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- Hamid Reza Feyzmahdavian, Arda Aytekin, Mikael Johansson
- IEEE Trans. Automat. Contr.
- 2015

Mini-batch optimization has proven to be a powerful paradigm for large-scale learning. However, the state of the art parallel mini-batch algorithms assume synchronous operation or cyclic update orders. When worker nodes are heterogeneous (due to different computational capabilities or different communication delays), synchronous and cyclic operations are… (More)

- Hamid Reza Feyzmahdavian, Arda Aytekin, Mikael Johansson
- 2014 IEEE International Workshop on Machine…
- 2014

This paper presents a new incremental gradient algorithm for minimizing the average of a large number of smooth component functions based on delayed partial gradients. Even with a constant step size, which can be chosen independently of the maximum delay bound and the number of objective function components, the expected objective value is guaranteed to… (More)

- Arda Aytekin, Hamid Reza Feyzmahdavian, Mikael Johansson
- ArXiv
- 2016

This paper presents an asynchronous incremental aggregated gradient algorithm and its implementation in a parameter server framework for solving regularized optimization problems. The algorithm can handle both general convex (possibly non-smooth) regularizers and general convex constraints. When the empirical data loss is strongly convex, we establish… (More)

- Arda Aytekin, Hamid Reza Feyzmahdavian, Mikael Johansson
- 2014 52nd Annual Allerton Conference on…
- 2014

This paper studies a flexible algorithm for minimizing a sum of component functions, each of which depends on a large number of decision variables. Such formulations appear naturally in “big data” applications, where each function describes the loss estimated using the data available at a specific machine, and the number of features under… (More)

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