2011 - ACM Fellow For contributions to the design and analysis of algorithms.
Howard Karloff mostly deals with Algorithm, Approximation algorithm, Combinatorics, Mathematical optimization and Discrete mathematics. The Algorithm study combines topics in areas such as Range and Competitive analysis. Howard Karloff combines subjects such as Maximum cut, Karloff–Zwick algorithm, Simple, Greedy randomized adaptive search procedure and Linear programming relaxation with his study of Approximation algorithm.
His studies in Combinatorics integrate themes in fields like Norm, Matrix, Sparse matrix and Sparse approximation. His work in the fields of Mathematical optimization, such as Traveling purchaser problem, 2-opt, Christofides algorithm and Local search, intersects with other areas such as Cross-entropy method. The concepts of his Discrete mathematics study are interwoven with issues in Linear programming, Constraint satisfaction, Semidefinite programming and Restricted isometry property.
His primary areas of study are Combinatorics, Approximation algorithm, Algorithm, Discrete mathematics and Upper and lower bounds. His Combinatorics research incorporates elements of Embedding, Polynomial, Semidefinite programming and Metric space. He has researched Approximation algorithm in several fields, including Maximum cut, Graph, Time complexity and Linear programming, Linear programming relaxation.
His research investigates the link between Algorithm and topics such as Mathematical optimization that cross with problems in Deterministic algorithm. His biological study spans a wide range of topics, including Graph theory, Code word, Code and Linear combination. In the subject of general Upper and lower bounds, his work in Competitive analysis is often linked to Omega, thereby combining diverse domains of study.
The scientist’s investigation covers issues in Approximation algorithm, Combinatorics, Linear regression, Time complexity and Optimization problem. Approximation algorithm is a subfield of Algorithm that Howard Karloff explores. Howard Karloff interconnects Tree, Construct and Speedup in the investigation of issues within Algorithm.
His Combinatorics research is multidisciplinary, relying on both Discrete mathematics, Structure, Geometric data analysis and Sequential dependency. Howard Karloff has included themes like Dynamic programming, Sequence, Computation and Database theory in his Discrete mathematics study. His Linear regression research integrates issues from P-matrix, Feature selection, Polynomial and Sparse vector.
His main research concerns Linear regression, Combinatorics, Discrete mathematics, P-matrix and Sparse vector. The study incorporates disciplines such as Dimension, Time complexity, Open problem, Regret and Bounded function in addition to Linear regression. He combines Combinatorics and Tree rearrangement in his research.
His work deals with themes such as Interval tree, 2–3 tree, -tree, Square and Complement, which intersect with Discrete mathematics. His studies deal with areas such as Polynomial and Feature selection as well as P-matrix.
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.
Algebraic methods for interactive proof systems
Carsten Lund;Lance Fortnow;Howard Karloff;Noam Nisan.
Journal of the ACM (1992)
A model of computation for MapReduce
Howard Karloff;Siddharth Suri;Sergei Vassilvitskii.
symposium on discrete algorithms (2010)
Combining geometry and combinatorics: A unified approach to sparse signal recovery
R. Berinde;A.C. Gilbert;P. Indyk;H. Karloff.
allerton conference on communication, control, and computing (2008)
New results on server problems
M. Chrobak;H. Karloff;T. Payne;S. Vishwanathan.
SIAM Journal on Discrete Mathematics (1991)
New algorithms for an ancient scheduling problem
Yair Bartal;Amos Fiat;Howard Karloff;Rakesh Vohra.
symposium on the theory of computing (1992)
An improved approximation algorithm for multiway cut
Gruia Călinescu;Howard Karloff;Yuval Rabani.
symposium on the theory of computing (1998)
A 7/8-approximation algorithm for MAX 3SAT?
H. Karloff;U. Zwick.
foundations of computer science (1997)
Approximation Algorithms for the 0-Extension Problem
Gruia Calinescu;Howard Karloff;Yuval Rabani.
SIAM Journal on Computing (2005)
On generating near-optimal tableaux for conditional functional dependencies
Lukasz Golab;Howard Karloff;Flip Korn;Divesh Srivastava.
very large data bases (2008)
Improved Approximation Algorithms for Prize-Collecting Steiner Tree and TSP
Aaron Archer;MohammadHossein Bateni;MohammadTaghi Hajiaghayi;Howard Karloff.
SIAM Journal on Computing (2011)
Profile was last updated on December 6th, 2021.
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