Ekeland's variational principle

In mathematical analysis, Ekeland's variational principle, discovered by Ivar Ekeland,[1][2][3] is a theorem that asserts that there exist nearly optimal solutions to some optimization problems.

Ekeland's principle can be used when the lower level set of a minimization problems is not compact, so that the Bolzano–Weierstrass theorem cannot be applied. The principle relies on the completeness of the metric space.[4]

The principle has been shown to be equivalent to completeness of metric spaces.[5] In proof theory, it is equivalent to Π1
1
CA0 over RCA0, i.e. relatively strong.

It also leads to a quick proof of the Caristi fixed point theorem.[4][6]

History

Ekeland was associated with the Paris Dauphine University when he proposed this theorem.[1]

Ekeland's variational principle

Preliminary definitions

A function f : X R { , + } {\displaystyle f:X\to \mathbb {R} \cup \{-\infty ,+\infty \}} valued in the extended real numbers R { , + } = [ , + ] {\displaystyle \mathbb {R} \cup \{-\infty ,+\infty \}=[-\infty ,+\infty ]} is said to be bounded below if inf f ( X ) = inf x X f ( x ) > {\displaystyle \inf _{}f(X)=\inf _{x\in X}f(x)>-\infty } and it is called proper if it has a non-empty effective domain, which by definition is the set

dom f   = def   { x X : f ( x ) + } , {\displaystyle \operatorname {dom} f~{\stackrel {\scriptscriptstyle {\text{def}}}{=}}~\{x\in X:f(x)\neq +\infty \},}
and it is never equal to . {\displaystyle -\infty .} In other words, a map is proper if is valued in R { + } {\displaystyle \mathbb {R} \cup \{+\infty \}} and not identically + . {\displaystyle +\infty .} The map f {\displaystyle f} is proper and bounded below if and only if < inf f ( X ) + , {\displaystyle -\infty <\inf _{}f(X)\neq +\infty ,} or equivalently, if and only if inf f ( X ) R . {\displaystyle \inf _{}f(X)\in \mathbb {R} .}

A function f : X [ , + ] {\displaystyle f:X\to [-\infty ,+\infty ]} is lower semicontinuous at a given x 0 X {\displaystyle x_{0}\in X} if for every real y < f ( x 0 ) {\displaystyle y<f\left(x_{0}\right)} there exists a neighborhood U {\displaystyle U} of x 0 {\displaystyle x_{0}} such that f ( u ) > y {\displaystyle f(u)>y} for all u U . {\displaystyle u\in U.} A function is called lower semicontinuous if it is lower semicontinuous at every point of X , {\displaystyle X,} which happens if and only if { x X :   f ( x ) > y } {\displaystyle \{x\in X:~f(x)>y\}} is an open set for every y R , {\displaystyle y\in \mathbb {R} ,} or equivalently, if and only if all of its lower level sets { x X :   f ( x ) y } {\displaystyle \{x\in X:~f(x)\leq y\}} are closed.

Statement of the theorem

Ekeland's variational principle[7] — Let ( X , d ) {\displaystyle (X,d)} be a complete metric space and let f : X R { + } {\displaystyle f:X\to \mathbb {R} \cup \{+\infty \}} be a proper lower semicontinuous function that is bounded below (so inf f ( X ) R {\displaystyle \inf _{}f(X)\in \mathbb {R} } ). Pick x 0 X {\displaystyle x_{0}\in X} such that f ( x 0 ) R {\displaystyle f(x_{0})\in \mathbb {R} } (or equivalently, f ( x 0 ) + {\displaystyle f(x_{0})\neq +\infty } ) and fix any real ε > 0. {\displaystyle \varepsilon >0.} There exists some v X {\displaystyle v\in X} such that

f ( v )     f ( x 0 ) ε d ( x 0 , v ) {\displaystyle f(v)~\leq ~f\left(x_{0}\right)-\varepsilon \;d\left(x_{0},v\right)}
and for every x X {\displaystyle x\in X} other than v {\displaystyle v} (that is, x v {\displaystyle x\neq v} ),
f ( v )   <   f ( x ) + ε d ( v , x ) . {\displaystyle f(v)~<~f(x)+\varepsilon \;d(v,x).}

