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The coordinate conditions define a subgradient precisely when $p\in {\ell }^2$ and $J_w(u)<\infty$. They follow by varying one coordinate; summing their scalar supporting inequalities proves sufficiency. Since $w_k\ge c>0$, a subgradient can exist only at finitely supported $u$. At such a base point $v$, $D_{J_w}^p(u,v)={\sum }_k(w_k|u_k|-p_k{u}_k)$, allowing the value infinity. Merely having finite penalty does not guarantee a nonempty subdifferential in the ambient l2 sequence space.

For a finite convex function $h:\mathbb{R}\to \mathbb{R}$, the function $f(x)=h(c^{T}x+b)$ is convex. The subgradient inequality gives $c\partial h(u)\subseteq \partial f(x)$, where $u=c^{T}x+b$. Conversely, every subgradient $g$ of $f$ annihilates $\ker c^T$, because $f$ is constant on lines in those directions. For $c\ne 0$, write $g=cv$ and test the subgradient inequality at $y=x+(t-u)c/\Vert c{\Vert }_2^2$; this proves $v\in \partial h(u)$. For $c=0$, $f$ is constant and both sides are $\{0\}$ since a finite convex function on the real line has a nonempty subdifferential everywhere.

For a proper lower-semicontinuous convex functional on a real Hilbert space, $p\in \partial J(u)$ means $⟨v,p⟩-J(v)\le ⟨u,p⟩-J(u)$ for all $v$. Taking the supremum gives equality in the Fenchel–Young inequality: $J(u)+J^{\ast }(p)=⟨u,p⟩$. Apply the same argument to $J^{\ast }$ and use the Fenchel-Moreau theorem $J^{\ast \ast }=J$ to obtain the reciprocal subgradient condition.

The subgradient method minimizes a possibly nonsmooth convex function by choosing $g_k\in \partial f(x_k)$ and iteratingIf the subgradients are bounded by $G$ and a minimizer is within distance $R$ of $x_0$, a suitable constant or diminishing step size finds objective error at most $ϵ$ in $O(R^2{G}^2/{ϵ}^2)$ iterations.

For a nonempty closed convex set $C$, the finite-valued support function has subgradients exactly at the maximizing points of $C$. This follows from Fenchel–Young inequality and the conjugacy between the support function and the indicator functional of a constraint set. For compact $C$, the exposed maximizing set is nonempty.

For $p_u\in \partial J(u)$ and $p_v\in \partial J(v)$, add the two Bregman distances to get $D_J^{p_v}(u,v)+D_J^{p_u}(v,u)=⟨p_u-p_v,u-v⟩\ge 0$. The choice of subgradients matters. This is generally not a metric: it need not separate points or satisfy a triangle inequality.

For an absolutely one-homogeneous functional $J=\mathrm{TV}$ on a real Hilbert space, a subgradient $q$ at $u$ is characterized by $⟨q,v⟩\le J(v)$ for every $v$ and $⟨q,u⟩=J(u)$. A bounded vector field with $|z|\le 1$ and $q=\mathrm{div}z$ proves the inequality through the dual definition, provided cutoff approximation is valid in the ambient space. Equality is called a calibration. It certifies a global minimizer of total variation denoising, rather than only a minimizer within a chosen ansatz.

For data $g={\chi }_{B(0,R)}$ on ${\mathbb{R}}^2$, the minimizer of $\alpha \mathrm{TV}(u)+\frac{1}{2}\Vert u-g{\Vert }_2^2$ is the displayed amplitude shrinkage, including extinction at $\alpha =R/2$. The total variation calibration uses $z(x)=x/R$ inside the disk and $z(x)=Rx/|x{|}^2$ outside. Its continuous normal component creates no boundary measure, and $\mathrm{div}z=(2/R){\chi }_{B(0,R)}$. This subgradient certifies the positive branch; scaling it down certifies the zero branch. The quadratic fidelity is strictly convex, ensuring uniqueness.

Total variation flow is the $L^2$ gradient flow of the total variation seminorm on a domain. Formally it is $u_t=\mathrm{div}(\nabla u/|\nabla u|)$, but the subgradient formulation handles zero gradients and jumps. Adding quadratic fidelity gives a strictly convex stationary total variation denoising problem.