms for Fractional Packing and Covering Problems
, 1995
"... This paper presents fast algorithms that find approximate solutions for a general class of problems, which we call fractional packing and covering problems. The only previously known algorithms for solving these problems are based on general linear programming techniques. The techniques developed ..."
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Cited by 260 (13 self)
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This paper presents fast algorithms that find approximate solutions for a general class of problems, which we call fractional packing and covering problems. The only previously known algorithms for solving these problems are based on general linear programming techniques. The techniques
Fast Approximation Algorithms for Multicommodity Flow Problems
 JOURNAL OF COMPUTER AND SYSTEM SCIENCES
, 1991
"... All previously known algorithms for solving the multicommodity flow problem with capacities are based on linear programming. The best of these algorithms [15] uses a fast matrix multiplication algorithm and takes O(k 3:5 n 3 m :5 log(nDU )) time for the multicommodity flow problem with inte ..."
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Cited by 191 (21 self)
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All previously known algorithms for solving the multicommodity flow problem with capacities are based on linear programming. The best of these algorithms [15] uses a fast matrix multiplication algorithm and takes O(k 3:5 n 3 m :5 log(nDU )) time for the multicommodity flow problem
Fast Approximation Algorithm for Minimum Cost Multicommodity Flow
, 1995
"... Minimumcost multicommodity flow problem is one of the classical optimization problems that arises in a variety of contexts. Applications range from finding optimal ways to route information through communication networks to VLSI layout. In this paper, we describe an efficient deterministic approxim ..."
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Cited by 12 (2 self)
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approximation algorithm, which given that there exists a multicommodity flow of cost B that satisfies all the demands, produces a flow of cost at most (1 + ffi )B that satisfies (1 \Gamma ffl)fraction of each demand. For constant ffi and ffl, our algorithm runs in O (kmn 2 ) time, which is an improvement
A Fast Approximation Algorithm for the SubsetSum Problem
, 1999
"... The subsetsum problem (SSP) is defined as follows: given a positive integer bound and a set of n positive integers find a subset whose sum is closest to, but not greater than, the bound. We present a randomized approximation algorithm for this problem with linear space complexity and time complexit ..."
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Cited by 8 (0 self)
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The subsetsum problem (SSP) is defined as follows: given a positive integer bound and a set of n positive integers find a subset whose sum is closest to, but not greater than, the bound. We present a randomized approximation algorithm for this problem with linear space complexity and time
A fast approximation algorithm for treesparse recovery
 In International Symposium on Information Theory (ISIT
, 2014
"... Abstract—Sparse signals whose nonzeros obey a treelike structure occur in a range of applications such as image modeling, genetic data analysis, and compressive sensing. An important problem encountered in recovering signals is that of optimal treeprojection, i.e., finding the closest treesparse ..."
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Cited by 3 (3 self)
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sparse recovery. Our approach is based on a specific approximation algorithm for treeprojection and provably has a nearlinear runtime of O(n log(kr)) and a memory cost of O(n), where r is the dynamic range of the signal. We leverage this approach in a fast recovery algorithm for treesparse compressive sensing
A Fast Approximate Algorithm for LargeScale Latent Semantic Indexing
, 2008
"... Latent Semantic Indexing (LSI) is an effective method to discover the underlying semantic structure of data. It has numerous applications in information retrieval and data mining. However, the computational complexity of LSI may be prohibitively high when applied to very large datasets. In this pape ..."
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Cited by 1 (0 self)
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. In this paper, we present a fast approximate algorithm for largescale LSI that is conceptually simple and theoretically justified. Our main contribution is to show that the proposed algorithm has provable error bound and linear computational complexity.
Fast Approximation Algorithms for Cutbased Problems in Undirected Graphs
"... We present a general method of designing fast approximation algorithms for cutbased minimization problems in undirected graphs. In particular, we develop a technique that given any such problem that can be approximated quickly on trees, allows approximating it almost as quickly on general graphs wh ..."
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Cited by 19 (3 self)
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We present a general method of designing fast approximation algorithms for cutbased minimization problems in undirected graphs. In particular, we develop a technique that given any such problem that can be approximated quickly on trees, allows approximating it almost as quickly on general graphs
Near Optimal Online Algorithms and Fast Approximation Algorithms for Resource Allocation Problems
, 2011
"... We present algorithms for a class of resource allocation problems both in the online setting with stochastic input and in the offline setting. This class of problems contains many interesting special cases such as the Adwords problem. In the online setting we introduce a new distributional model cal ..."
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Cited by 33 (5 self)
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called the adversarial stochastic input model, which is a generalization of the i.i.d model with unknown distributions, where the distributions can change over time. In this model we give a 1 − O(ǫ) approximation algorithm for the resource allocation problem, with almost the weakest possible assumption
A Fast Approximation Algorithm for TSP with Neighborhoods and RedBlue Separation
 Nordic Journal of Computing
, 1997
"... In TSP with neighborhoods (TSPN) we are given a collection X of k polygonal regions, called neighborhoods, with totally n vertices, and we seek the shortest tour that visits each neighborhood. The Euclidean TSP is a special case of the TSPN problem, so TSPN is also NPhard. In this paper we pre ..."
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Cited by 32 (5 self)
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present a simple and fast algorithm that, given a start point, computes a TSPN tour of length O(log k) times the optimum in time O(n+k log k). When no start point is given we show how to compute a \good" start point in time O(n 2 log n), hence we obtain a logarithmic approximation algorithm
Results 1  10
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1,749,332