collaborators

6 papers

math.AP2026

The nonlocal attraction-repulsion transport equation with power kernels

Massimo Fornasier, Hui Huang, Lukang Sun

We study a nonlocal continuity equation on in which a probability density is driven by the competition between attraction toward a prescribed background measure

math.PR2026

Sharp Rates of MMD Empirical Estimation with Power Kernels

Francesco Colasanto, Matteo Focardi, Massimo Fornasier +1

We establish quantitative rates of convergence for the empirical estimation of probability measures by means of the Maximum Mean Discrepancy (MMD) with power kernel $K_q(x,y) = -|x…

math.AP2026

Large-Time Analysis of the Langevin Dynamics for Energies Fulfilling Polyak-Łojasiewicz Conditions

Massimo Fornasier, Lukang Sun, Rachel Ward

In this work, we take a step towards understanding overdamped Langevin dynamics for the minimization of a general class of objective functions . We establish well-pose…

math.OC2026

From Consensus-Based Optimization to Evolution Strategies: Proof of Global Convergence

Massimo Fornasier, Hui Huang, Jona Klemenc +1

Consensus-based optimization (CBO) is a powerful and versatile zero-order multi-particle method designed to provably solve high-dimensional global optimization problems, including…

math.OC2026

Constrained Consensus-Based Optimization and Numerical Heuristics for the Few Particle Regime

Jonas Beddrich, Enis Chenchene, Massimo Fornasier +2

Consensus-based optimization (CBO) is a versatile multi-particle optimization method for performing nonconvex and nonsmooth global optimizations in high dimensions. Proofs of globa…

math.AP2025

Regularity and positivity of solutions of the Consensus-Based Optimization equation: unconditional global convergence

Massimo Fornasier, Lukang Sun

Introduced in 2017 \cite{B1-pinnau2017consensus}, Consensus-Based Optimization (CBO) has rapidly emerged as a significant breakthrough in global optimization. This straightforward…