In mathematics and mathematical optimization, the convex conjugate of a function is a generalization of the Legendre transformation which applies to non-convex functions. It is also known as Legendre–Fenchel transformation, Fenchel transformation, or Fenchel conjugate (after Adrien-Marie Legendre and Werner Fenchel). The convex conjugate is widely used for constructing the dual problem in optimization theory, thus generalizing Lagrangian duality.
Definition
Let X {\displaystyle X} be a real topological vector space and let X ∗ {\displaystyle X^{*}} be the dual space to X {\displaystyle X} . Denote by
⟨ ⋅ , ⋅ ⟩ : X ∗ × X → R {\displaystyle \langle \cdot ,\cdot \rangle :X^{*}\times X\to \mathbb {R} }the canonical dual pairing, which is defined by ⟨ x ∗ , x ⟩ ↦ x ∗ ( x ) . {\displaystyle \left\langle x^{*},x\right\rangle \mapsto x^{*}(x).}
For a function f : X → R ∪ { − ∞ , + ∞ } {\displaystyle f:X\to \mathbb {R} \cup \{-\infty ,+\infty \}} taking values on the extended real number line, its convex conjugate is the function
f ∗ : X ∗ → R ∪ { − ∞ , + ∞ } {\displaystyle f^{*}:X^{*}\to \mathbb {R} \cup \{-\infty ,+\infty \}}whose value at x ∗ ∈ X ∗ {\displaystyle x^{*}\in X^{*}} is defined to be the supremum:
f ∗ ( x ∗ ) := sup { ⟨ x ∗ , x ⟩ − f ( x ) : x ∈ X } , {\displaystyle f^{*}\left(x^{*}\right):=\sup \left\{\left\langle x^{*},x\right\rangle -f(x)~\colon ~x\in X\right\},}or, equivalently, in terms of the infimum:
f ∗ ( x ∗ ) := − inf { f ( x ) − ⟨ x ∗ , x ⟩ : x ∈ X } . {\displaystyle f^{*}\left(x^{*}\right):=-\inf \left\{f(x)-\left\langle x^{*},x\right\rangle ~\colon ~x\in X\right\}.}This definition can be interpreted as an encoding of the convex hull of the function's epigraph in terms of its supporting hyperplanes.1
Examples
For more examples, see § Table of selected convex conjugates.
- The convex conjugate of an affine function f ( x ) = ⟨ a , x ⟩ − b {\displaystyle f(x)=\left\langle a,x\right\rangle -b} is f ∗ ( x ∗ ) = { b , x ∗ = a + ∞ , x ∗ ≠ a . {\displaystyle f^{*}\left(x^{*}\right)={\begin{cases}b,&x^{*}=a\\+\infty ,&x^{*}\neq a.\end{cases}}}
- The convex conjugate of a power function f ( x ) = 1 p | x | p , 1 < p < ∞ {\displaystyle f(x)={\frac {1}{p}}|x|^{p},1<p<\infty } is f ∗ ( x ∗ ) = 1 q | x ∗ | q , 1 < q < ∞ , where 1 p + 1 q = 1. {\displaystyle f^{*}\left(x^{*}\right)={\frac {1}{q}}|x^{*}|^{q},1<q<\infty ,{\text{where}}{\tfrac {1}{p}}+{\tfrac {1}{q}}=1.}
- The convex conjugate of the absolute value function f ( x ) = | x | {\displaystyle f(x)=\left|x\right|} is f ∗ ( x ∗ ) = { 0 , | x ∗ | ≤ 1 ∞ , | x ∗ | > 1. {\displaystyle f^{*}\left(x^{*}\right)={\begin{cases}0,&\left|x^{*}\right|\leq 1\\\infty ,&\left|x^{*}\right|>1.\end{cases}}}
- The convex conjugate of the exponential function f ( x ) = e x {\displaystyle f(x)=e^{x}} is f ∗ ( x ∗ ) = { x ∗ ln x ∗ − x ∗ , x ∗ > 0 0 , x ∗ = 0 ∞ , x ∗ < 0. {\displaystyle f^{*}\left(x^{*}\right)={\begin{cases}x^{*}\ln x^{*}-x^{*},&x^{*}>0\\0,&x^{*}=0\\\infty ,&x^{*}<0.\end{cases}}}
The 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.
