coordinate descent
An Asynchronous Parallel Stochastic Coordinate Descent Algorithm
... Formulations of the type (1,2) arise in many data analysis and machine learning prob- lems, for example, support vector machines (linear or nonlinear dual formulation) (Cortes and Vapnik, 1995), LASSO (after decomposing ...
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A distributed block coordinate descent method for training l1 regularized linear classifiers
... Generic Coordinate Descent Method (Scherrer et ...for coordinate descent methods ( GenCD ) suitable for parallel computing ...Several coordinate de- scent algorithms such as stochastic ...
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Low-complexity RLS algorithms using dichotomous coordinate descent iterations
... Abstract—In this paper, we derive low-complexity recursive least squares (RLS) adaptive filtering algorithms. We express the RLS problem in terms of auxiliary normal equations with respect to increments of the filter ...
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Asynchronous Delay-Aware Accelerated Proximal Coordinate Descent for Nonconvex Nonsmooth Problems
... Asynchronous Coordinate Descent: The asynchronous computation is much more efficient than the synchronous ...stochastic coordinate de- scent (Liu et ...asynchronous coordinate descent ...
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On Faster Convergence of Cyclic Block Coordinate Descent-type Methods for Strongly Convex Minimization
... block coordinate descent-type (CBCD-type) methods, which perform iterative updates for a few coordinates (a block) simultaneously throughout the procedure, have shown remarkable computational performance ...
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Dual Coordinate Descent Algorithms for Efficient Large Margin Structured Prediction
... dual coordinate descent and cutting plane methods, the hybrid approach, DCD-SSVM outper- forms other SSVM training methods both in terms of objective value reduction and testing error rate ...
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Coordinate Descent Method for Large-scale L2-loss Linear Support Vector Machines
... Linear support vector machines (SVM) are useful for classifying large-scale sparse data. Prob- lems with sparse features are common in applications such as document classification and natural language processing. In this ...
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Coordinate descent iterations in fast affine projection algorithm
... We have proposed a new approach for real-time implementa- tion of the fast affine projection algorithm. This is based on the application of the dichotomous coordinate descent iterations for solving systems ...
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Iterative Scaling and Coordinate Descent Methods for Maximum Entropy Models
... Iterative scaling ( IS ) methods are popular in training Maxent models. They all share the same property of solving a one-variable sub-problem at a time. Existing IS methods include general- ized iterative scaling ( GIS ...
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Antenna Array Pattern Synthesis via Coordinate Descent Method
... In this article, an array pattern synthesis algorithm for arbitrary arrays based on coordinate descent method (CDM) is presented. CDM has been first mentioned in [26] for solving smooth unconstrained ...
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Distributed Coordinate Descent Method for Learning with Big Data
... The Shotgun algorithm (parallel coordinate descent) of Bradley et al. (2011) is similar to Hydra for c = 1. Some of the differences: Bradley et al. (2011) only consider R equal to the L1 norm and their ...
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QUIC: Quadratic Approximation for Sparse Inverse Covariance Estimation
... efficient coordinate descent method that exploits the structure of Hessian matrix, so that we reduce the time complexity of each coordinate descent update step from O(p 2 ) to ...sufficient ...
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The Relationships between Energy Balance Deviations and Adiposity in Children and Adolescents
... cyclical coordinate descent methods as an alternative to produce the entire solution path without verifying the existence of piecewise ...The coordinate descent algorithm is proven to be ...
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Time Evolving Undirected Graphical Model for Protein-Protein Interaction Networks
... Block Coordinate Descent algorithm using C++, I obtained an implementation that is very competitive and often outperforms other state-of-the-art approaches for this ...
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PubMedCentral-PMC5120762.pdf
... The Support Vector Machine (SVM) is a very popular classification tool with many successful applications. It was originally designed for binary problems with desirable theoretical properties. Although there exist various ...
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Stochastic Primal-Dual Coordinate Method for Regularized Empirical Risk Minimization
... on coordinate descent methods; see, ...randomized coordinate descent sparked a lot of recent activities on this ...dual coordinate ascent methods ...
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Improving the Lexical Function Composition Model with Pathwise Optimized Elastic Net Regression
... In this paper, we focus on the simpler, linear lexical function model proposed by Baroni and Zamparelli (2010) (see also Coecke et al. (2010)) and show that its performance can be further im- proved through more advanced ...
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Linear Convergence of Randomized Feasible Descent Methods Under the Weak Strong Convexity Assumption
... Ye (2016). The optimal solution to this problem is x = 0. For the first experiment we set n = 100 and c = 0.03 (L W f = 3.97 ≈ 1), and in the second experiment we keep n unchanged and set c = 0.99 (L W f = 99.01 ≈ n). ...
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A Comparison of Optimization Methods and Software for Large-scale L1-regularized Linear Classification
... where f ∗ is obtained by running TRON with a strict stopping condition. Both x-axis and y-axis are log-scaled. We draw a dotted reference line in Figure 1 to indicate the relative error 0.1. From Figure 1, BBR and CDN ...
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Group lassoing change-points in piecewise-constant AR processes
... Regularizing the least-squares criterion with the total number of coefficient changes, it is possible to estimate time- varying (TV) autoregressive (AR) models with piecewise-constant coefficients. Such models emerge in ...
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