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An Optimal Local Approximation Algorithm for MaxMin Linear Programs
"... In a maxmin LP, the objective is to maximise ω subject to Ax ≤ 1, Cx ≥ ω1, and x ≥ 0 for nonnegative matrices A and C. We present a local algorithm (constanttime distributed algorithm) for approximating maxmin LPs. The approximation ratio of our algorithm is the best possible for any local algori ..."
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Cited by 5 (5 self)
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In a maxmin LP, the objective is to maximise ω subject to Ax ≤ 1, Cx ≥ ω1, and x ≥ 0 for nonnegative matrices A and C. We present a local algorithm (constanttime distributed algorithm) for approximating maxmin LPs. The approximation ratio of our algorithm is the best possible for any local
Approximating maxmin linear programs with local algorithms
 In Proc. 22nd IEEE International Parallel and Distributed Processing Symposium (IPDPS
, 2008
"... Abstract. A local algorithm is a distributed algorithm where each node must operate solely based on the information that was available at system startup within a constantsize neighbourhood of the node. We study the applicability of local algorithms to maxmin LPs where the objective is to maximise ..."
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Cited by 10 (10 self)
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Abstract. A local algorithm is a distributed algorithm where each node must operate solely based on the information that was available at system startup within a constantsize neighbourhood of the node. We study the applicability of local algorithms to maxmin LPs where the objective is to maximise
Approximation Algorithms for MAXMIN Tiling
"... The MAXMIN tiling problem is as follows. We are given a twodimensional array A[1; : : : ; n][1; : : : ; n] where each entry A[i][j] stores a nonnegative number. De ne a tile of A to be a subarray A[`; : : : ; r][t; : : : ; b] of A, the weight of a tile to be the sum of all array entries in it ..."
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Cited by 3 (2 self)
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The MAXMIN tiling problem is as follows. We are given a twodimensional array A[1; : : : ; n][1; : : : ; n] where each entry A[i][j] stores a nonnegative number. De ne a tile of A to be a subarray A[`; : : : ; r][t; : : : ; b] of A, the weight of a tile to be the sum of all array entries
Local approximability of maxmin and minmax linear programs
 Theory of Computing Systems
, 2011
"... Abstract. In a maxmin LP, the objective is to maximise ω subject to Ax ≤ 1, Cx ≥ ω1, and x ≥ 0. In a minmax LP, the objective is to minimise ρ subject to Ax ≤ ρ1, Cx ≥ 1, and x ≥ 0. The matrices A and C are nonnegative and sparse: each row ai of A has at most ∆I positive elements, and each row ck ..."
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Cited by 3 (3 self)
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of C has at most ∆K positive elements. We study the approximability of maxmin LPs and minmax LPs in a distributed setting; in particular, we focus on local algorithms (constanttime distributed algorithms). We show that for any ∆I ≥ 2, ∆K ≥ 2, and ε> 0 there exists a local algorithm
An Experimental Comparison of MinCut/MaxFlow Algorithms for Energy Minimization in Vision
 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 2001
"... After [10, 15, 12, 2, 4] minimum cut/maximum flow algorithms on graphs emerged as an increasingly useful tool for exact or approximate energy minimization in lowlevel vision. The combinatorial optimization literature provides many mincut/maxflow algorithms with different polynomial time compl ..."
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Cited by 1311 (54 self)
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After [10, 15, 12, 2, 4] minimum cut/maximum flow algorithms on graphs emerged as an increasingly useful tool for exact or approximate energy minimization in lowlevel vision. The combinatorial optimization literature provides many mincut/maxflow algorithms with different polynomial time
Feature selection based on mutual information: Criteria of maxdepe ndency, maxrelevance, and minredundancy
 IEEE Trans. Pattern Analysis and Machine Intelligence
"... Abstract—Feature selection is an important problem for pattern classification systems. We study how to select good features according to the maximal statistical dependency criterion based on mutual information. Because of the difficulty in directly implementing the maximal dependency condition, we f ..."
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Cited by 533 (7 self)
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to select a compact set of superior features at very low cost. We perform extensive experimental comparison of our algorithm and other methods using three different classifiers (naive Bayes, support vector machine, and linear discriminate analysis) and four different data sets (handwritten digits
Improved Approximation Algorithms for Maximum Cut and Satisfiability Problems Using Semidefinite Programming
 Journal of the ACM
, 1995
"... We present randomized approximation algorithms for the maximum cut (MAX CUT) and maximum 2satisfiability (MAX 2SAT) problems that always deliver solutions of expected value at least .87856 times the optimal value. These algorithms use a simple and elegant technique that randomly rounds the solution ..."
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Cited by 1231 (13 self)
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We present randomized approximation algorithms for the maximum cut (MAX CUT) and maximum 2satisfiability (MAX 2SAT) problems that always deliver solutions of expected value at least .87856 times the optimal value. These algorithms use a simple and elegant technique that randomly rounds
SNOPT: An SQP Algorithm For LargeScale Constrained Optimization
, 2002
"... Sequential quadratic programming (SQP) methods have proved highly effective for solving constrained optimization problems with smooth nonlinear functions in the objective and constraints. Here we consider problems with general inequality constraints (linear and nonlinear). We assume that first deriv ..."
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Cited by 582 (23 self)
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Sequential quadratic programming (SQP) methods have proved highly effective for solving constrained optimization problems with smooth nonlinear functions in the objective and constraints. Here we consider problems with general inequality constraints (linear and nonlinear). We assume that first
Tight local approximation results for maxmin linear programs
 In Proc. 4th International Workshop on Algorithmic Aspects of Wireless Sensor Networks (Algosensors
"... linear programs ..."
Multicommodity maxflow mincut theorems and their use in designing approximation algorithms
 J. ACM
, 1999
"... Abstract. In this paper, we establish maxflow mincut theorems for several important classes of multicommodity flow problems. In particular, we show that for any nnode multicommodity flow problem with uniform demands, the maxflow for the problem is within an O(log n) factor of the upper bound imp ..."
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Cited by 370 (6 self)
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implied by the mincut. The result (which is existentially optimal) establishes an important analogue of the famous 1commodity maxflow mincut theorem for problems with multiple commodities. The result also has substantial applications to the field of approximation algorithms. For example, we use
Results 1  10
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