Smoothness of the Augmented Lagrangian Dual in Convex Optimization
Jingwang Li, Vincent K. N. Lau · arXiv (Cornell University) · 2025
This paper focuses on the general linearly constrained optimization problem: $\min_{x \in \mathbb{R}^d} f(x) \ \text{s.t.} \ Ax = b$, where $f: \mathbb{R}^d \rightarrow \mathbb{R} \cup \{+\infty\}$ is a closed proper convex function, $A \in \mathbb{R}^{p \times d}$, and $b \in \mathbb{R}^p$. We define the standard dual function $ϕ(λ) = \inf_x \{f(x) + \langle λ, A x - b \rangle\}$, the augmented Lagrangian $\mathcal{L}_ρ(x, λ) = f(x) + \langle λ, Ax - b \rangle + \fracρ{2}\|Ax - b\|^2$ ($ρ> 0$), and the augmented Lagrangian dual function $ϕ_ρ(λ) = \inf_x \mathcal{L}_ρ(x, λ)$. Under the fundamental condition that $\text{dom} \ ϕ eq \emptyset$, we establish that: (1) $ϕ_ρ$ is $\frac{1}ρ$-smooth everywhere; and (2) the solution to $\min_{x \in \mathbb{R}^d} \mathcal{L}_ρ(x, λ)$ exists for any $λ\in \mathbb{R}^p$. These theoretical findings substantially weaken the stringent assumptions typically imposed in the literature to ensure such properties.