Multi-Feasibility Variable Selection

TL;DR

This paper reports on the solutions developed by the Panda team for the !Optimizer 2021 competition, focusing on multi-feasibility variable selection, highlighting their approach, preprocessing techniques, and implementation details in Julia.

Contribution

The paper presents the winning strategies and algorithms for multi-feasibility variable selection in an optimization competition, including preprocessing methods and implementation insights.

Findings

Julia outperformed Python in optimization tasks during R&D.

Panda team's solutions achieved high accuracy and speed in the competition.

Preprocessing techniques improved algorithm performance.

Abstract

This paper is the report of the problem proposed for the !Optimizer 2021 competition, and the solutions of the gold medalist team, i.e., the Panda team. The competition was held in two stages, the research and development stage and a two-week contest stage, consisting of five rounds, and seven teams succeeded in finishing both stages to the end. In this joint report of the winner team Panda and the problem design committee coordinated by Mojtaba Tefagh, we first explain each of the five rounds and then provide the solutions proposed by our team (Panda) to fulfill the required tasks in the fastest and most accurate way. Afterward, some preprocessing and data manipulating ideas used to enhance the algorithms would be presented. All codes are written in the Julia language, which showed a better performance than Python on optimization problems in our comparisons during the R&D stage, and are publicly available in the Github repository: https://github.com/Optimizer-Competition-Panda