NANASep 2, 2015

Convergence analysis in convex regularization depending on the smoothness degree of the penalizer

arXiv:1406.1227
Originality Synthesis-oriented
AI Analysis

Provides theoretical convergence guarantees for inverse problems with smooth regularizers, but the analysis is incremental as it extends known results to a specific smoothness class.

The paper analyzes convergence of regularized solutions for least squares minimization with smooth convex regularizers, showing that for twice-differentiable regularizers the discrepancy can be bounded using the Hessian Lipschitz constant, while for once-differentiable regularizers a different parameter choice yields convergence.

The problem of minimization of the least squares functional with a smooth, lower semi-continuous, convex regularizer $J(\cdot)$ is considered to be solved. Over some compact and convex subset $Ω$ of the Hilbert space $\mathcal{H},$ the regularizer is implicitly defined as $ J(\cdot) : \mathcal{C}^{k}(Ω, \mathcal{H}) \rightarrow \mathbb{R}_{+}$ where $k \in \{1,2\}.$ So the cost functional associated with some given linear, compact and injective forward operator $\mathcal{T} :Ω\subset \mathcal{H} \rightarrow \mathcal{H},$ \begin{align} F_α(\cdot , f^δ) := \frac{1}{2} \Vert \mathcal{T}( \cdot ) - f^δ\Vert_{\mathcal{H}}^2 + αJ(\cdot) , \nonumber \end{align} where $f^δ$ is the given perturbed data with its perturbation amount $δ$ in it. Convergence of the regularized optimum solution $φ_{α(δ)} \in \mbox{argmin} F_α(φ, f^δ)$ to the true solution $φ^{\dagger}$ is analysed depending on the smoothness degree of the regularizer, \textit{i.e.} the cases $k \in \{1,2\}$ in $ J(\cdot) : \mathcal{C}^{k}(Ω, \mathcal{H}) \rightarrow \mathbb{R}_{+}.$ In both cases, we define such a regularization parameter that is in cooperation with the condition \begin{align} α(δ, f^δ) \in \{ α> 0 \mbox{ }\vert \mbox{ }\Vert\mathcal{T}φ_α^δ - f^δ\Vert \leq τδ\} , \nonumber \end{align} for some fixed $τ\geq 1.$ In the case of $k = 2,$ we are able to evaluate the discrepancy $\Vert\mathcal{T}φ_{α(δ)} - f^δ\Vert\leq τδ$ with the Hessian Lipschitz constant $L_H$ of the functional $F_α(\cdot , f^δ).$

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