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Random assignment and shortest path problems
, 2006
"... We explore a similarity between the n by n random assignment problem and the random shortest path problem on the complete graph on n + 1 vertices. This similarity is a consequence of the proof of the Parisi formula for the assignment problem given by C. Nair, B. Prabhakar and M. Sharma in 2003. We g ..."
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Cited by 10 (2 self)
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We explore a similarity between the n by n random assignment problem and the random shortest path problem on the complete graph on n + 1 vertices. This similarity is a consequence of the proof of the Parisi formula for the assignment problem given by C. Nair, B. Prabhakar and M. Sharma in 2003. We
On the Advantage Over a Random Assignment
 Proceedings of the 34th Annual ACM Symposium on Theory of Computing, 2002
, 2002
"... ABSTRACT We initiate the study of a new measure of approximation. This measure compares the performance of an approximation algorithm to the random assignment algorithm. Since the random assignment algorithm is known to give essentially the best possible polynomial time approximation algorithm for m ..."
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Cited by 2 (0 self)
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ABSTRACT We initiate the study of a new measure of approximation. This measure compares the performance of an approximation algorithm to the random assignment algorithm. Since the random assignment algorithm is known to give essentially the best possible polynomial time approximation algorithm
Randomized Algorithms
, 1995
"... Randomized algorithms, once viewed as a tool in computational number theory, have by now found widespread application. Growth has been fueled by the two major benefits of randomization: simplicity and speed. For many applications a randomized algorithm is the fastest algorithm available, or the simp ..."
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Cited by 2210 (37 self)
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Randomized algorithms, once viewed as a tool in computational number theory, have by now found widespread application. Growth has been fueled by the two major benefits of randomization: simplicity and speed. For many applications a randomized algorithm is the fastest algorithm available
How Random Must Random Assignment Be in Random Assignment Experiments?
, 2003
"... analysis for the SelfSufficiency Project (SSP) sponsored by Human Resources Development Canada (HRDC). The SelfSufficiency Project is sponsored by HRDC. This paper was produced for SRDC. The opinions expressed herein are the authors ’ and do not necessarily reflect those of SRDC or HRDC. The Socia ..."
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analysis for the SelfSufficiency Project (SSP) sponsored by Human Resources Development Canada (HRDC). The SelfSufficiency Project is sponsored by HRDC. This paper was produced for SRDC. The opinions expressed herein are the authors ’ and do not necessarily reflect those of SRDC or HRDC. The Social Research and Demonstration Corporation is a nonprofit organization and registered charity with offices in Ottawa, Vancouver, and Sydney, Nova Scotia. SRDC was created specifically to develop, field test, and rigorously evaluate social programs. SRDC’s twopart mission is to help policymakers and practitioners identify social policies and programs that improve the wellbeing of all Canadians, with a special concern for the effects on the disadvantaged, and to raise the standards of evidence that are used in assessing social policies. As an intermediary organization, SRDC attempts to bridge the worlds of academic researchers, government policymakers, and ontheground program operators. Providing a vehicle for the development and management of complex demonstration projects, SRDC seeks to work in close partnership with provinces, the federal
Constructive Bounds and Exact Expectations for the Random Assignment Problem
, 1998
"... The random assignment problem is to choose a minimumcost perfect matching in a complete n x n bipartite graph, whose edge weights are chosen randomly from some distribution such as the exponential distribution with mean 1. In this case it is known that the expectation does not grow unboundedly with ..."
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Cited by 54 (6 self)
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The random assignment problem is to choose a minimumcost perfect matching in a complete n x n bipartite graph, whose edge weights are chosen randomly from some distribution such as the exponential distribution with mean 1. In this case it is known that the expectation does not grow unboundedly
Teaching Random Assignment: Do You Believe It Works?
"... Textbook authors admonish students to check on the comparability of two randomly assigned groups by conducting statistical tests on pretest means to determine if randomization worked. A Monte Carlo study was conducted on a sample of n = 2 per group, where each participant’s personality profile was r ..."
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Cited by 2 (0 self)
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Textbook authors admonish students to check on the comparability of two randomly assigned groups by conducting statistical tests on pretest means to determine if randomization worked. A Monte Carlo study was conducted on a sample of n = 2 per group, where each participant’s personality profile
A generalization of the random assignment problem
"... Abstract. We give a conjecture for the expected value of the optimal kassignment in an m × nmatrix, where the entries are all exp(1)distributed random variables or zeros. We prove this conjecture in the case there is a zerocost k − 1assignment. Assuming our conjecture, we determine some limits, ..."
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Cited by 11 (8 self)
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Abstract. We give a conjecture for the expected value of the optimal kassignment in an m × nmatrix, where the entries are all exp(1)distributed random variables or zeros. We prove this conjecture in the case there is a zerocost k − 1assignment. Assuming our conjecture, we determine some limits
Inducing Features of Random Fields
 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 1997
"... We present a technique for constructing random fields from a set of training samples. The learning paradigm builds increasingly complex fields by allowing potential functions, or features, that are supported by increasingly large subgraphs. Each feature has a weight that is trained by minimizing the ..."
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Cited by 664 (14 self)
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We present a technique for constructing random fields from a set of training samples. The learning paradigm builds increasingly complex fields by allowing potential functions, or features, that are supported by increasingly large subgraphs. Each feature has a weight that is trained by minimizing
The ζ(2) Limit in the Random Assignment Problem
, 2000
"... The random assignment (or bipartite matching) problem asks about An = min P n i=1 c(i; (i)), where (c(i; j)) is a n \Theta n matrix with i.i.d. entries, say with exponential(1) distribution, and the minimum is over permutations . M'ezard and Parisi (1987) used the replica method from sta ..."
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Cited by 56 (1 self)
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The random assignment (or bipartite matching) problem asks about An = min P n i=1 c(i; (i)), where (c(i; j)) is a n \Theta n matrix with i.i.d. entries, say with exponential(1) distribution, and the minimum is over permutations . M'ezard and Parisi (1987) used the replica method from
Results 11  20
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1,071,697