What is he best known for?
The fields of study he is best known for:
- 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.
His most cited work include:
- 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)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Combinatorics (75.17%)
- Discrete mathematics (43.62%)
- Uniqueness (22.82%)
What were the highlights of his more recent work (between 2018-2021)?
- Combinatorics (75.17%)
- Ising model (22.15%)
- Bipartite graph (19.46%)
In recent papers he was focusing on the following fields of study:
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.
Between 2018 and 2021, his most popular works were:
- 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)
In his most recent research, the most cited papers focused on:
- 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.
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