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Fenchel duality : ウィキペディア英語版
Fenchel's duality theorem
In mathematics, Fenchel's duality theorem is a result in the theory of convex functions named after Werner Fenchel.
Let ''ƒ'' be a proper convex function on R''n'' and let ''g'' be a proper concave function on R''n''. Then, if regularity conditions are satisfied,
:\min_x ( f(x)-g(x) ) = \max_p ( g_\star(p)-f^\star(p)).\,
where ''ƒ'' 
*
is the convex conjugate of ''ƒ'' (also referred to as the Fenchel–Legendre transform) and ''g'' 
*
is the concave conjugate of ''g''. That is,
:f^ \left( x^ \right) := \sup \left \^n \right\}
:g_ \left( x^ \right) := \inf \left \^n \right\}
==Mathematical theorem==
Let ''X'' and ''Y'' be Banach spaces, f: X \to \mathbb \cup \ and g: Y \to \mathbb \cup \ be convex functions and A: X \to Y be a bounded linear map. Then the Fenchel problems:
:p^
* = \inf_ \
:d^
* = \sup_ \
satisfy weak duality, i.e. p^
* \geq d^
*. Note that f^
*,g^
* are the convex conjugates of ''f'',''g'' respectively, and A^
* is the adjoint operator. The perturbation function for this dual problem is given by F(x,y) = f(x) + g(Ax - y).
Suppose that ''f'',''g'', and ''A'' satisfy either
# ''f'' and ''g'' are lower semi-continuous and 0 \in \operatorname(\operatornameg - A \operatornamef) where \operatorname is the algebraic interior and \operatornameh where ''h'' is some function is the set \, or
# A \operatornamef \cap \operatornameg \neq \emptyset where \operatorname are the points where the function is continuous.
Then strong duality holds, i.e. p^
* = d^
*. If d^
* \in \mathbb then supremum is attained.

抄文引用元・出典: フリー百科事典『 ウィキペディア(Wikipedia)
ウィキペディアで「Fenchel's duality theorem」の詳細全文を読む



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