What is he best known for?
The fields of study he is best known for:
- Combinatorics
- Algorithm
- Geometry
The scientist’s investigation covers issues in Combinatorics, Polyhedron, Regular polygon, Discrete mathematics and Convex hull.
His Combinatorics research is multidisciplinary, incorporating elements of Cardinality, Bounded function and Star-shaped polygon.
The various areas that David Avis examines in his Polyhedron study include Vertex, Anatomic Surface and Partial volume.
David Avis works mostly in the field of Regular polygon, limiting it down to topics relating to Algorithm and, in certain cases, Convex metric space, Fisher information metric, Metric, Injective metric space and Metric k-center, as a part of the same area of interest.
His research in Discrete mathematics intersects with topics in Assignment problem, Plane, Heuristics and Euclidean geometry.
His Convex hull study often links to related topics such as Convex polytope.
His most cited work include:
- Automated 3-D Extraction of Inner and Outer Surfaces of Cerebral Cortex from MRI (734 citations)
- Reverse search for enumeration (630 citations)
- A pivoting algorithm for convex hulls and vertex enumeration of arrangements and polyhedra (482 citations)
What are the main themes of his work throughout his whole career to date?
David Avis spends much of his time researching Combinatorics, Discrete mathematics, Polytope, Polyhedron and Convex hull.
His Combinatorics study integrates concerns from other disciplines, such as Linear programming, Space and Convex polytope.
His studies deal with areas such as Orthogonal convex hull and Vertex enumeration problem as well as Convex polytope.
His Discrete mathematics research focuses on Birkhoff polytope and how it connects with Uniform k 21 polytope.
His Polytope study also includes fields such as
- Exponential function, which have a strong connection to Upper and lower bounds,
- Simplex algorithm which is related to area like Digraph.
His Polyhedron study incorporates themes from Speedup, Shared memory, Parallel computing and Regular polygon.
He most often published in these fields:
- Combinatorics (65.96%)
- Discrete mathematics (38.30%)
- Polytope (28.19%)
What were the highlights of his more recent work (between 2011-2021)?
- Polytope (28.19%)
- Combinatorics (65.96%)
- Discrete mathematics (38.30%)
In recent papers he was focusing on the following fields of study:
David Avis mainly focuses on Polytope, Combinatorics, Discrete mathematics, Simple and Polyhedron.
His Polytope research is multidisciplinary, incorporating perspectives in Linear programming, Time complexity, Theory of computation and Facet.
His specific area of interest is Combinatorics, where David Avis studies Vertex.
His Discrete mathematics study combines topics from a wide range of disciplines, such as Generalization, Uniform k 21 polytope, Game theory, Birkhoff polytope and Polynomial.
His Simple research incorporates themes from Algorithm, Search algorithm and Limit.
His studies in Polyhedron integrate themes in fields like Travelling salesman problem and Speedup, Shared memory, Parallel computing.
Between 2011 and 2021, his most popular works were:
- Ground metric learning (62 citations)
- On the extension complexity of combinatorial polytopes (27 citations)
- Optimized open pit mine design, pushbacks and the gap problem—a review (26 citations)
In his most recent research, the most cited papers focused on:
- Algorithm
- Combinatorics
- Geometry
His main research concerns Polytope, Parallel computing, Polyhedron, Combinatorics and Mathematical optimization.
The concepts of his Polytope study are interwoven with issues in Theory of computation and Descriptive complexity theory.
His work carried out in the field of Parallel computing brings together such families of science as Minor, Theoretical computer science, Branch and bound and Vertex.
His Combinatorics research includes themes of Discrete mathematics, Upper and lower bounds, Exponential function and Simplex algorithm.
His biological study spans a wide range of topics, including Computational complexity theory and Generalization.
The study incorporates disciplines such as Tree, Algorithm and Parameterized complexity in addition to Mathematical optimization.
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