# A primal-dual fixed-point algorithm for minimization of the sum of three convex separable functions

arXiv: https://arxiv.org/abs/1512.09235 · HTML: https://mlsanity.com/paper/1512.09235/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Dec 2015.

## Built on by (most cited first, 4 of 4)

- [Decentralized Composite Optimization with Compression](https://mlsanity.com/paper/2108.04448/) (2021)
- [DISA: A Dual Inexact Splitting Algorithm for Distributed Convex Composite Optimization](https://mlsanity.com/paper/2209.01850/) (2022)
- [Stochastic Gradient Methods with Compressed Communication for Decentralized Saddle Point Problems](https://mlsanity.com/paper/2205.14452/) (2022)
- [BALPA: A Balanced Primal-Dual Algorithm for Nonsmooth Optimization with Application to Distributed Optimization](https://mlsanity.com/paper/2212.02835/) (2022)

## Abstract (arXiv)

Many problems arising in image processing and signal recovery with multi-regularization can be formulated as minimization of a sum of three convex separable functions. Typically, the objective function involves a smooth function with Lipschitz continuous gradient, a linear composite nonsmooth function and a nonsmooth function. In this paper, we propose a primal-dual fixed-point (PDFP) scheme to solve the above class of problems. The proposed algorithm for three block problems is a fully splitting symmetric scheme, only involving explicit gradient and linear operators without inner iteration, when the nonsmooth functions can be easily solved via their proximity operators, such as ℓ1 type regularization. We study the convergence of the proposed algorithm and illustrate its efficiency through examples on fused LASSO and image restoration with non-negative constraint and sparse regularization.