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Random coordinate descent
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#	Item
1	Random coordinate descent algorithms for huge-scale optimization problems Ion Necoara Automatic Control and Systems Engineering Depart. University Politehnica Bucharest Add to Reading List Source URL: automatica.dei.unipd.it Language: English - Date: 2013-07-16 06:27:23
2	A Lower Bound for the Optimization of Finite Sums Alekh Agarwal Microsoft Research NYC, New York, NY. ALEKHA @ MICROSOFT. COM Add to Reading List Source URL: jmlr.org Language: English - Date: 2015-09-16 19:38:43 Mathematical optimization Operations research Convex analysis Convex optimization Gradient descent Big O notation Regularized least squares Random coordinate descent Online machine learning
3	One coordinate at a time • Adaboost performs gradient descent on exponential loss • Adds one coordinate (“weak learner”) at each iteration. • Weak learning in binary classification = slightly better than random Add to Reading List Source URL: seed.ucsd.edu Language: English - Date: 2011-02-10 16:52:04
4	Perceptron Learning with Random Coordinate Descent Ling Li Learning Systems Group, California Institute of Technology Abstract. A perceptron is a linear threshold classifier that separates examples with a hyperplane. It Add to Reading List Source URL: www.work.caltech.edu Language: English - Date: 2006-04-22 18:28:41 Perceptron Linear classifier Boosting Support vector machine AdaBoost Artificial neural network Backpropagation Statistical classification Ensemble learning Machine learning Statistics Neural networks
5	mS2GD: Mini-Batch Semi-Stochastic Gradient Descent in the Proximal Setting Jie Liu Lehigh University Bethlehem, PA 18015 Add to Reading List Source URL: www.opt-ml.org Language: English - Date: 2014-12-10 11:28:41 Stochastic optimization Numerical linear algebra Stochastic gradient descent Gradient method Gradient descent Stochastic approximation Mathematical optimization Random coordinate descent Coordinate descent Numerical analysis Mathematical analysis Mathematics
6	Coordinate descent converges faster with the Gauss-Southwell rule than random selection Michael Friedlander Department of Mathematics University of California, Davis Add to Reading List Source URL: www.opt-ml.org Language: English - Date: 2014-12-10 11:28:45 Convex analysis Differential calculus Mathematical optimization Regression analysis Lipschitz continuity Gradient Coordinate descent Convex function Derivative Mathematical analysis Mathematics Numerical analysis
7	Generic Methods for Optimization-Based Modeling Justin Domke Rochester Institute of Technology Abstract Add to Reading List Source URL: users.cecs.anu.edu.au Language: English - Date: 2012-05-24 15:13:47 Image processing Spatial data analysis Mathematical optimization Coordinate descent Limited-memory BFGS BFGS method Gradient descent Conditional random field Conjugate gradient method Numerical analysis Machine learning Image denoising
8	Randomized Subspace Descent Rafael M. Frongillo Harvard University [removed] Add to Reading List Source URL: www.opt-ml.org Language: English - Date: 2014-12-10 11:28:44 Abstract algebra Matrix theory Random coordinate descent Hypergraph Vector space Eigenvalues and eigenvectors Convex function Algebra Mathematics Linear algebra
9	Perceptron Learning with Random Coordinate Descent Ling Li Learning Systems Group, California Institute of Technology Abstract. A perceptron is a linear threshold classifier that separates examples with a hyperplane. It Add to Reading List Source URL: authors.library.caltech.edu Language: English - Date: 2012-12-26 09:14:16 Perceptron Linear classifier Support vector machine Boosting AdaBoost Artificial neural network Ensemble learning Backpropagation Statistical classification Machine learning Statistics Neural networks

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