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Exponential Time Hypothesis
"... The quest for fast exact exponentialtime algorithms and fast parameterized algorithms for NPhard problems has been an exciting and fruitful area of research over the last decade. There is an accompanying theory of hardness based on the Exponential Time Hypothesis (ETH) and the Strong Exponential ..."
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The quest for fast exact exponentialtime algorithms and fast parameterized algorithms for NPhard problems has been an exciting and fruitful area of research over the last decade. There is an accompanying theory of hardness based on the Exponential Time Hypothesis (ETH) and the Strong Exponential
Lower bounds based on the Exponential Time Hypothesis
 Bulletin of the EATCS
, 2011
"... In this article we survey algorithmic lower bound results that have been obtained in the field of exact exponential time algorithms and parameterized complexity under certain assumptions on the running time of algorithms solving CNFSat, namely Exponential time hypothesis (ETH) and Strong Exponenti ..."
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Cited by 34 (4 self)
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In this article we survey algorithmic lower bound results that have been obtained in the field of exact exponential time algorithms and parameterized complexity under certain assumptions on the running time of algorithms solving CNFSat, namely Exponential time hypothesis (ETH) and Strong
The Exponential Time Hypothesis and the Parameterized Clique Problem
"... Abstract. In parameterized complexity there are three natural definitions of fixedparameter tractability called strongly uniform, weakly uniform and nonuniform fpt. Similarly, there are three notions of subexponential time, yielding three flavours of the exponential time hypothesis (ETH) stating ..."
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Abstract. In parameterized complexity there are three natural definitions of fixedparameter tractability called strongly uniform, weakly uniform and nonuniform fpt. Similarly, there are three notions of subexponential time, yielding three flavours of the exponential time hypothesis (ETH
Improving Strong Exponential Time Hypothesis lower bounds for treelike resolution
, 2014
"... A Strong Exponential Time Hypothesis lower bound for resolution has the form 2(1−k)n for some kCNF on n variables such that k → 0 as k → ∞. For every large k we prove that there exists an unsatisfiable kCNF formula on n variables which requires treelike resolution refutations of size at least 2 ..."
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A Strong Exponential Time Hypothesis lower bound for resolution has the form 2(1−k)n for some kCNF on n variables such that k → 0 as k → ∞. For every large k we prove that there exists an unsatisfiable kCNF formula on n variables which requires treelike resolution refutations of size at least 2
Relating the Time Complexity of Optimization Problems in Light of the ExponentialTime Hypothesis⋆
"... ar ..."
Panel Cointegration; Asymptotic and Finite Sample Properties of Pooled Time Series Tests, With an Application to the PPP Hypothesis; New Results. Working paper
, 1997
"... We examine properties of residualbased tests for the null of no cointegration for dynamic panels in which both the shortrun dynamics and the longrun slope coefficients are permitted to be heterogeneous across individual members of the panel+ The tests also allow for individual heterogeneous fixed ..."
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Cited by 498 (13 self)
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We examine properties of residualbased tests for the null of no cointegration for dynamic panels in which both the shortrun dynamics and the longrun slope coefficients are permitted to be heterogeneous across individual members of the panel+ The tests also allow for individual heterogeneous fixed effects and trend terms, and we consider both pooled within dimension tests and group mean between dimension tests+ We derive limiting distributions for these and show that they are normal and free of nuisance parameters+ We also provide Monte Carlo evidence to demonstrate their small sample size and power performance, and we illustrate their use in testing purchasing power parity for the post–Bretton Woods period+ 1.
A direct approach to false discovery rates
, 2002
"... Summary. Multiplehypothesis testing involves guarding against much more complicated errors than singlehypothesis testing. Whereas we typically control the type I error rate for a singlehypothesis test, a compound error rate is controlled for multiplehypothesis tests. For example, controlling the ..."
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Cited by 753 (14 self)
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Summary. Multiplehypothesis testing involves guarding against much more complicated errors than singlehypothesis testing. Whereas we typically control the type I error rate for a singlehypothesis test, a compound error rate is controlled for multiplehypothesis tests. For example, controlling
Depthfirst IterativeDeepening: An Optimal Admissible Tree Search
 Artificial Intelligence
, 1985
"... The complexities of various search algorithms are considered in terms of time, space, and cost of solution path. It is known that breadthfirst search requires too much space and depthfirst search can use too much time and doesn't always find a cheapest path. A depthfirst iteratiwdeepening a ..."
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Cited by 517 (23 self)
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The complexities of various search algorithms are considered in terms of time, space, and cost of solution path. It is known that breadthfirst search requires too much space and depthfirst search can use too much time and doesn't always find a cheapest path. A depthfirst iteratiw
WideArea Traffic: The Failure of Poisson Modeling
 IEEE/ACM TRANSACTIONS ON NETWORKING
, 1995
"... Network arrivals are often modeled as Poisson processes for analytic simplicity, even though a number of traffic studies have shown that packet interarrivals are not exponentially distributed. We evaluate 24 widearea traces, investigating a number of widearea TCP arrival processes (session and con ..."
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Cited by 1757 (24 self)
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Network arrivals are often modeled as Poisson processes for analytic simplicity, even though a number of traffic studies have shown that packet interarrivals are not exponentially distributed. We evaluate 24 widearea traces, investigating a number of widearea TCP arrival processes (session
Learning quickly when irrelevant attributes abound: A new linearthreshold algorithm
 Machine Learning
, 1988
"... learning Boolean functions, linearthreshold algorithms Abstract. Valiant (1984) and others have studied the problem of learning various classes of Boolean functions from examples. Here we discuss incremental learning of these functions. We consider a setting in which the learner responds to each ex ..."
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Cited by 766 (5 self)
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example according to a current hypothesis. Then the learner updates the hypothesis, if necessary, based on the correct classification of the example. One natural measure of the quality of learning in this setting is the number of mistakes the learner makes. For suitable classes of functions, learning
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