Proof

Define a function G : X × X R { + } {\displaystyle G:X\times X\to \mathbb {R} \cup \{+\infty \}} by

G ( x , y )   = def   f ( x ) + ε d ( x , y ) {\displaystyle G(x,y)~{\stackrel {\scriptscriptstyle {\text{def}}}{=}}~f(x)+\varepsilon \;d(x,y)}
which is lower semicontinuous because it is the sum of the lower semicontinuous function f {\displaystyle f} and the continuous function ( x , y ) ε d ( x , y ) . {\displaystyle (x,y)\mapsto \varepsilon \;d(x,y).} Given z X , {\displaystyle z\in X,} denote the functions with one coordinate fixed at z {\displaystyle z} by
G z   = def   G ( z , ) : X R { + }  and  {\displaystyle G_{z}~{\stackrel {\scriptscriptstyle {\text{def}}}{=}}~G(z,\cdot ):X\to \mathbb {R} \cup \{+\infty \}\;{\text{ and }}}
G z   = def   G ( , z ) : X R { + } {\displaystyle G^{z}~{\stackrel {\scriptscriptstyle {\text{def}}}{=}}~G(\cdot ,z):X\to \mathbb {R} \cup \{+\infty \}}
and define the set
F ( z )   = def   { y X : G z ( y ) f ( z ) }   =   { y X : f ( y ) + ε d ( y , z ) f ( z ) } , {\displaystyle F(z)~{\stackrel {\scriptscriptstyle {\text{def}}}{=}}~\left\{y\in X:G^{z}(y)\leq f(z)\right\}~=~\{y\in X:f(y)+\varepsilon \;d(y,z)\leq f(z)\},}
which is not empty since z F ( z ) . {\displaystyle z\in F(z).} An element v X {\displaystyle v\in X} satisfies the conclusion of this theorem if and only if F ( v ) = { v } . {\displaystyle F(v)=\{v\}.} It remains to find such an element.

It may be verified that for every x X , {\displaystyle x\in X,}

  1. F ( x ) {\displaystyle F(x)} is closed (because G x = def G ( , x ) : X R { + } {\displaystyle G^{x}\,{\stackrel {\scriptscriptstyle {\text{def}}}{=}}\,G(\cdot ,x):X\to \mathbb {R} \cup \{+\infty \}} is lower semicontinuous);
  2. if x dom f {\displaystyle x\notin \operatorname {dom} f} then F ( x ) = X ; {\displaystyle F(x)=X;}
  3. if x dom f {\displaystyle x\in \operatorname {dom} f} then x F ( x ) dom f ; {\displaystyle x\in F(x)\subseteq \operatorname {dom} f;} in particular, x 0 F ( x 0 ) dom f ; {\displaystyle x_{0}\in F\left(x_{0}\right)\subseteq \operatorname {dom} f;}
  4. if y F ( x ) {\displaystyle y\in F(x)} then F ( y ) F ( x ) . {\displaystyle F(y)\subseteq F(x).}

Let s 0 = inf x F ( x 0 ) f ( x ) , {\displaystyle s_{0}=\inf _{x\in F\left(x_{0}\right)}f(x),} which is a real number because f {\displaystyle f} was assumed to be bounded below. Pick x 1 F ( x 0 ) {\displaystyle x_{1}\in F\left(x_{0}\right)} such that f ( x 1 ) < s 0 + 2 1 . {\displaystyle f\left(x_{1}\right)<s_{0}+2^{-1}.} Having defined s n 1 {\displaystyle s_{n-1}} and x n , {\displaystyle x_{n},} let

