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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 Rⁿ and let g be a proper concave function on Rⁿ. 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 convex conjugate of g.

[edit] One dimensional illustration

In the following figure, the minimization problem on the left side of the equation is illustrated. One seeks to vary x such that the vertical distance between the convex and concave curves at x is as small as possible. The position of the vertical line in the figure is close to, but not exactly at the optimum.

The next figure illustrates the maximization problem on the right hand side of the above equation. Tangents are drawn to each of the two curves such that both tangents have the same slope p. The problem is to adjust p in such a way that the two tangents are as far away from each other as possible (more precisely, such that the point where they intersect the y-axis are as far from each other as possible). Imagine the two tangents as metal bars with vertical springs between them that push them apart and against the two parabolas that are fixed in place.

Fenchel's theorem states that the two problems have the same solution. The points having the minimum vertical separation are also the tangency points for the maximally separated parallel tangents.

[edit] See also

[edit] References

Rockafellar, Ralph Tyrell (1996). Convex Analysis. Princeton University Press. ISBN 0-691-01586-4. See page 327.