JuliaCon 2026

This talk introduces a new package called CoolPDLP.jl, which implements state-of-the-art parallel algorithms for large-scale linear programming. Thanks to Julia's flexible GPU ecosystem, these algorithms run on various kinds of accelerators, accepting arbitrary matrix and number types.

Slides available at https://gdalle.github.io/JuliaCon2026

Pfaffian point processes (PfPPs) most famously arise in the eigenvalue distributions of random orthogonal or symplectic matrices, but also appear in the description of other stochastic processes, such as annihilating and coalescing random walks, random involutions, or symmetric corner growth. We introduce novel sampling algorithms for discrete and continuous PfPPs, as well as a method for constructing skew-symmetric kernels based on skew-orthogonal polynomials derived from arbitrary weights.