This study presents alternating optimization (AO) algorithms for computing $α$-mutual information ($α$-MI) and $α$-capacity based on variational characterizations of $α$-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 $α$-MI and $α$-capacity; their performances for computing $α$-capacity are also compared. The comparison results show that the AO algorithm based on the Sibson MI's characterization has the fastest convergence speed.