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486,847
An Exact Characterization of Greedy Structures
 SIAM Journal on Discrete Mathematics
, 1993
"... We present exact characterizations of structures on which the greedy algorithm produces optimal solutions. Our characterization, which we call matroid embeddings, complete the partial characterizations of Rado, Gale, and Edmonds (matroids), and of Korte and Lovasz (greedoids). We show that the gre ..."
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Cited by 12 (1 self)
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We present exact characterizations of structures on which the greedy algorithm produces optimal solutions. Our characterization, which we call matroid embeddings, complete the partial characterizations of Rado, Gale, and Edmonds (matroids), and of Korte and Lovasz (greedoids). We show
Constrained model predictive control: Stability and optimality
 AUTOMATICA
, 2000
"... Model predictive control is a form of control in which the current control action is obtained by solving, at each sampling instant, a finite horizon openloop optimal control problem, using the current state of the plant as the initial state; the optimization yields an optimal control sequence and t ..."
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Cited by 738 (16 self)
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from an extensive literature essential principles that ensure stability and use these to present a concise characterization of most of the model predictive controllers that have been proposed in the literature. In some cases the finite horizon optimal control problem solved online is exactly
Probabilistic checking of proofs: a new characterization of NP
 JOURNAL OF THE ACM
, 1998
"... We give a new characterization of NP: the class NP contains exactly those languages L for which membership proofs (a proof that an input x is in L) can be verified probabilistically in polynomial time using logarithmic number of random bits and by reading sublogarithmic number of bits from the proof ..."
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Cited by 414 (26 self)
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We give a new characterization of NP: the class NP contains exactly those languages L for which membership proofs (a proof that an input x is in L) can be verified probabilistically in polynomial time using logarithmic number of random bits and by reading sublogarithmic number of bits from
An Exact Characterization of Symmetric Functions in qAC°[2]
 In Proceedings of the 4th Annual International Computing and Combinatorics Conference (COCOON
"... qAC 0 [2] is the class of languages computable by circuits of constant depth and quasipolynomial (2 log O(1) n ) size with unbounded fanin AND, OR, and PARITY gates. Symmetric functions are those functions that are invariant under permutations of the input variables. Thus a symmetric function ..."
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Cited by 1 (0 self)
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function f n : f0; 1g n ! f0; 1g can also be seen as a function f n : f0; 1; \Delta \Delta \Delta ; ng ! f0; 1g. We give the following characterization of symmetric functions in qAC 0 [2], according to how fn (x) changes as x grows from 0 to n. A symmetric function f = (f n ) n2N is in qAC 0 [2
A New Parallel Vector Model, With Exact Characterizations
"... Abstract This paper develops a new and natural parallel vector model, and shows that for all k> = 1, the languages recognizable in O(logk n) time and polynomial work in the model are exactly those in NCk. Some improvements to other simulations in parallel models and reversal complexity are given. ..."
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Abstract This paper develops a new and natural parallel vector model, and shows that for all k> = 1, the languages recognizable in O(logk n) time and polynomial work in the model are exactly those in NCk. Some improvements to other simulations in parallel models and reversal complexity are given
Interference alignment and the degrees of freedom for the Kuser interference channel
 IEEE TRANSACTIONS ON INFORMATION THEORY
, 2008
"... For the fully connected K user wireless interference channel where the channel coefficients are timevarying and are drawn from a continuous distribution, the sum capacity is characterized as C(SNR) = K 2 log(SNR) +o(log(SNR)). Thus, the K user timevarying interference channel almost surely has K ..."
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Cited by 430 (18 self)
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For the fully connected K user wireless interference channel where the channel coefficients are timevarying and are drawn from a continuous distribution, the sum capacity is characterized as C(SNR) = K 2 log(SNR) +o(log(SNR)). Thus, the K user timevarying interference channel almost surely has
ICOLLIDE: An interactive and exact collision detection system for largescale environments
 In Proc. of ACM Interactive 3D Graphics Conference
, 1995
"... We present an exact and interactive collision detection system, ICOLLIDE, for largescale environments. Such environments are characterized by the number of objects undergoing rigid motion and the complexity of the models. The algorithm does not assume the objects ’ motions can be expressed as a c ..."
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Cited by 322 (24 self)
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We present an exact and interactive collision detection system, ICOLLIDE, for largescale environments. Such environments are characterized by the number of objects undergoing rigid motion and the complexity of the models. The algorithm does not assume the objects ’ motions can be expressed as a
A new parallel vector model, with exact characterizations of NC k
 in Proc. 11th Annual Symposium on Theoretical Aspects of Computer Science
, 1994
"... This paper develops a new and natural parallel vector model, and shows that for all k ≥ 1, the languages recognizable in O(log k n) time and polynomial work in the model are exactly those in NC k. Some improvements to other simulations in parallel models and reversal complexity are given. 1 ..."
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Cited by 5 (4 self)
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This paper develops a new and natural parallel vector model, and shows that for all k ≥ 1, the languages recognizable in O(log k n) time and polynomial work in the model are exactly those in NC k. Some improvements to other simulations in parallel models and reversal complexity are given. 1
Wireless Network Information Flow: A Deterministic Approach
, 2009
"... In contrast to wireline networks, not much is known about the flow of information over wireless networks. The main barrier is the complexity of the signal interaction in wireless channels in addition to the noise in the channel. A widely accepted model is the the additive Gaussian channel model, and ..."
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Cited by 296 (42 self)
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the Gaussian model but still captures two key wireless channel properties of broadcast and superposition. We consider a model for a wireless relay network with nodes connected by such deterministic channels, and present an exact characterization of the endtoend capacity when there is a single source and one
Results 1  10
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486,847