Connection with expected shortfall (average value at risk)
Let F denote a cumulative distribution function of a random variable X. Then (integrating by parts), f ( x ) := ∫ − ∞ x F ( u ) d u = E [ max ( 0 , x − X ) ] = x − E [ min ( x , X ) ] {\displaystyle f(x):=\int _{-\infty }^{x}F(u)\,du=\operatorname {E} \left[\max(0,x-X)\right]=x-\operatorname {E} \left[\min(x,X)\right]} has the convex conjugate f ∗ ( p ) = ∫ 0 p F − 1 ( q ) d q = ( p − 1 ) F − 1 ( p ) + E [ min ( F − 1 ( p ) , X ) ] = p F − 1 ( p ) − E [ max ( 0 , F − 1 ( p ) − X ) ] . {\displaystyle f^{*}(p)=\int _{0}^{p}F^{-1}(q)\,dq=(p-1)F^{-1}(p)+\operatorname {E} \left[\min(F^{-1}(p),X)\right]=pF^{-1}(p)-\operatorname {E} \left[\max(0,F^{-1}(p)-X)\right].}
Ordering
A particular interpretation has the transform f inc ( x ) := arg sup t t ⋅ x − ∫ 0 1 max { t − f ( u ) , 0 } d u , {\displaystyle f^{\text{inc}}(x):=\arg \sup _{t}t\cdot x-\int _{0}^{1}\max\{t-f(u),0\}\,du,} as this is a nondecreasing rearrangement of the initial function f; in particular, f inc = f {\displaystyle f^{\text{inc}}=f} for f nondecreasing.
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.
Order reversing
Declare that f ≤ g {\displaystyle f\leq g} if and only if f ( x ) ≤ g ( x ) {\displaystyle f(x)\leq g(x)} for all x . {\displaystyle x.} Then convex-conjugation is order-reversing, which by definition means that if f ≤ g {\displaystyle f\leq g} then f ∗ ≥ g ∗ . {\displaystyle f^{*}\geq g^{*}.}
For a family of functions ( f α ) α {\displaystyle \left(f_{\alpha }\right)_{\alpha }} it follows from the fact that supremums may be interchanged that
( inf α f α ) ∗ ( x ∗ ) = sup α f α ∗ ( x ∗ ) , {\displaystyle \left(\inf _{\alpha }f_{\alpha }\right)^{*}(x^{*})=\sup _{\alpha }f_{\alpha }^{*}(x^{*}),}and from the max–min inequality that
( sup α f α ) ∗ ( x ∗ ) ≤ inf α f α ∗ ( x ∗ ) . {\displaystyle \left(\sup _{\alpha }f_{\alpha }\right)^{*}(x^{*})\leq \inf _{\alpha }f_{\alpha }^{*}(x^{*}).}Biconjugate
The convex conjugate of a function is always lower semi-continuous. The biconjugate f ∗ ∗ {\displaystyle f^{**}} (the convex conjugate of the convex conjugate) is also the closed convex hull, i.e. the largest lower semi-continuous convex function with f ∗ ∗ ≤ f . {\displaystyle f^{**}\leq f.} For proper functions f , {\displaystyle f,}
f = f ∗ ∗ {\displaystyle f=f^{**}} if and only if f {\displaystyle f} is convex and lower semi-continuous, by the Fenchel–Moreau theorem.Fenchel's inequality
For any function f and its convex conjugate f *, Fenchel's inequality (also known as the Fenchel–Young inequality) holds for every x ∈ X {\displaystyle x\in X} and p ∈ X ∗ {\displaystyle p\in X^{*}} :
⟨ p , x ⟩ ≤ f ( x ) + f ∗ ( p ) . {\displaystyle \left\langle p,x\right\rangle \leq f(x)+f^{*}(p).}Furthermore, the equality holds only when p ∈ ∂ f ( x ) {\displaystyle p\in \partial f(x)} . The proof follows from the definition of convex conjugate: f ∗ ( p ) = sup x ~ { ⟨ p , x ~ ⟩ − f ( x ~ ) } ≥ ⟨ p , x ⟩ − f ( x ) . {\displaystyle f^{*}(p)=\sup _{\tilde {x}}\left\{\langle p,{\tilde {x}}\rangle -f({\tilde {x}})\right\}\geq \langle p,x\rangle -f(x).}
Convexity
For two functions f 0 {\displaystyle f_{0}} and f 1 {\displaystyle f_{1}} and a number 0 ≤ λ ≤ 1 {\displaystyle 0\leq \lambda \leq 1} the convexity relation
( ( 1 − λ ) f 0 + λ f 1 ) ∗ ≤ ( 1 − λ ) f 0 ∗ + λ f 1 ∗ {\displaystyle \left((1-\lambda )f_{0}+\lambda f_{1}\right)^{*}\leq (1-\lambda )f_{0}^{*}+\lambda f_{1}^{*}}holds. The ∗ {\displaystyle {*}} operation is a convex mapping itself.
