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SUMMARY:A Riemannian Framework for Optimization Problems in Reversible Mar
 kov Chains
DTSTART:20260519T090000Z
DTEND:20260519T100000Z
DTSTAMP:20260506T090800Z
UID:indico-event-342@events.dm.unipi.it
DESCRIPTION:Speakers: Miryam Gnazzo\n\nAn ergodic Markov chain with transi
 tion matrix $P$ and stationary distribution ${\\pi}$ is said to be reversi
 ble if $D_{{\\pi}} P = P^\\top D_{{\\pi}}$\, where $D_{{\\pi}}$ denotes th
 e diagonal matrix with the components of ${\\pi}$ on its diagonal. Reversi
 bility is a key property in Markov chain theory\, with applications rangin
 g from computational biology to network models\, such as those arising in 
 power grid analysis.\n \nIn this talk\, we present an approach to optimiz
 ation problems over reversible Markov chains\, with prescribed stationary 
 distribution ${\\pi}$. Our framework is based on Riemannian optimization o
 ver suitable manifolds of stochastic matrices associated with reversible c
 hains\, and it allows us to employ efficient and reliable Riemannian solve
 rs. Within this framework\, we address the approximation of the reversible
  Markov chain closest to a given one. In addition\, we describe an applica
 tion for minimizing Kemeny’s constant\, which measures the efficiency of
  a Markov chain in traversing its states. \n\nhttps://events.dm.unipi.it/
 event/342/
LOCATION:Aula Magna (Dipartimento di Matematica)
URL:https://events.dm.unipi.it/event/342/
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