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Numerical Optimization
Numerical Optimization presents a comprehensive and up-to-date description of the most effective methods in continuous optimization. Expand
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Hogwild: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent
We show that when the associated optimization problem is sparse, meaning most gradient updates only modify a small part of the decision variable, then HOGWILD! achieves a nearly optimal rate of convergence. Expand
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Numerical Optimization (Springer Series in Operations Research and Financial Engineering)
Optimization is an important tool used in decision science and for the analysis of physical systems . Expand
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Primal-Dual Interior-Point Methods
  • Stephen J. Wright
  • Mathematics, Computer Science
  • Other Titles in Applied Mathematics
  • 1997
Preface Notation 1. Introduction. Linear Programming Primal-Dual Methods The Central Path A Primal-Dual Framework Path-Following Methods Potential-Reduction Methods Infeasible Starting PointsExpand
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Sparse Reconstruction by Separable Approximation
We present an algorithmic framework for the more general problem of minimizing the sum of a smooth convex function and a nonsmooth, possibly nonconvex regularizer. Expand
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Computational Methods for Sparse Solution of Linear Inverse Problems
The goal of the sparse approximation problem is to approximate a target signal using a linear combination of a few elementary signals drawn from a fixed collection. Expand
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Power Awareness in Network Design and Routing
We investigate the potential for significant power savings in operational networks by including power-awareness in the design and configuration of networks, and in the implementation of network protocols. Expand
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Coordinate descent algorithms
This paper describes the fundamentals of the coordinate descent approach, together with variants and extensions and their convergence properties, mostly with reference to convex objectives. Expand
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Application of Interior-Point Methods to Model Predictive Control
We present a structured interior-point method for the efficient solution of the optimal control problem in model predictive control. The cost of this approach is linear in the horizon length,Expand
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Asynchronous Stochastic Coordinate Descent: Parallelism and Convergence Properties
We describe an asynchronous parallel stochastic proximal coordinate descent algorithm for minimizing a composite objective function, which consists of a smooth convex function added to a separable convexfunction. Expand
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