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This stronger source condition in variational regularization requires a subgradient $w=K^{\ast }Kv$ at $u^{†}$. For each $\alpha >0$ it is equivalent to making $u^{†}$ minimize $\Vert Ku-K\bar{u}{\Vert }^2/2+\alpha J(u)$ for artificial input $\bar{u}=u^{†}+\alpha v$. The subgradient optimality condition proves both implications. The source vector may be zero: $K=I$ and $J\equiv 0$ satisfy the condition but admit no nonzero source vector.

Assume the displayed variational minimizer exists and $K:U\to V$ is bounded and linear. The smooth data-fidelity term and the subdifferential sum rule giveUse the supplied source condition in variational regularization $p^{†}=K^{\ast }w\in \partial J(u^{†})$. Put $e=f^{\delta }-f$ and $q=K(u_{\alpha }-u^{†})$, so $\Vert e\Vert \le \delta$. By the symmetric Bregman distance identity,Dropping the last square and using $\Vert e-\alpha w{\Vert }^2\le 2\Vert e{\Vert }^2+2{\alpha }^2\Vert w{\Vert }^2$ proves the source-condition estimate for symmetric Bregman distance:The source condition also makes $u^{†}$ a $J$-minimizing exact solution: for $Ku=f$, its subgradient inequality has zero pairing with $u-u^{†}$. Existence of $u_{\alpha }$ is not implied by properness and lower semicontinuity alone; it is a hypothesis when no coercivity/compactness condition is provided.

The source condition in variational regularization at an exact solution $u^{†}$ is the existence of a source element ${\mu }^{†}\in {\mathcal{V}}^{\ast }$ such thatFor a Hilbert data space one identifies ${\mathcal{V}}^{\ast }$ with $\mathcal{V}$. If $u$ satisfies $Au=f$ and $A^{\ast }\mu \in \partial J(u)$, then every other exact solution $v$ obeys the subgradient inequalityHence $u$ is a J-minimizing solution. This is source-condition certification of a penalty-minimizing solution.

A $J$-minimizing solution is an exact solution $u_J^{†}$ satisfyingThe source condition in variational regularization says that some $p^{†}\in Y$ satisfieswhere $\partial J$ is the subdifferential.

Fix $\alpha >0$. The subgradient optimality condition forat $u_J^{†}$ isIf the source condition holds, choose $g=f+\alpha p^{†}$. Since $A{u}_J^{†}=f$, the displayed inclusion holds. Because the objective is a convex function, this condition is necessary and sufficient for global minimality.

Conversely, if the stated range condition holds for some $g$, optimality supplies $\xi \in \partial J(u_J^{†})$ withThus $\xi =A^{\ast }((g-f)/\alpha )$, which is precisely the source condition. This proves the equivalence.

An element $u^{†}$ is a $J$-minimizing solution whenIt satisfies the source condition in variational regularization when there is a $w^{†}\in Y^{\ast }$ such thatwhere $\partial J(u^{†})$ is the subdifferential of the convex functional $J$.

The exact solution is feasible becauseThe feasible set $E_{\delta }$ is convex and weakly closed. A minimizing sequence has bounded residual and bounded $J$; the coercivity assumption from part (a), applied to a fixed positive weighted objective, makes it bounded in $X$. Reflexivity gives a weakly convergent subsequence, and weak lower semicontinuity of the residual and $J$ keeps its limit feasible and minimizing.

Since ${\hat{u}}_{\delta }$ minimizes $J$ over $E_{\delta }$,The source condition in variational regularization and feasibility then yieldThus the claimed constant is $\boxed{C=(c+1)\Vert w^{†}{\Vert }_{Y^{\ast }}}$.

Let $K:U\to V$ be a bounded linear operator from a real Banach space to a real Hilbert space, let $J$ be a proper lower-semicontinuous convex function, and let $\alpha >0$ and $\Vert f^{\delta }-K{u}^{†}\Vert \le \delta$. For an existing minimizer of $\Vert Ku-f^{\delta }{\Vert }^2/2+\alpha J(u)$ and a source condition in variational regularization $p^{†}=K^{\ast }w\in \partial J(u^{†})$, let $e=f^{\delta }-K{u}^{†}$ and $q=K(u_{\alpha }-u^{†})$. Its optimality subgradient is $p_{\alpha }=K^{\ast }(f^{\delta }-K{u}_{\alpha })/\alpha$. Then $\alpha D_J^{\mathrm{sym}}=⟨e-\alpha w,q⟩-\Vert q{\Vert }^2\le \Vert e-\alpha w{\Vert }^2/4$. The displayed estimate follows from $\Vert e-\alpha w{\Vert }^2\le 2{\delta }^2+2{\alpha }^2\Vert w{\Vert }^2$.

This range condition puts the exact solution in the operator range of the adjoint operator. It controls the small-singular value components of the solution and supplies convergence rates for spectral regularization methods. It is the quadratic-penalty case of a source condition in variational regularization.