Author
Alan Frieze
Other affiliations: Max Planck Society, University of North London, Queen Mary University of London ...read more
Bio: Alan Frieze is an academic researcher from Carnegie Mellon University. The author has contributed to research in topics: Random graph & Random regular graph. The author has an hindex of 71, co-authored 599 publications receiving 27236 citations. Previous affiliations of Alan Frieze include Max Planck Society & University of North London.
Papers published on a yearly basis
Papers
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TL;DR: Some of the major results in random graphs and some of the more challenging open problems are reviewed, including those related to the WWW.
Abstract: We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.
7,116 citations
01 Jun 2000
TL;DR: This research was motivated by the fact that such a family of permutations is essential to the algorithm used in practice by the AltaVista web index software to detect and filter near-duplicate documents.
Abstract: We define and study the notion of min-wise independent families of permutations. We say that F?Sn (the symmetric group) is min-wise independent if for any set X?n and any x?X, when ? is chosen at random in F we havePr(min{?(X)}=?(x))=1|X| . In other words we require that all the elements of any fixed set X have an equal chance to become the minimum element of the image of X under ?. Our research was motivated by the fact that such a family (under some relaxations) is essential to the algorithm used in practice by the AltaVista web index software to detect and filter near-duplicate documents. However, in the course of our investigation we have discovered interesting and challenging theoretical questions related to this concept?we present the solutions to some of them and we list the rest as open problems.
962 citations
TL;DR: The proof of correctness of the algorithm relies on recent theory of rapidly mixing Markov chains and isoperimetric inequalities to show that a certain random walk can be used to sample nearly uniformly from within K within Euclidean space.
Abstract: A randomized polynomial-time algorithm for approximating the volume of a convex body K in n-dimensional Euclidean space is presented. The proof of correctness of the algorithm relies on recent theory of rapidly mixing Markov chains and isoperimetric inequalities to show that a certain random walk can be used to sample nearly uniformly from within K.
702 citations
TL;DR: An algorithm is developed that is qualitatively faster, provided the authors may sample the entries of the matrix in accordance with a natural probability distribution, and implies that in constant time, it can be determined if a given matrix of arbitrary size has a good low-rank approximation.
Abstract: We consider the problem of approximating a given m × n matrix A by another matrix of specified rank k, which is smaller than m and n. The Singular Value Decomposition (SVD) can be used to find the "best" such approximation. However, it takes time polynomial in m, n which is prohibitive for some modern applications. In this article, we develop an algorithm that is qualitatively faster, provided we may sample the entries of the matrix in accordance with a natural probability distribution. In many applications, such sampling can be done efficiently. Our main result is a randomized algorithm to find the description of a matrix D* of rank at most k so that holds with probability at least 1 − δ (where v·vF is the Frobenius norm). The algorithm takes time polynomial in k,1/e, log(1/δ) only and is independent of m and n. In particular, this implies that in constant time, it can be determined if a given matrix of arbitrary size has a good low-rank approximation.
613 citations
TL;DR: The matrix approximation is generalized to multi-dimensional arrays and from that derive approximation algorithms for all dense Max-SNP problems and the Regularity Lemma is derived.
Abstract: ×n matrix A with entries between say −1 and 1, and an error parameter e between 0 and 1, we find a matrix D (implicitly) which is the sum of \(\) simple rank 1 matrices so that the sum of entries of any submatrix (among the \(\)) of (A−D) is at most emn in absolute value. Our algorithm takes time dependent only on e and the allowed probability of failure (not on m, n).
566 citations
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TL;DR: There is, I think, something ethereal about i —the square root of minus one, which seems an odd beast at that time—an intruder hovering on the edge of reality.
Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.
Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …
33,785 citations
Journal Article•
28,685 citations
Book•
01 Jan 1996TL;DR: A valuable reference for the novice as well as for the expert who needs a wider scope of coverage within the area of cryptography, this book provides easy and rapid access of information and includes more than 200 algorithms and protocols.
Abstract: From the Publisher:
A valuable reference for the novice as well as for the expert who needs a wider scope of coverage within the area of cryptography, this book provides easy and rapid access of information and includes more than 200 algorithms and protocols; more than 200 tables and figures; more than 1,000 numbered definitions, facts, examples, notes, and remarks; and over 1,250 significant references, including brief comments on each paper.
13,597 citations
TL;DR: The major concepts and results recently achieved in the study of the structure and dynamics of complex networks are reviewed, and the relevant applications of these ideas in many different disciplines are summarized, ranging from nonlinear science to biology, from statistical mechanics to medicine and engineering.
9,441 citations