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Distributed adaptive Newton methods with global superlinear convergence
Jiaqi Zhang, Keyou You,
Tamer Başar
Electrical and Computer Engineering
Coordinated Science Lab
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Dive into the research topics of 'Distributed adaptive Newton methods with global superlinear convergence'. Together they form a unique fingerprint.
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Keyphrases
Low-rank Matrix Approximation
100%
Superlinear Rate
100%
Modified Newton Method
100%
Superlinear Convergence
100%
Numerical Experiments
50%
Compress
50%
Hessian Matrix
50%
Logistic Regression
50%
Peer-to-peer Networks
50%
Regression Problem
50%
Finite Sum
50%
Distributed Algorithms
50%
First-order Methods
50%
Approximation Techniques
50%
Distributed Optimization
50%
Neighbor Node
50%
Newton Algorithm
50%
Consensus Methods
50%
Decision Vector
50%
Adaptive Step Size
50%
Quadratic Convergence Rate
50%
Set Consensus
50%
Computer Science
Newton's Method
100%
Approximated Matrix
100%
Optimization Problem
50%
Objective Function
50%
Convergence Rate
50%
Regression Problem
50%
Distributed Algorithm
50%
Distributed Optimization
50%
Algorithm Converges
50%
Peer to Peer Networks
50%
Approximation Technique
50%
Neighboring Node
50%
Logistic Regression
50%
Newton Algorithm
50%
Hessian Matrix
50%
Decision Vector
50%
Mathematics
Newton's Method
100%
Approximated Matrix
100%
Numerical Experiment
50%
Minimizes
50%
Convergence Rate
50%
Objective Function
50%
Logistic Regression
50%
Finite Sum
50%
Approximation Technique
50%
Finite Time
50%
Sharp Contrast
50%
Hessian Matrix
50%
Consensus Method
50%