Infimal convolution
The infimal convolution (or epi-sum) of two functions f {\displaystyle f} and g {\displaystyle g} is defined as
( f ◻ g ) ( x ) = inf { f ( x − y ) + g ( y ) ∣ y ∈ R n } . {\displaystyle \left(f\operatorname {\Box } g\right)(x)=\inf \left\{f(x-y)+g(y)\mid y\in \mathbb {R} ^{n}\right\}.}Let f 1 , … , f m {\displaystyle f_{1},\ldots ,f_{m}} be proper, convex and lower semicontinuous functions on R n . {\displaystyle \mathbb {R} ^{n}.} Then the infimal convolution is convex and lower semicontinuous (but not necessarily proper),2 and satisfies
( f 1 ◻ ⋯ ◻ f m ) ∗ = f 1 ∗ + ⋯ + f m ∗ . {\displaystyle \left(f_{1}\operatorname {\Box } \cdots \operatorname {\Box } f_{m}\right)^{*}=f_{1}^{*}+\cdots +f_{m}^{*}.}The infimal convolution of two functions has a geometric interpretation: The (strict) epigraph of the infimal convolution of two functions is the Minkowski sum of the (strict) epigraphs of those functions.3
Maximizing argument
If the function f {\displaystyle f} is differentiable, then its derivative is the maximizing argument in the computation of the convex conjugate:
f ′ ( x ) = x ∗ ( x ) := arg sup x ∗ ⟨ x , x ∗ ⟩ − f ∗ ( x ∗ ) {\displaystyle f^{\prime }(x)=x^{*}(x):=\arg \sup _{x^{*}}{\langle x,x^{*}\rangle }-f^{*}\left(x^{*}\right)} and f ∗ ′ ( x ∗ ) = x ( x ∗ ) := arg sup x ⟨ x , x ∗ ⟩ − f ( x ) ; {\displaystyle f^{{*}\prime }\left(x^{*}\right)=x\left(x^{*}\right):=\arg \sup _{x}{\langle x,x^{*}\rangle }-f(x);}hence
x = ∇ f ∗ ( ∇ f ( x ) ) , {\displaystyle x=\nabla f^{*}\left(\nabla f(x)\right),} x ∗ = ∇ f ( ∇ f ∗ ( x ∗ ) ) , {\displaystyle x^{*}=\nabla f\left(\nabla f^{*}\left(x^{*}\right)\right),}and moreover
f ′ ′ ( x ) ⋅ f ∗ ′ ′ ( x ∗ ( x ) ) = 1 , {\displaystyle f^{\prime \prime }(x)\cdot f^{{*}\prime \prime }\left(x^{*}(x)\right)=1,} f ∗ ′ ′ ( x ∗ ) ⋅ f ′ ′ ( x ( x ∗ ) ) = 1. {\displaystyle f^{{*}\prime \prime }\left(x^{*}\right)\cdot f^{\prime \prime }\left(x(x^{*})\right)=1.}Scaling properties
If for some γ > 0 , {\displaystyle \gamma >0,} g ( x ) = α + β x + γ ⋅ f ( λ x + δ ) {\displaystyle g(x)=\alpha +\beta x+\gamma \cdot f\left(\lambda x+\delta \right)} , then
g ∗ ( x ∗ ) = − α − δ x ∗ − β λ + γ ⋅ f ∗ ( x ∗ − β λ γ ) . {\displaystyle g^{*}\left(x^{*}\right)=-\alpha -\delta {\frac {x^{*}-\beta }{\lambda }}+\gamma \cdot f^{*}\left({\frac {x^{*}-\beta }{\lambda \gamma }}\right).}Behavior under linear transformations
Let A : X → Y {\displaystyle A:X\to Y} be a bounded linear operator. For any convex function f {\displaystyle f} on X , {\displaystyle X,}
( A f ) ∗ = f ∗ A ∗ {\displaystyle \left(Af\right)^{*}=f^{*}A^{*}}where
( A f ) ( y ) = inf { f ( x ) : x ∈ X , A x = y } {\displaystyle (Af)(y)=\inf\{f(x):x\in X,Ax=y\}}is the preimage of f {\displaystyle f} with respect to A {\displaystyle A} and A ∗ {\displaystyle A^{*}} is the adjoint operator of A . {\displaystyle A.} 4
