# A portfolio-based analysis method for competition results

arXiv: https://arxiv.org/abs/2205.15414 · HTML: https://mlsanity.com/paper/2205.15414/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 20 references that are in the map.
- First posted on arXiv 30 May 2022.

## Builds on (most cited first, 1 of 1)

- [Automated Algorithm Selection: Survey and Perspectives](https://mlsanity.com/paper/1811.11597/) (2018)

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

- [Automatic Feature Learning for Essence: a Case Study on Car Sequencing](https://mlsanity.com/paper/2409.15158/) (2024)

## Abstract (arXiv)

Competitions such as the MiniZinc Challenges or the SAT competitions have been very useful sources for comparing performance of different solving approaches and for advancing the state-of-the-arts of the fields. Traditional competition setting often focuses on producing a ranking between solvers based on their average performance across a wide range of benchmark problems and instances. While this is a sensible way to assess the relative performance of solvers, such ranking does not necessarily reflect the full potential of a solver, especially when we want to utilise a portfolio of solvers instead of a single one for solving a new problem. In this paper, I will describe a portfolio-based analysis method which can give complementary insights into the performance of participating solvers in a competition. The method is demonstrated on the results of the MiniZinc Challenges and new insights gained from the portfolio viewpoint are presented.