What is he best known for?
The fields of study he is best known for:
- Combinatorics
- Algorithm
- Mathematical optimization
His scientific interests lie mostly in Approximation algorithm, Combinatorics, Discrete mathematics, Mathematical optimization and Linear programming relaxation.
He interconnects Linear programming, Assignment problem, Maximum cut and Greedy algorithm in the investigation of issues within Approximation algorithm.
His Combinatorics research integrates issues from Bin packing problem and Knapsack problem.
His work on Graph, Undirected graph and Unit cube as part of general Discrete mathematics study is frequently linked to High probability, bridging the gap between disciplines.
His research in Mathematical optimization intersects with topics in Competitive analysis, Network packet, Quality of service, Buffer overflow and Algorithm.
His Submodular set function research includes elements of Function, Matroid and Subject.
His most cited work include:
- A note on maximizing a submodular set function subject to a knapsack constraint (469 citations)
- Pipage Rounding: A New Method of Constructing Algorithms with Proven Performance Guarantee (292 citations)
- Tight approximation algorithms for maximum general assignment problems (227 citations)
What are the main themes of his work throughout his whole career to date?
Maxim Sviridenko focuses on Combinatorics, Approximation algorithm, Mathematical optimization, Discrete mathematics and Time complexity.
His research investigates the connection between Combinatorics and topics such as Bin packing problem that intersect with problems in Polynomial-time approximation scheme.
Maxim Sviridenko combines subjects such as Linear programming, Linear programming relaxation, Special case and Combinatorial optimization with his study of Approximation algorithm.
His Mathematical optimization research is multidisciplinary, incorporating elements of Scheduling, Dynamic priority scheduling, Job shop scheduling, Algorithm and Upper and lower bounds.
His study in Discrete mathematics is interdisciplinary in nature, drawing from both Assignment problem, Generalized assignment problem and Quadratic assignment problem.
His Time complexity research incorporates elements of Mathematical economics and Randomized algorithm.
He most often published in these fields:
- Combinatorics (47.06%)
- Approximation algorithm (45.99%)
- Mathematical optimization (33.69%)
What were the highlights of his more recent work (between 2015-2021)?
- Approximation algorithm (45.99%)
- Combinatorics (47.06%)
- Fantasy (5.88%)
In recent papers he was focusing on the following fields of study:
Maxim Sviridenko mostly deals with Approximation algorithm, Combinatorics, Fantasy, Mathematical optimization and Discrete mathematics.
The study incorporates disciplines such as Dimension and Supermodular function in addition to Approximation algorithm.
His studies deal with areas such as Matching pursuit, Restricted isometry property and Compressed sensing as well as Combinatorics.
His Mathematical optimization study combines topics in areas such as Scheduling and Dynamic priority scheduling.
His Discrete mathematics study combines topics from a wide range of disciplines, such as Stochastic optimization, Element, Optimization problem, Universe and Linear programming relaxation.
He has researched Linear programming relaxation in several fields, including Assignment problem, Generalized assignment problem and Quadratic assignment problem.
Between 2015 and 2021, his most popular works were:
- Optimal Approximation for Submodular and Supermodular Optimization with Bounded Curvature (78 citations)
- An Algorithm for Online K-Means Clustering (51 citations)
- Submodular stochastic probing on matroids (28 citations)
In his most recent research, the most cited papers focused on:
- Algorithm
- Combinatorics
- Mathematical analysis
Maxim Sviridenko mainly investigates Approximation algorithm, Mathematical optimization, k-means clustering, Combinatorics and Submodular set function.
His study deals with a combination of Approximation algorithm and Alpha.
His study explores the link between Mathematical optimization and topics such as Dynamic priority scheduling that cross with problems in Fair-share scheduling.
His Facility location problem research extends to Combinatorics, which is thematically connected.
His Submodular set function study incorporates themes from Function, Bounded function, Matroid and Greedy algorithm.
His Matroid study necessitates a more in-depth grasp of Discrete mathematics.
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