An Accelerated Primal Dual Algorithm with Backtracking for Decentralized Constrained Optimization

This paper proposes D-APDB, a distributed accelerated primal-dual algorithm with backtracking that achieves optimal O(1/K)\mathcal{O}(1/K) convergence for decentralized constrained optimization over undirected networks without requiring prior knowledge of Lipschitz constants, making it the first method of its kind to handle private nonlinear constraints efficiently.

Qiushui Xu, Necdet Serhat Aybat, Mert Gürbüzbalaban2026-03-06🔢 math

On average population levels for models with directed diffusion in heterogeneous environments

This paper investigates the total population levels in heterogeneous environments with directed diffusion for any power-law relationship between intrinsic growth rate and carrying capacity, disproving the existence of a critical exponent that determines population prevalence over carrying capacity and analyzing how the total population depends on the diffusion coefficient under a generalized dispersal strategy.

André Rickes, Elena Braverman2026-03-06🔢 math