Convex conjugate

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

Definition

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. leftlangle x^{*} , x rightrangle - f left(x right) right| x in X right},

or, equivalently, by

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

Examples

The convex conjugate of an affine function
f(x) = leftlangle a,x rightrangle - b,, a in mathbb{R}^n, b in mathbb{R} is
f^starleft(x^{*} right)

begin{cases} b, & x^{*}

a infty, & x^{*} ne a end{cases}

The convex conjugate of a power function

f(x) = frac{1}{p}|x|^p,,1 is
f^starleft(x^{*} right) = frac{1}{q}|x^{*}|^q,,1 where tfrac{1}{p} + tfrac{1}{q} = 1.

The convex conjugate of the absolute value function

f(x) = left| x right|
is
f^starleft(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^starleft(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.

Properties

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)).

Biconjugate

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.

Fenchel's inequality

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

leftlangle p,x rightrangle le f(x) + f^star(p)

Behavior under linear transformations

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

fleft(A xright) = f(x), ; forall x, ; forall A in G
if and only if its convex conjugate f* is symmetric with respect to G.

Infimal convolution

The infimal convolution of two functions f and g is defined as
left(f star_inf gright)(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

References

External links

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Last updated on Friday October 10, 2008 at 18:36:44 PDT (GMT -0700)
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