# A Taxonomy of Constraints in Simulation-Based Optimization

arXiv: https://arxiv.org/abs/1505.07881 · HTML: https://mlsanity.com/paper/1505.07881/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 May 2015.

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

- [TREGO: a Trust-Region Framework for Efficient Global Optimization](https://mlsanity.com/paper/2101.06808/) (2021)
- [Generalized Early Stopping in Evolutionary Direct Policy Search](https://mlsanity.com/paper/2308.03574/) (2023)
- [Generative Models for Anomaly Detection and Design-Space Dimensionality Reduction in Shape Optimization](https://mlsanity.com/paper/2308.04051/) (2023)
- [Parallel Multi-Objective Hyperparameter Optimization with Uniform Normalization and Bounded Objectives](https://mlsanity.com/paper/2309.14936/) (2023)

## Abstract (arXiv)

The types of constraints encountered in black-box and simulation-based optimization problems differ significantly from those treated in nonlinear programming. We introduce a characterization of constraints to address this situation. We provide formal definitions for several constraint classes and present illustrative examples in the context of the resulting taxonomy. This taxonomy, denoted QRAK, is useful for modeling and problem formulation, as well as optimization software development and deployment. It can also be used as the basis for a dialog with practitioners in moving problems to increasingly solvable branches of optimization.