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Wolfe dual problem : ウィキペディア英語版
Wolfe duality

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,
: \begin
&\underset
& &g_i(x) \leq 0, \quad i = 1,\dots,m
\end
the Lagrangian dual problem is
: \begin
&\underset^m u_j g_j(x)\right) \\
&\operatorname
& &u_i \geq 0, \quad i = 1,\dots,m
\end
where the objective function is the Lagrange dual function. Provided that the functions f and g_1, \ldots, g_m are continuously differentiable, the infimum occurs where the gradient is equal to zero. The problem
: \begin
&\underset^m u_j g_j(x) \\
&\operatorname
& & \nabla f(x) + \sum_^m u_j \nabla g_j(x) = 0 \\
&&&u_i \geq 0, \quad i = 1,\dots,m
\end
is called the Wolfe dual problem. This problem employs the KKT conditions as a constraint. This problem may be difficult to deal with computationally, because the objective function is not concave in the joint variables (u,x). Also, the equality constraint \nabla f(x) + \sum_^m u_j \nabla g_j(x) is nonlinear in general, so the Wolfe dual problem is typically a nonconvex optimization problem. In any case, weak duality holds.

抄文引用元・出典: フリー百科事典『 ウィキペディア(Wikipedia)
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