# Parallel and Distributed Successive Convex Approximation Methods for Big-Data Optimization

arXiv: https://arxiv.org/abs/1805.06963 · HTML: https://mlsanity.com/paper/1805.06963/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 May 2018.

## Built on by (most cited first, 7 of 7)

- [Causal Abstraction Learning based on the Semantic Embedding Principle](https://mlsanity.com/paper/2502.00407/) (2025)
- [Biased Federated Learning under Wireless Heterogeneity](https://mlsanity.com/paper/2503.06078/) (2025)
- [Learning Network Sheaves for AI-native Semantic Communication](https://mlsanity.com/paper/2512.03248/) (2025)
- [Sparsity-Aware Distributed Learning for Gaussian Processes with Linear Multiple Kernel](https://mlsanity.com/paper/2309.08201/) (2023)
- [Learning Multi-Frequency Partial Correlation Graphs](https://mlsanity.com/paper/2311.15756/) (2023)
- [T-SKM-Net: Trainable Neural Network Framework for Linear Constraint Satisfaction via Sampling Kaczmarz-Motzkin Method](https://mlsanity.com/paper/2512.10461/) (2025)
- [Robust Filtering and Learning in State-Space Models: Skewness and Heavy Tails Via Asymmetric Laplace Distribution](https://mlsanity.com/paper/2507.22343/) (2025)

## Abstract (arXiv)

Recent years have witnessed a surge of interest in parallel and distributed optimization methods for large-scale systems. In particular, nonconvex large-scale optimization problems have found a wide range of applications in several engineering fields. The design and the analysis of such complex, large-scale, systems pose several challenges and call for the development of new optimization models and algorithms. The major contribution of this paper is to put forth a general, unified, algorithmic framework, based on Successive Convex Approximation (SCA) techniques, for the parallel and distributed solution of a general class of non-convex constrained (non-separable, networked) problems. The presented framework unifies and generalizes several existing SCA methods, making them appealing for a parallel/distributed implementation while offering a flexible selection of function approximants, step size schedules, and control of the computation/communication efficiency. This paper is organized according to the lectures that one of the authors delivered at the CIME Summer School on Centralized and Distributed Multi-agent Optimization Models and Algorithms, held in Cetraro, Italy, June 23--27, 2014. These lectures are: I) Successive Convex Approximation Methods: Basics; II) Parallel Successive Convex Approximation Methods; and III) Distributed Successive Convex Approximation Methods.