Averaging for switched DAEs: Convergence, partial averaging and stability

Weak convergence and averaging for ODE

This mostly expository paper shows how weak convergence methods provide simple, elegant proofs of (i) the stabilization of an inverted pendulum under fast vertical oscillations, (ii) the existence of particle traps induced by rapidly varying electric fields and (iii) the adiabatic invariance of ∫ Γ p dx for slowing varying planar Hamiltonian dynamics. Under an appropriate, but very restrictive,...

On Averaging Methods for Partial Diierential Equations

Convergence Speed in Distributed Consensus and Averaging

We propose three new algorithms for the distributed averaging and consensus problems: two for the fixed-graph case, and one for the dynamic-topology case. The convergence times of our fixed-graph algorithms compare favorably with other known methods, while our algorithm for the dynamic-topology case is the first to be accompanied by a polynomial-time bound on the worst-case convergence time. Th...

Prediction from Partial Data, Renormalization, and Averaging

We summarize and compare our recent methods for reducing the complexity of computational problems, in particular dimensional reduction methods based on the Mori–Zwanzig formalism of statistical physics, block Monte-Carlo methods, and an averaging method for deriving an effective equation for a nonlinear wave propagation problem. We show that their common thread is scale change and renormalization.

Convergence and Divergence in Standard and Averaging Reinforcement Learning

Although tabular reinforcement learning (RL) methods have been proved to converge to an optimal policy, the combination of particular conventional reinforcement learning techniques with function approximators can lead to divergence. In this paper we show why off-policy RL methods combined with linear function approximators can lead to divergence. Furthermore, we analyze two different types of u...