Our approach is similar to the two-class case, except now we have to cycle over the classes as well in the outer loop. For each value of λ, we create an outer loop which cycles over ℓ and computes the partial quadratic approximation ℓQℓ about the current parameters (β̃0, β̃). Then we use coordinate descent to solve the penalized weighted least-squares problem (26)min(β0ℓ,βℓ)∈ℝp+1{−ℓQℓ(β0ℓ,βℓ)+λPα(βℓ)}.
