# Nonconvex Proximal Incremental Aggregated Gradient Method with Linear Convergence

arXiv: https://arxiv.org/abs/1804.02571 · HTML: https://mlsanity.com/paper/1804.02571/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Apr 2018.

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

- [Incremental Aggregation on the Grassmannian for Asynchronous Eigenspace Computation](https://mlsanity.com/paper/2608.04406/) (2026)

## Abstract (arXiv)

In this paper, we study the proximal incremental aggregated gradient(PIAG) algorithm for minimizing the sum of L-smooth nonconvex component functions and a proper closed convex function. By exploiting the L-smooth property and with the help of an error bound condition, we can show that the PIAG method still enjoys some nice linear convergence properties even for nonconvex minimization. To illustrate this, we first demonstrate that the generated sequence globally converges to the stationary point set. Then, there exists a threshold such that the objective function value sequence and the iterate point sequence are R-linearly convergent when the stepsize is chosen below this threshold.