GSOC 2026: Improve efficiency of `OptiSim` selection method

Description

OptiSim is a distance-based method implemented in Selector. The method itself scales linearly with the number of points to select. However, the algorithm is slowed down by the setup phase, where an optimal radius is searched to obtain the required number of selected data points. In the current implementation, the associated cost makes the use of large databases impractical for this method. The primary goal of this project is to fix this limitation.

📚 Package Description and Impact

The Selector library provides methods for selecting a diverse subset of a dataset. The goal is to build representative subsets from large datasets without introducing bias, and in some cases even reducing it (e.g., when the original dataset is unbalanced). This is critical for data-driven modeling across various fields and helps mitigate issues such as class imbalance in machine learning training sets. More information about this package can be found at https://www.biorxiv.org/content/10.1101/2025.11.21.689756v1 and https://selector.qcdevs.org.

👷 What will you do?

The main focus will be to refactor the OptiSim class and likely the radius optimization utility function. The objective is to decrease the computational prefactor by several orders of magnitude, while at the same time maintaining readability and a clear mapping between the implementation and the underlying mathematics.

🏁 Expected Outcomes

1. Decrease the computational cost of OptiSim by several orders of magnitude.
2. Improve the efficiency of the radius optimization used by OptiSim.
3. Write tests to ensure correctness and numerical stability after performance optimizations.
4. Provide profiling-based examples demonstrating the performance improvements.

🙋 Mentors

🏋️ Stretch Goal

Improve the performance of the directed sphere exclusion (DISE) algorithm also.

Start by locating the OptiSim class in Selector and the radius optimization utility it uses. Profile the setup phase on large datasets, then read the existing OptiSim and DISE tests if present. Done means preserving selection correctness and numerical stability while adding tests and profiling-based examples that demonstrate the expected speedup.