For a proper convex function and its convex conjugate ,
Equality holds exactly when , or equivalently .
Moreau decomposition 2026-09-28
For , the generalized Moreau decomposition is , where is the convex conjugate.
Use the sign convention
for the Lagrangian function in constrained optimization. The Lagrangian dual problem is
where is the convex conjugate. For this convex problem with affine equality constraints, the stationarity and feasibility parts of the Karush-Kuhn-Tucker conditions are
They say exactly that the displayed operator satisfies
Thus its zeros are precisely the primal-dual optimal points, subject to the usual attainment assumptions.
For and , the Euclidean inner product gives
The last two terms cancel by the defining property of the matrix transpose, and the first is nonnegative by part a. Hence is a monotone operator.
For a proper lower-semicontinuous convex function , its proximal operator is
The squared norm is strongly convex, so the minimizer is unique. The subdifferential sum rule gives the necessary and sufficient condition
More generally,
The subgradient inversion rule for the convex conjugate says exactly when . Hence
which is precisely the proximal optimality condition
Since , this proves the generalized Moreau decomposition
The function is the support function . For a nonempty compact convex set,
so its convex conjugate is the indicator function . Applying the Moreau decomposition,
Multiplication of an indicator function by a positive scalar does not change it, and its proximal operator is the Euclidean projection onto a convex set. Therefore
Support function 2026-09-28
The support function of a nonempty set is . It is convex and positively homogeneous, and for a closed convex set its convex conjugate is the indicator function of .