s n   = def   inf x F ( x n ) f ( x ) {\displaystyle s_{n}~{\stackrel {\scriptscriptstyle {\text{def}}}{=}}~\inf _{x\in F\left(x_{n}\right)}f(x)}
and pick x n + 1 F ( x n ) {\displaystyle x_{n+1}\in F\left(x_{n}\right)} such that f ( x n + 1 ) < s n + 2 ( n + 1 ) . {\displaystyle f\left(x_{n+1}\right)<s_{n}+2^{-(n+1)}.} For any n 0 , {\displaystyle n\geq 0,} x n + 1 F ( x n ) {\displaystyle x_{n+1}\in F\left(x_{n}\right)} guarantees that s n f ( x n + 1 ) {\displaystyle s_{n}\leq f\left(x_{n+1}\right)} and F ( x n + 1 ) F ( x n ) , {\displaystyle F\left(x_{n+1}\right)\subseteq F\left(x_{n}\right),} which in turn implies s n + 1 s n {\displaystyle s_{n+1}\geq s_{n}} and thus also
f ( x n + 2 ) s n + 1 s n . {\displaystyle f\left(x_{n+2}\right)\geq s_{n+1}\geq s_{n}.}
So if n 1 {\displaystyle n\geq 1} then x n + 1 F ( x n ) = def { y X : f ( y ) + ε d ( y , x n ) f ( x n ) } {\displaystyle x_{n+1}\in F\left(x_{n}\right){\stackrel {\scriptscriptstyle {\text{def}}}{=}}\left\{y\in X:f(y)+\varepsilon \;d\left(y,x_{n}\right)\leq f\left(x_{n}\right)\right\}} and f ( x n + 1 ) s n 1 , {\displaystyle f\left(x_{n+1}\right)\geq s_{n-1},} which guarantee
ε d ( x n + 1 , x n )     f ( x n ) f ( x n + 1 )     f ( x n ) s n 1   <   1 2 n . {\displaystyle \varepsilon \;d\left(x_{n+1},x_{n}\right)~\leq ~f\left(x_{n}\right)-f\left(x_{n+1}\right)~\leq ~f\left(x_{n}\right)-s_{n-1}~<~{\frac {1}{2^{n}}}.}

It follows that for all positive integers n , p 1 , {\displaystyle n,p\geq 1,}

d ( x n + p , x n )     2 ε 1 2 n , {\displaystyle d\left(x_{n+p},x_{n}\right)~\leq ~2\;{\frac {\varepsilon ^{-1}}{2^{n}}},}
which proves that x := ( x n ) n = 0 {\displaystyle x_{\bullet }:=\left(x_{n}\right)_{n=0}^{\infty }} is a Cauchy sequence. Because X {\displaystyle X} is a complete metric space, there exists some v X {\displaystyle v\in X} such that x {\displaystyle x_{\bullet }} converges to v . {\displaystyle v.} For any n 0 , {\displaystyle n\geq 0,} since F ( x n ) {\displaystyle F\left(x_{n}\right)} is a closed set that contain the sequence x n , x n + 1 , x n + 2 , , {\displaystyle x_{n},x_{n+1},x_{n+2},\ldots ,} it must also contain this sequence's limit, which is v ; {\displaystyle v;} thus v F ( x n ) {\displaystyle v\in F\left(x_{n}\right)} and in particular, v F ( x 0 ) . {\displaystyle v\in F\left(x_{0}\right).}

The theorem will follow once it is shown that F ( v ) = { v } . {\displaystyle F(v)=\{v\}.} So let x F ( v ) {\displaystyle x\in F(v)} and it remains to show x = v . {\displaystyle x=v.} Because x F ( x n ) {\displaystyle x\in F\left(x_{n}\right)} for all n 0 , {\displaystyle n\geq 0,} it follows as above that ε d ( x , x n ) 2 n , {\displaystyle \varepsilon \;d\left(x,x_{n}\right)\leq 2^{-n},} which implies that x {\displaystyle x_{\bullet }} converges to x . {\displaystyle x.} Because x {\displaystyle x_{\bullet }} also converges to v {\displaystyle v} and limits in metric spaces are unique, x = v . {\displaystyle x=v.} {\displaystyle \blacksquare } Q.E.D.