A closed convex function f {\displaystyle f} is symmetric with respect to a given set G {\displaystyle G} of orthogonal linear transformations,
f ( A x ) = f ( x ) {\displaystyle f(Ax)=f(x)} for all x {\displaystyle x} and all A ∈ G {\displaystyle A\in G}if and only if its convex conjugate f ∗ {\displaystyle f^{*}} is symmetric with respect to G . {\displaystyle G.}
Table of selected convex conjugates
The following table provides Legendre transforms for many common functions as well as a few useful properties.5
| g ( x ) {\displaystyle g(x)} | dom ( g ) {\displaystyle \operatorname {dom} (g)} | g ∗ ( x ∗ ) {\displaystyle g^{*}(x^{*})} | dom ( g ∗ ) {\displaystyle \operatorname {dom} (g^{*})} |
|---|---|---|---|
| f ( a x ) {\displaystyle f(ax)} (where a ≠ 0 {\displaystyle a\neq 0} ) | X {\displaystyle X} | f ∗ ( x ∗ a ) {\displaystyle f^{*}\left({\frac {x^{*}}{a}}\right)} | X ∗ {\displaystyle X^{*}} |
| f ( x + b ) {\displaystyle f(x+b)} | X {\displaystyle X} | f ∗ ( x ∗ ) − ⟨ b , x ∗ ⟩ {\displaystyle f^{*}(x^{*})-\langle b,x^{*}\rangle } | X ∗ {\displaystyle X^{*}} |
| a f ( x ) {\displaystyle af(x)} (where a > 0 {\displaystyle a>0} ) | X {\displaystyle X} | a f ∗ ( x ∗ a ) {\displaystyle af^{*}\left({\frac {x^{*}}{a}}\right)} | X ∗ {\displaystyle X^{*}} |
| α + β x + γ ⋅ f ( λ x + δ ) {\displaystyle \alpha +\beta x+\gamma \cdot f(\lambda x+\delta )} | X {\displaystyle X} | − α − δ x ∗ − β λ + γ ⋅ f ∗ ( x ∗ − β γ λ ) ( γ > 0 ) {\displaystyle -\alpha -\delta {\frac {x^{*}-\beta }{\lambda }}+\gamma \cdot f^{*}\left({\frac {x^{*}-\beta }{\gamma \lambda }}\right)\quad (\gamma >0)} | X ∗ {\displaystyle X^{*}} |
| | x | p p {\displaystyle {\frac {|x|^{p}}{p}}} (where p > 1 {\displaystyle p>1} ) | R {\displaystyle \mathbb {R} } | | x ∗ | q q {\displaystyle {\frac {|x^{*}|^{q}}{q}}} (where 1 p + 1 q = 1 {\displaystyle {\frac {1}{p}}+{\frac {1}{q}}=1} ) | R {\displaystyle \mathbb {R} } |
| − x p p {\displaystyle {\frac {-x^{p}}{p}}} (where 0 < p < 1 {\displaystyle 0<p<1} ) | R + {\displaystyle \mathbb {R} _{+}} | − ( − x ∗ ) q q {\displaystyle {\frac {-(-x^{*})^{q}}{q}}} (where 1 p + 1 q = 1 {\displaystyle {\frac {1}{p}}+{\frac {1}{q}}=1} ) | R − − {\displaystyle \mathbb {R} _{--}} |
| 1 + x 2 {\displaystyle {\sqrt {1+x^{2}}}} | R {\displaystyle \mathbb {R} } | − 1 − ( x ∗ ) 2 {\displaystyle -{\sqrt {1-(x^{*})^{2}}}} | [ − 1 , 1 ] {\displaystyle [-1,1]} |
| − log ( x ) {\displaystyle -\log(x)} | R + + {\displaystyle \mathbb {R} _{++}} | − ( 1 + log ( − x ∗ ) ) {\displaystyle -(1+\log(-x^{*}))} | R − − {\displaystyle \mathbb {R} _{--}} |
| e x {\displaystyle e^{x}} | R {\displaystyle \mathbb {R} } | { x ∗ log ( x ∗ ) − x ∗ if x ∗ > 0 0 if x ∗ = 0 {\displaystyle {\begin{cases}x^{*}\log(x^{*})-x^{*}&{\text{if }}x^{*}>0\\0&{\text{if }}x^{*}=0\end{cases}}} | R + {\displaystyle \mathbb {R} _{+}} |
