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Iteration complexity of randomized block-coordinate descent methods for minimizing a composite function
TLDR
A randomized block-coordinate descent method for minimizing the sum of a smooth and a simple nonsmooth block-separable convex function is developed and it is proved that it obtains an accurate solution with probability at least 1-\rho in at most O(n/\varepsilon) iterations, thus achieving first true iteration complexity bounds.
SARAH: A Novel Method for Machine Learning Problems Using Stochastic Recursive Gradient
TLDR
A StochAstic Recursive grAdient algoritHm (SARAH), as well as its practical variant SARAH+, as a novel approach to the finite-sum minimization problems is proposed, and a linear convergence rate is proven under strong convexity assumption.
Reinforcement Learning for Solving the Vehicle Routing Problem
TLDR
This work presents an end-to-end framework for solving the Vehicle Routing Problem (VRP) using reinforcement learning, and demonstrates how this approach can handle problems with split delivery and explore the effect of such deliveries on the solution quality.
Communication-Efficient Distributed Dual Coordinate Ascent
TLDR
A communication-efficient framework that uses local computation in a primal-dual setting to dramatically reduce the amount of necessary communication is proposed, and a strong convergence rate analysis is provided for this class of algorithms.
Parallel coordinate descent methods for big data optimization
In this work we show that randomized (block) coordinate descent methods can be accelerated by parallelization when applied to the problem of minimizing the sum of a partially separable smooth convex
Adding vs. Averaging in Distributed Primal-Dual Optimization
TLDR
A novel generalization of the recent communication-efficient primal-dual framework (COCOA) for distributed optimization, which allows for additive combination of local updates to the global parameters at each iteration, whereas previous schemes with convergence guarantees only allow conservative averaging.
Distributed Coordinate Descent Method for Learning with Big Data
TLDR
This paper develops and analyzes Hydra: HYbriD cooRdinAte descent method for solving loss minimization problems with big data, and gives bounds on the number of iterations sufficient to approximately solve the problem with high probability.
Mini-Batch Primal and Dual Methods for SVMs
TLDR
It is shown that the same quantity, the spectral norm of the data, controls the parallelization speedup obtained for both primal stochastic subgradient descent (SGD) and Stochastic dual coordinate ascent (SCDA) methods and is used to derive novel variants of mini-batched SDCA.
Distributed Learning with Compressed Gradient Differences
TLDR
This work proposes a new distributed learning method --- DIANA --- which resolves issues via compression of gradient differences, and performs a theoretical analysis in the strongly convex and nonconvex settings and shows that its rates are superior to existing rates.
CoCoA: A General Framework for Communication-Efficient Distributed Optimization
TLDR
This work presents a general-purpose framework for distributed computing environments, CoCoA, that has an efficient communication scheme and is applicable to a wide variety of problems in machine learning and signal processing, and extends the framework to cover general non-strongly-convex regularizers, including L1-regularized problems like lasso.
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