English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We derive novel results on the ergodic theory of irreducible, aperiodicMarkov chains. We show how to optimally steer the network flow to a stationarydistribution over a finite or infinite time horizon. Optimality is with respectto an entropic distance between distributions on feasible paths. When the prioris reversible, it shown that solutions to this discrete time and space steeringproblem are reversible as well. A notion of temperature is defined forBoltzmann distributions on networks, and problems analogous to cooling (in thiscase, for evolutions in discrete space and time) are discussed.

Optimal steering to invariant distributions for networks flows