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- Maxim Sviridenko

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
disciplines.
Citations
Publications
World Ranking
National Ranking

Mathematics
D-index
40
Citations
7,459
140
World Ranking
1367
National Ranking
619

Computer Science
D-index
40
Citations
7,484
143
World Ranking
5770
National Ranking
2799

- 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.

- 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)

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.

- Combinatorics (47.06%)
- Approximation algorithm (45.99%)
- Mathematical optimization (33.69%)

- Approximation algorithm (45.99%)
- Combinatorics (47.06%)
- Fantasy (5.88%)

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.

- 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)

- 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.

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 note on maximizing a submodular set function subject to a knapsack constraint

Maxim Sviridenko.

Operations Research Letters **(2004)**

666 Citations

Pipage Rounding: A New Method of Constructing Algorithms with Proven Performance Guarantee

Alexander A. Ageev;Maxim Sviridenko.

Journal of Combinatorial Optimization **(2004)**

400 Citations

Buffer Overflow Management in QoS Switches

Alexander Kesselman;Zvi Lotker;Yishay Mansour;Boaz Patt-Shamir.

SIAM Journal on Computing **(2004)**

354 Citations

Tight approximation algorithms for maximum general assignment problems

Lisa Fleischer;Michel X. Goemans;Vahab S. Mirrokni;Maxim Sviridenko.

symposium on discrete algorithms **(2006)**

320 Citations

The Santa Claus problem

Nikhil Bansal;Maxim Sviridenko.

symposium on the theory of computing **(2006)**

286 Citations

Non-monotone submodular maximization under matroid and knapsack constraints

Jon Lee;Vahab S. Mirrokni;Viswanath Nagarajan;Maxim Sviridenko.

symposium on the theory of computing **(2009)**

275 Citations

Approximation algorithms for asymmetric TSP by decomposing directed regular multigraphs

Haim Kaplan;Moshe Lewenstein;Nira Shafrir;Maxim Sviridenko.

Journal of the ACM **(2005)**

266 Citations

Dynamic placement for clustered web applications

A. Karve;T. Kimbrel;G. Pacifici;M. Spreitzer.

the web conference **(2006)**

249 Citations

The diameter of a long-range percolation graph

Don Coppersmith;David Gamarnik;Maxim Sviridenko.

Random Structures and Algorithms **(2002)**

248 Citations

Approximation schemes for minimizing average weighted completion time with release dates

F. Afrati;E. Bampis;C. Chekuri;D. Karger.

foundations of computer science **(1999)**

241 Citations

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