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Faster Randomized Interior Point Methods for Tall/Wide Linear Programs
Agniva Chowdhury
, Gregory Dexter
, Palma London
,
Haim Avron
, Petros Drineas
School of Mathematical Sciences
Oak Ridge National Laboratory
Purdue University
Cornell University
Research output
:
Contribution to journal
›
Article
›
peer-review
7
Scopus citations
Overview
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Dive into the research topics of 'Faster Randomized Interior Point Methods for Tall/Wide Linear Programs'. Together they form a unique fingerprint.
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Keyphrases
Linear Programming
100%
Interior Point Method
100%
Popular
33%
Operations Research
33%
Engineering Economics
33%
Synthetic Data
33%
Machine Learning Applications
33%
Combinatorics
33%
System of Linear Equations
33%
Iteration Complexity
33%
Iterative Solvers
33%
Preconditioning Technique
33%
Conjugate Gradient Iteration
33%
Basis Pursuit
33%
Non-negative Matrix Factorization
33%
Randomized numerical Linear Algebra
33%
Infeasible Interior-point Method
33%
Approximate Solver
33%
Feasible Interior-point Method
33%
Chebyshev Iteration
33%
Mathematics
Linear Program
100%
Interior Point
100%
Wide Range
25%
Approximates
25%
Matrix (Mathematics)
25%
Synthetic Data
25%
Factorization
25%
Real-World Data
25%
Combinatorics
25%
Linear Algebra
25%
Chebyshev
25%
Systems of Linear Equation
25%
Operations Research
25%
Linear Programming
25%
Computer Science
Linear Program
100%
Interior-Point Method
100%
System Analysis
25%
Synthetic Data
25%
Support Vector Machine
25%
Linear Equation
25%
Conjugate Gradients
25%
nonnegative matrix factorization
25%
Machine Learning
25%
Learning System
25%
Operations Research
25%
Linear Programming
25%
Chemical Engineering
Learning System
100%
System Analysis
100%