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For a proper convex function $E$ on a real Hilbert space, its subdifferential at $y$ isIn a Banach space, interpret the pairing with an element of the dual space. For a chosen subgradient $p\in \partial E(y)$, the Bregman distance isIf also $q\in \partial E(x)$, its symmetric Bregman distance isThe subgradient inequality proves nonnegativity. These quantities depend on the chosen subgradients and generally are not metrics: they need not distinguish different points or obey the triangle inequality.

For a convex functional $J:U\to \mathbb{R}\cup \{+\infty \}$ on a real Banach space, its subdifferential at $v$ is empty unless $J(v)$ is finite; otherwiseGiven $p_v\in \partial J(v)$, the Bregman distance isIt can be infinite if $J(u)$ is infinite. If $J$ is differentiable at $v$, the choice is its derivative; otherwise the base subgradient must be specified. Given also $p_u\in \partial J(u)$, add both directional distances to obtain the symmetric Bregman distanceThe symmetric version here is the sum, not half the sum. These nonnegative quantities are not generally metrics and can vanish at distinct points.

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.