# Signal Decomposition Using Masked Proximal Operators

arXiv: https://arxiv.org/abs/2202.09338 · HTML: https://mlsanity.com/paper/2202.09338/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 95 references that are in the map.
- First posted on arXiv 18 Feb 2022.

## Builds on (most cited first, 7 of 7)

- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [CVXPY: A Python-Embedded Modeling Language for Convex Optimization](https://mlsanity.com/paper/1603.00943/) (2016)
- [Coordinate Descent Algorithms](https://mlsanity.com/paper/1502.04759/) (2015)
- [A Rewriting System for Convex Optimization Problems](https://mlsanity.com/paper/1709.04494/) (2017)
- [Generalized Low Rank Models](https://mlsanity.com/paper/1410.0342/) (2014)
- [Contextually Supervised Source Separation with Application to Energy Disaggregation](https://mlsanity.com/paper/1312.5023/) (2013)
- [Three Operator Splitting with a Nonconvex Loss Function](https://mlsanity.com/paper/2103.04568/) (2021)

## Built on by (most cited first, 3 of 3)

- [PV Fleet Modeling via Smooth Periodic Gaussian Copula](https://mlsanity.com/paper/2307.00004/) (2023)
- [Additive decomposition of one-dimensional signals using Transformers](https://mlsanity.com/paper/2506.05942/) (2025)
- [AdaSTI: Conditional Diffusion Models with Adaptive Dependency Modeling for Spatio-Temporal Imputation](https://mlsanity.com/paper/2509.18144/) (2025)

## Abstract (arXiv)

We consider the well-studied problem of decomposing a vector time series signal into components with different characteristics, such as smooth, periodic, nonnegative, or sparse. We describe a simple and general framework in which the components are defined by loss functions (which include constraints), and the signal decomposition is carried out by minimizing the sum of losses of the components (subject to the constraints). When each loss function is the negative log-likelihood of a density for the signal component, this framework coincides with maximum a posteriori probability (MAP) estimation; but it also includes many other interesting cases. Summarizing and clarifying prior results, we give two distributed optimization methods for computing the decomposition, which find the optimal decomposition when the component class loss functions are convex, and are good heuristics when they are not. Both methods require only the masked proximal operator of each of the component loss functions, a generalization of the well-known proximal operator that handles missing entries in its argument. Both methods are distributed, i.e., handle each component separately. We derive tractable methods for evaluating the masked proximal operators of some loss functions that, to our knowledge, have not appeared in the literature.