# A Sequential Approximation Framework for Coded Distributed Optimization

arXiv: https://arxiv.org/abs/1710.09001 · HTML: https://mlsanity.com/paper/1710.09001/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Oct 2017.

## Built on by (most cited first, 1 of 1)

- [Optimization-based Block Coordinate Gradient Coding for Mitigating Partial Stragglers in Distributed Learning](https://mlsanity.com/paper/2206.02450/) (2022)

## Abstract (arXiv)

Building on the previous work of Lee et al. and Ferdinand et al. on coded computation, we propose a sequential approximation framework for solving optimization problems in a distributed manner. In a distributed computation system, latency caused by individual processors ("stragglers") usually causes a significant delay in the overall process. The proposed method is powered by a sequential computation scheme, which is designed specifically for systems with stragglers. This scheme has the desirable property that the user is guaranteed to receive useful (approximate) computation results whenever a processor finishes its subtask, even in the presence of uncertain latency. In this paper, we give a coding theorem for sequentially computing matrix-vector multiplications, and the optimality of this coding scheme is also established. As an application of the results, we demonstrate solving optimization problems using a sequential approximation approach, which accelerates the algorithm in a distributed system with stragglers.