| log ( 1 + e x ) {\displaystyle \log \left(1+e^{x}\right)} | R {\displaystyle \mathbb {R} } | { x ∗ log ( x ∗ ) + ( 1 − x ∗ ) log ( 1 − x ∗ ) if 0 < x ∗ < 1 0 if x ∗ = 0 , 1 {\displaystyle {\begin{cases}x^{*}\log(x^{*})+(1-x^{*})\log(1-x^{*})&{\text{if }}0<x^{*}<1\\0&{\text{if }}x^{*}=0,1\end{cases}}} | [ 0 , 1 ] {\displaystyle [0,1]} |
| − log ( 1 − e x ) {\displaystyle -\log \left(1-e^{x}\right)} | R − − {\displaystyle \mathbb {R} _{--}} | { x ∗ log ( x ∗ ) − ( 1 + x ∗ ) log ( 1 + x ∗ ) if x ∗ > 0 0 if x ∗ = 0 {\displaystyle {\begin{cases}x^{*}\log(x^{*})-(1+x^{*})\log(1+x^{*})&{\text{if }}x^{*}>0\\0&{\text{if }}x^{*}=0\end{cases}}} | R + {\displaystyle \mathbb {R} _{+}} |
See also
- Arnol'd, Vladimir Igorevich (1989). Mathematical Methods of Classical Mechanics (Second ed.). Springer. ISBN 0-387-96890-3. MR 0997295.
- Rockafellar, R. Tyrrell; Wets, Roger J.-B. (26 June 2009). Variational Analysis. Grundlehren der mathematischen Wissenschaften. Vol. 317. Berlin New York: Springer Science & Business Media. ISBN 9783642024313. OCLC 883392544.
- Rockafellar, R. Tyrell (1970). Convex Analysis. Princeton: Princeton University Press. ISBN 0-691-01586-4. MR 0274683.
Further reading
- Touchette, Hugo (2014-10-16). "Legendre-Fenchel transforms in a nutshell" (PDF). Archived from the original (PDF) on 2017-04-07. Retrieved 2017-01-09.
- Touchette, Hugo (2006-11-21). "Elements of convex analysis" (PDF). Archived from the original (PDF) on 2015-05-26. Retrieved 2008-03-26.
- Ellerman, David Patterson (1995-03-21). "Chapter 12: Parallel Addition, Series-Parallel Duality, and Financial Mathematics". Intellectual Trespassing as a Way of Life: Essays in Philosophy, Economics, and Mathematics (PDF). The worldly philosophy: studies in intersection of philosophy and economics. Rowman & Littlefield Publishers, Inc. pp. 237–268. ISBN 0-8476-7932-2. Archived (PDF) from the original on 2016-03-05. Retrieved 2019-08-09. Series G - Reference, Information and Interdisciplinary Subjects Series [1] (271 pages)
- Ellerman, David Patterson (May 2004) [1995-03-21]. "Introduction to Series-Parallel Duality" (PDF). University of California at Riverside. CiteSeerX 10.1.1.90.3666. Archived from the original on 2019-08-10. Retrieved 2019-08-09. [2] (24 pages)
References
"Legendre Transform". Retrieved April 14, 2019. https://physics.stackexchange.com/a/9360/821 ↩
Phelps, Robert (1993). Convex Functions, Monotone Operators and Differentiability (2 ed.). Springer. p. 42. ISBN 0-387-56715-1. 0-387-56715-1 ↩
Bauschke, Heinz H.; Goebel, Rafal; Lucet, Yves; Wang, Xianfu (2008). "The Proximal Average: Basic Theory". SIAM Journal on Optimization. 19 (2): 766. CiteSeerX 10.1.1.546.4270. doi:10.1137/070687542. /wiki/CiteSeerX_(identifier) ↩
Ioffe, A.D. and Tichomirov, V.M. (1979), Theorie der Extremalaufgaben. Deutscher Verlag der Wissenschaften. Satz 3.4.3 /wiki/Deutscher_Verlag_der_Wissenschaften ↩
Borwein, Jonathan; Lewis, Adrian (2006). Convex Analysis and Nonlinear Optimization: Theory and Examples (2 ed.). Springer. pp. 50–51. ISBN 978-0-387-29570-1. 978-0-387-29570-1 ↩