# Avoiding Traps in Nonconvex Problems

arXiv: https://arxiv.org/abs/2106.05206 · HTML: https://mlsanity.com/paper/2106.05206/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 14 references that are in the map.
- First posted on arXiv 9 Jun 2021.

## Builds on (most cited first, 6 of 6)

- [The Douglas-Rachford Algorithm for Convex and Nonconvex Feasibility Problems](https://mlsanity.com/paper/1904.09148/) (2019)
- [Reconstructing cellular automata rules from observations at nonconsecutive times](https://mlsanity.com/paper/2012.02179/) (2020)
- [Learning Without Loss](https://mlsanity.com/paper/1911.00493/) (2019)
- [Global Behavior of the Douglas-Rachford Method for a Nonconvex Feasibility Problem](https://mlsanity.com/paper/1506.09026/) (2015)
- [Matrix product constraints by projection methods](https://mlsanity.com/paper/1601.01003/) (2016)
- [The complexity of bit retrieval](https://mlsanity.com/paper/1601.03428/) (2016)

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

- [Learning grammar with a divide-and-concur neural network](https://mlsanity.com/paper/2201.07341/) (2022)

## Abstract (arXiv)

Iterative projection methods may become trapped at non-solutions when the constraint sets are nonconvex. Two kinds of parameters are available to help avoid this behavior and this study gives examples of both. The first kind of parameter, called a hyperparameter, includes any kind of parameter that appears in the definition of the iteration rule itself. The second kind comprises metric parameters in the definition of the constraint sets, a feature that arises when the problem to be solved has two or more kinds of variables. Through examples we show the importance of properly tuning both kinds of parameters and offer heuristic interpretations of the observed behavior.