Dr Sarat Moka

PaRKol (Partial Rejection sampling for K-colouring) draws exact uniform samples of proper k-colourings of a graph. It implements the soft colouring framework, which decomposes the global sampling problem into small independent subproblems via partial rejection sampling, then solves each with coupling from the past. The algorithm is inherently parallelisable and achieves near-linear runtime.

This package, jointly developed with Hua Yang Hu, provides an implementation of COMBSS (continuous optimisation method toward best subset selection) for linear regression, proposed in the paper Moka et al. (2024).

PyREGG estimates rare-event probabilities in Gilbert random geometric graphs using Naïve Monte Carlo, Conditional Monte Carlo, and Importance Sampling. It supports seven rare events including edge count, maximum degree, connected component size, triangle count, clique size, planarity, and forest. More rare events and higher-dimensional windows are coming soon.

This repository provides a Python implementation of our method Grid-FW proposed in Moka et al. (2025) for minimum-variance portfolio selection.

This repository provides Python code for estimating the rare events corresponding to the edge count and maximum degree of random geometric graphs on a square window using the following three methods:

• Naive Monte Carlo;
• Conditional Monte Carlo;
• Importance Sampling based Monte Carlo;