Titlebook: Large-Scale and Distributed Optimization; Pontus Giselsson,Anders Rantzer Book 2018 Springer Nature Switzerland AG 2018 Large-Scale Optimi

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Primal-Dual Proximal Algorithms for Structured Convex Optimization: A Unifying Framework, and two nonsmooth proximable functions, one of which is composed with a linear mapping. The framework is based on the recently proposed asymmetric forward-backward-adjoint three-term splitting (AFBA); depending on the value of two parameters, (extensions of) known algorithms as well as many new pri

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Mirror Descent and Convex Optimization Problems with Non-smooth Inequality Constraints,hods to solve such problems in different situations: smooth or non-smooth objective function; convex or strongly convex objective and constraint; deterministic or randomized information about the objective and constraint. Described methods are based on Mirror Descent algorithm and switching subgradi

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Frank-Wolfe Style Algorithms for Large Scale Optimization,rithm using stochastic gradients, approximate subproblem solutions, and sketched decision variables in order to scale to enormous problems while preserving (up to constants) the optimal convergence rate ..

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Communication-Efficient Distributed Optimization of Self-concordant Empirical Loss,ization in machine learning. We assume that each machine in the distributed computing system has access to a local empirical loss function, constructed with i.i.d. data sampled from a common distribution. We propose a communication-efficient distributed algorithm to minimize the overall empirical lo

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Convergence of an Inexact Majorization-Minimization Method for Solving a Class of Composite Optimizy constructed . of the objective function. We describe a variety of classes of functions for which such a construction is possible. We introduce an inexact variant of the method, in which only approximate minimization of the consistent majorizer is performed at each iteration. Both the exact and the