# Demixing Structured Superposition Signals from Periodic and Aperiodic Nonlinear Observations

arXiv: https://arxiv.org/abs/1708.02999 · HTML: https://mlsanity.com/paper/1708.02999/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Aug 2017.

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

- [Demixing Sparse Signals from Nonlinear Observations using Generalized Non-convex Regularization](https://mlsanity.com/paper/2607.10618/) (2026)

## Abstract (arXiv)

We consider the demixing problem of two (or more) structured high-dimensional vectors from a limited number of nonlinear observations where this nonlinearity is due to either a periodic or an aperiodic function. We study certain families of structured superposition models, and propose a method which provably recovers the components given (nearly) m = O(s) samples where s denotes the sparsity level of the underlying components. This strictly improves upon previous nonlinear demixing techniques and asymptotically matches the best possible sample complexity. We also provide a range of simulations to illustrate the performance of the proposed algorithms.