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- Eric Vigoda

Discipline name
D-index
D-index (Discipline H-index) only includes papers and citation values for an examined
discipline in contrast to General H-index which accounts for publications across all
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Citations
Publications
World Ranking
National Ranking

Mathematics
D-index
31
Citations
4,155
145
World Ranking
2592
National Ranking
1083

2019 - Fellow of the American Mathematical Society For contributions to theoretical computer science, in particular through its interactions with probability, combinatorics and statistical physics and for service to the profession.

- Mathematical analysis
- Combinatorics
- Discrete mathematics

Eric Vigoda focuses on Combinatorics, Markov chain Monte Carlo, Discrete mathematics, Glauber and Independent set. His biological study spans a wide range of topics, including Uniqueness and Ising model. His research investigates the connection between Markov chain Monte Carlo and topics such as Algorithm that intersect with problems in Posterior probability and Tree.

His Discrete mathematics research is multidisciplinary, relying on both Degree, Minimax approximation algorithm and Computing the permanent. His Minimax approximation algorithm study combines topics in areas such as Scheme, Polynomial time approximation algorithm and Polynomial-time approximation scheme. His Independent set research is multidisciplinary, incorporating perspectives in Distribution, Hardness of approximation and Vertex.

- A polynomial-time approximation algorithm for the permanent of a matrix with nonnegative entries (671 citations)
- Improved bounds for sampling colorings (184 citations)
- Phylogenetic MCMC Algorithms Are Misleading on Mixtures of Trees (150 citations)

His primary areas of study are Combinatorics, Discrete mathematics, Uniqueness, Ising model and Glauber. The study of Combinatorics is intertwined with the study of Mixing in a number of ways. His Discrete mathematics research incorporates themes from Upper and lower bounds, Graph and Markov chain mixing time.

His study explores the link between Uniqueness and topics such as Independent set that cross with problems in Hardness of approximation. His work deals with themes such as Fixed point and Spectral gap, which intersect with Ising model. The Adaptive simulated annealing study combines topics in areas such as Discrete system and Markov chain Monte Carlo.

- Combinatorics (75.17%)
- Discrete mathematics (43.62%)
- Uniqueness (22.82%)

- Combinatorics (75.17%)
- Ising model (22.15%)
- Bipartite graph (19.46%)

Eric Vigoda mostly deals with Combinatorics, Ising model, Bipartite graph, Uniqueness and Potts model. Combinatorics is closely attributed to Mixing in his work. Eric Vigoda has researched Ising model in several fields, including Polynomial and Random graph.

His research integrates issues of Time complexity and Partition function in his study of Bipartite graph. His Time complexity study is concerned with Discrete mathematics in general. His study in Uniqueness is interdisciplinary in nature, drawing from both Tree, Independent set, Spectral gap and Tree.

- Rapid Mixing of Glauber Dynamics up to Uniqueness via Contraction (13 citations)
- Rapid Mixing for Colorings via Spectral Independence (11 citations)
- Swendsen-Wang dynamics for general graphs in the tree uniqueness region (10 citations)

- Mathematical analysis
- Combinatorics
- Graph theory

His scientific interests lie mostly in Combinatorics, Uniqueness, Glauber, Ising model and Lambda. His study on Combinatorics is mostly dedicated to connecting different topics, such as Mixing. His research investigates the connection with Uniqueness and areas like Tree which intersect with concerns in Spectral gap.

His work carried out in the field of Ising model brings together such families of science as Polynomial and Random graph. His Polynomial research is multidisciplinary, incorporating perspectives in Discrete mathematics, Distribution, Exponential time hypothesis and Boundary value problem. His studies in Random graph integrate themes in fields like Time complexity and Bipartite graph.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

A polynomial-time approximation algorithm for the permanent of a matrix with nonnegative entries

Mark Jerrum;Alistair Sinclair;Eric Vigoda.

Journal of the ACM **(2004)**

883 Citations

Improved bounds for sampling colorings

Eric Vigoda.

Journal of Mathematical Physics **(2000)**

257 Citations

Phylogenetic MCMC Algorithms Are Misleading on Mixtures of Trees

Elchanan Mossel;Elchanan Mossel;Eric Vigoda;Eric Vigoda.

Science **(2005)**

186 Citations

A polynomial-time approximation algorithm for the permanent of a matrix with non-negative entries

Mark Jerrum;Alistair Sinclair;Eric Vigoda.

symposium on the theory of computing **(2001)**

159 Citations

Torpid mixing of some Monte Carlo Markov chain algorithms in statistical physics

C. Borgs;J.T. Chayes;A. Frieze;Jeong Han Kim.

foundations of computer science **(1999)**

132 Citations

Fast convergence of the Glauber dynamics for sampling independent sets

Michael Luby;Eric Vigoda;Eric Vigoda.

Random Structures and Algorithms **(1999)**

117 Citations

Accelerating Simulated Annealing for the Permanent and Combinatorial Counting Problems

Ivona Bezáková;Daniel Štefankovič;Vijay V. Vazirani;Eric Vigoda.

SIAM Journal on Computing **(2008)**

111 Citations

Mixing in time and space for lattice spin systems: A combinatorial view

Martin Dyer;Alistair Sinclair;Eric Vigoda;Dror Weitz.

Random Structures and Algorithms **(2004)**

103 Citations

Heterogeneous genomic molecular clocks in primates.

Seong-Ho Kim;Navin Elango;Charles David Warden;Eric Vigoda.

PLOS Genetics **(2005)**

101 Citations

A non-Markovian coupling for randomly sampling colorings

T.P. Hayes;E. Vigoda.

foundations of computer science **(2003)**

98 Citations

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