REVIEW 2 cited by
Alternating Optimization Approach for Computing $\alpha$-Mutual Information and $\alpha$-Capacity
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
abstract
This study presents alternating optimization (AO) algorithms for computing $\alpha$-mutual information ($\alpha$-MI) and $\alpha$-capacity based on variational characterizations of $\alpha$-MI using a reverse channel. Specifically, we derive several variational characterizations of Sibson, Arimoto, Augustin--Csisz{\' a}r, and Lapidoth--Pfister MI and introduce novel AO algorithms for computing $\alpha$-MI and $\alpha$-capacity; their performances for computing $\alpha$-capacity are also compared. The comparison results show that the AO algorithm based on the Sibson MI's characterization has the fastest convergence speed.
Forward citations
Cited by 2 Pith papers
-
Alternating minimization for computing doubly minimized Petz Renyi mutual information
Alternating minimization provably computes the doubly minimized Petz Rényi mutual information for all quantum states, with linear convergence for α∈(1,2] and O(1/n) convergence for α∈(1/2,1).
-
A Linearly Convergent Algorithm for Computing the Petz-Augustin Mean
A fixed-point iteration computes the Petz-Augustin mean with linear convergence in the Thompson metric for alpha > 1/2, giving the first non-asymptotic guarantees for this quantity and for the Petz capacity.
Discussion (0). Continue with ORCID to comment.