For example, if f {\displaystyle f} and ( X , d ) {\displaystyle (X,d)} are as in the theorem's statement and if x 0 X {\displaystyle x_{0}\in X} happens to be a global minimum point of f , {\displaystyle f,} then the vector v {\displaystyle v} from the theorem's conclusion is v := x 0 . {\displaystyle v:=x_{0}.}

Corollaries

Corollary[8] — Let ( X , d ) {\displaystyle (X,d)} be a complete metric space, and let f : X R { + } {\displaystyle f:X\to \mathbb {R} \cup \{+\infty \}} be a lower semicontinuous functional on X {\displaystyle X} that is bounded below and not identically equal to + . {\displaystyle +\infty .} Fix ε > 0 {\displaystyle \varepsilon >0} and a point x 0 X {\displaystyle x_{0}\in X} such that

f ( x 0 )     ε + inf x X f ( x ) . {\displaystyle f\left(x_{0}\right)~\leq ~\varepsilon +\inf _{x\in X}f(x).}
Then, for every λ > 0 , {\displaystyle \lambda >0,} there exists a point v X {\displaystyle v\in X} such that
f ( v )     f ( x 0 ) , {\displaystyle f(v)~\leq ~f\left(x_{0}\right),}
d ( x 0 , v )     λ , {\displaystyle d\left(x_{0},v\right)~\leq ~\lambda ,}
and, for all x v , {\displaystyle x\neq v,}
f ( x ) + ε λ d ( v , x )   >   f ( v ) . {\displaystyle f(x)+{\frac {\varepsilon }{\lambda }}d(v,x)~>~f(v).}

The principle could be thought of as follows: For any point x 0 {\displaystyle x_{0}} which nearly realizes the infimum, there exists another point v {\displaystyle v} , which is at least as good as x 0 {\displaystyle x_{0}} , it is close to x 0 {\displaystyle x_{0}} and the perturbed function, f ( x ) + ε λ d ( v , x ) {\displaystyle f(x)+{\frac {\varepsilon }{\lambda }}d(v,x)} , has unique minimum at v {\displaystyle v} . A good compromise is to take λ := ε {\displaystyle \lambda :={\sqrt {\varepsilon }}} in the preceding result.[8]

See also

References

  1. ^ a b Ekeland, Ivar (1974). "On the variational principle". J. Math. Anal. Appl. 47 (2): 324–353. doi:10.1016/0022-247X(74)90025-0. ISSN 0022-247X.
  2. ^ Ekeland, Ivar (1979). "Nonconvex minimization problems". Bulletin of the American Mathematical Society. New Series. 1 (3): 443–474. doi:10.1090/S0273-0979-1979-14595-6. MR 0526967.
  3. ^ Ekeland, Ivar; Temam, Roger (1999). Convex analysis and variational problems. Classics in applied mathematics. Vol. 28 (Corrected reprinting of the (1976) North-Holland ed.). Philadelphia, PA: Society for Industrial and Applied Mathematics (SIAM). pp. 357–373. ISBN 0-89871-450-8. MR 1727362.
  4. ^ a b Kirk, William A.; Goebel, Kazimierz (1990). Topics in Metric Fixed Point Theory. Cambridge University Press. ISBN 0-521-38289-0.
  5. ^ Sullivan, Francis (October 1981). "A characterization of complete metric spaces". Proceedings of the American Mathematical Society. 83 (2): 345–346. doi:10.1090/S0002-9939-1981-0624927-9. MR 0624927.
  6. ^ Ok, Efe (2007). "D: Continuity I". Real Analysis with Economic Applications (PDF). Princeton University Press. p. 664. ISBN 978-0-691-11768-3. Retrieved January 31, 2009.
  7. ^ Zalinescu 2002, p. 29.
  8. ^ a b Zalinescu 2002, p. 30.

Bibliography

  • Ekeland, Ivar (1979). "Nonconvex minimization problems". Bulletin of the American Mathematical Society. New Series. 1 (3): 443–474. doi:10.1090/S0273-0979-1979-14595-6. MR 0526967.
  • Kirk, William A.; Goebel, Kazimierz (1990). Topics in Metric Fixed Point Theory. Cambridge University Press. ISBN 0-521-38289-0.
  • Zalinescu, C (2002). Convex analysis in general vector spaces. River Edge, N.J. London: World Scientific. ISBN 981-238-067-1. OCLC 285163112.
  • Zălinescu, Constantin (30 July 2002). Convex Analysis in General Vector Spaces. River Edge, N.J. London: World Scientific Publishing. ISBN 978-981-4488-15-0. MR 1921556. OCLC 285163112 – via Internet Archive.
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