Convex conjugate

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In mathematics, convex conjugation is a generalization of the Legendre transformation. It is also known as Legendre-Fenchel transformation or Fenchel transformation.

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Let X be a real normed vector space, and let X * be the dual space to X. Denote the dual pairing by

\langle \cdot , \cdot \rangle : X^{*} \times X \to \mathbb{R}.

For a function

f : X \to \mathbb{R} \cup \{ + \infty \}

taking values on the extended real number line the convex conjugate

f^\star : X^{*} \to \mathbb{R} \cup \{ + \infty \}

is defined by

f^{\star} \left( x^{*} \right) := \sup \left \{ \left. \left\langle x^{*} , x \right\rangle - f \left( x \right) \right| x \in X \right\},

or, equivalently, by

f^{\star} \left( x^{*} \right) := - \inf \left \{ \left. f \left( x \right) - \left\langle x^{*} , x \right\rangle \right| x \in X \right\}.

The convex conjugate of an affine function

f(x) = \left\langle a,x \right\rangle - b,\, a \in \mathbb{R}^n, b \in \mathbb{R}

is

f^\star\left(x^{*} \right) = \begin{cases} b, & x^{*} = a \\ \infty, & x^{*} \ne a \end{cases}

The convex conjugate of the absolute value function

f(x) = \left| x \right|

is

f^\star\left(x^{*} \right) = \begin{cases} 0, & \left|x^{*} \right| \le 1 \\ \infty, & \left|x^{*} \right| > 1 \end{cases}

The convex conjugate of the exponential function is

\exp^\star\left(x^{*} \right) = \begin{cases} x^{*} \ln x^{*} - x^{*} , & x^{*} > 0 \\ 0 , & x^{*} = 0 \\ \infty , & x^{*} < 0 \end{cases}

Convex conjugate and Legendre transform of the exponential function agree except that the domain of the convex conjugate is strictly larger as the Legendre transform is only defined for positive real numbers.

The convex conjugate of a closed convex function is again a closed convex function. The convex conjugate of a polyhedral convex function (a convex function with polyhedral epigraph) is again a polyhedral convex function.

Convex-conjugation is order-reversing: if f \le g then f^* \ge g^*. Here (f \le g ) :\iff (\forall x, f(x) \le g(x)).

The convex conjugate of a function is always lower semi-continuous. The biconjugate f** (the convex conjugate of the convex conjugate) is also the closed convex hull, i.e. the largest lower semi-continuous convex function smaller than f. For proper functions f, f = f** if and only if f is convex and lower semi-continuous.

For any proper convex function f and its convex conjugate f* Fenchel's inequality (also known as the Fenchel-Young inequality) holds:

\left\langle p,x \right\rangle \le f(x) + f^\star(p)

Let A be a linear transformation from Rn to Rm. For any convex function f on Rn, one has

\left(A f\right)^\star = f^\star A^\star

where A* is the adjoint operator of A defined by

\left \langle Ax, y^\star \right \rangle = \left \langle x, A^\star y^\star \right \rangle

A closed convex function f is symmetric with respect to a given set G of orthogonal linear transformations,

f\left(A x\right) = f(x), \; \forall x, \; \forall A \in G

if and only if its convex conjugate f* is symmetric with respect to G.

The infimal convolution of two functions f and g is defined as

\left(f \star_\inf g\right)(x) = \inf \left \{ f(x-y) + g(y) \, | \, y \in \mathbb{R}^n \right \}

Let f1, …, fm be proper convex functions on Rn. Then

\left( f_1 \star_\inf \cdots \star_\inf f_m \right)^\star = f_1^\star + \cdots + f_m^\star

Retrieved from "http://en.wikipedia.org/wiki/Convex_conjugate"
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