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Block Splitting for Large-Scale Distributed Learning
Block Splitting Large-Scale Distributed Learning
2015/7/9
Machine learning and statistics with very large datasets is now a topic of widespread interest, both in academia and industry. Many such tasks can be posed as convex optimization problems, so algorith...
We apply an operator splitting technique to a generic linear-convex optimal control problem, which results in an algorithm that alternates between solving a quadratic control problem, for which there ...
A Primal-Dual Operator Splitting Method for Conic Optimization
Primal-Dual Operator Splitting Method Conic Optimization
2015/7/9
We develop a simple operator splitting method for solving a primal conic optimization problem; we show that the iterates also solve the dual problem. The resulting algorithm is very simple to describe...
Conic Optimization via Operator Splitting and Homogeneous Self-Dual Embedding
Optimization via Operator Splitting Homogeneous Self-Dual Embedding
2015/7/9
We introduce a first order method for solving very large cone programs to modest accuracy. The method uses an operator splitting method, the alternating directions method of multipliers, to solve the ...
Block Splitting for Distributed Optimization
Distributed optimization · Alternating direction method of multipliers Operator splitting Proximal operators Cone programming Machine learning
2015/7/9
This paper describes a general purpose method for solving convex optimization problems in a distributed computing environment. In particular, if the problem data includes a large linear operator or ma...
Metric Selection in Fast Dual Forward Backward Splitting
Metric Selection Fast Dual Forward Backward Splitting
2015/7/9
The performance of fast forward-backward splitting, or equivalently fast proximal gradient methods, is susceptible to conditioning of the optimization problem data. This conditioning is related to a m...
Diagonal Scaling in Douglas-Rachford Splitting and ADMM
Diagonal Scaling Douglas-Rachford Splitting ADMM
2015/7/9
Recently, several convergence rate results for Douglas-Rachford splitting and the alternating direction method of multipliers (ADMM) have been presented in the literature. In this paper, we show linea...
How to Generate Uniform Samples on Discrete Sets Using the Splitting Method
Generate Uniform Samples Discrete Sets Splitting Method
2015/7/6
In spite of the common consensus on the classic Markov chain Monte Carlo (MCMC)as a universal tool for generating samples on complex sets, it fails to generate points uniformly distributed on discrete...
Splitting and Merging Components of a Nonconjugate Dirichlet Process Mixture Model
Bayesian model Markov chain Monte Carlo split-merge moves nonconjugate prior
2009/9/22
The inferential problem of associating data to mixture components is dif-
ficult when components are nearby or overlapping. We introduce a new split-merge
Markov chain Monte Carlo technique that eff...
Asymptotic behavior of some random splitting schemes
Splitting schemes asymptotic behavior Markov chains stationary distribution contraction principle
2009/9/21
We consider three new schemes of random splitting of
a unit interval. These schemes am related to settings considered earlier
in literature. Essentially we are concerned with asymptotic behavior of
...