In mathematical optimization, Wolfe duality, named after Philip Wolfe, is type of dual problem in which the objective function and constraints are all differentiable functions. Using this concept a lower bound for a minimization problem can be found because of the weak duality principle.
Mathematical formulation For a minimization problem with inequality constraints,
minimize x f ( x ) s u b j e c t t o g i ( x ) ≤ 0 , i = 1 , … , m {\displaystyle {\begin{aligned}&{\underset {x}{\operatorname {minimize} }}&&f(x)\\&\operatorname {subject\;to} &&g_{i}(x)\leq 0,\quad i=1,\dots ,m\end{aligned}}}
the Lagrangian dual problem is
maximize u inf x ( f ( x ) + ∑ j = 1 m u j g j ( x ) ) s u b j e c t t o u i ≥ 0 , i = 1 , … , m {\displaystyle {\begin{aligned}&{\underset {u}{\operatorname {maximize} }}&&\inf _{x}\left(f(x)+\sum _{j=1}^{m}u_{j}g_{j}(x)\right)\\&\operatorname {subject\;to} &&u_{i}\geq 0,\quad i=1,\dots ,m\end{aligned}}}
where the objective function is the Lagrange dual function. Provided that the functions f {\displaystyle f} and g 1 , … , g m {\displaystyle g_{1},\ldots ,g_{m}} are convex and continuously differentiable, the infimum occurs where the gradient is equal to zero. The problem
maximize x , u f ( x ) + ∑ j = 1 m u j g j ( x ) s u b j e c t t o ∇ f ( x ) + ∑ j = 1 m u j ∇ g j ( x ) = 0 u i ≥ 0 , i = 1 , … , m {\displaystyle {\begin{aligned}&{\underset {x,u}{\operatorname {maximize} }}&&f(x)+\sum _{j=1}^{m}u_{j}g_{j}(x)\\&\operatorname {subject\;to} &&\nabla f(x)+\sum _{j=1}^{m}u_{j}\nabla g_{j}(x)=0\\&&&u_{i}\geq 0,\quad i=1,\dots ,m\end{aligned}}}
is called the Wolfe dual problem. This problem employs the KKT conditions as a constraint. Also, the equality constraint ∇ f ( x ) + ∑ j = 1 m u j ∇ g j ( x ) {\displaystyle \nabla f(x)+\sum _{j=1}^{m}u_{j}\nabla g_{j}(x)} is nonlinear in general, so the Wolfe dual problem may be a nonconvex optimization problem. In any case, weak duality holds.
See also Lagrangian duality Fenchel duality
References