Mountain pass theorem

From HandWiki

The mountain pass theorem is an existence theorem from the calculus of variations, originally due to Antonio Ambrosetti and Paul Rabinowitz.[1] Given certain conditions on a function, the theorem demonstrates the existence of a saddle point. The theorem is unusual in that there are many other theorems regarding the existence of extrema, but few regarding saddle points.

Statement

The assumptions of the theorem are:

If we define:

Γ={𝐠C([0,1];H)|𝐠(0)=0,𝐠(1)=v}

and:

c=inf𝐠Γmax0t1I[𝐠(t)],

then the conclusion of the theorem is that c is a critical value of I.

Visualization

The intuition behind the theorem is in the name "mountain pass." Consider I as describing elevation. Then we know two low spots in the landscape: the origin because I[0]=0, and a far-off spot v where I[v]0. In between the two lies a range of mountains (at u=r) where the elevation is high (higher than a>0). In order to travel along a path g from the origin to v, we must pass over the mountains—that is, we must go up and then down. Since I is somewhat smooth, there must be a critical point somewhere in between. (Think along the lines of the mean-value theorem.) The mountain pass lies along the path that passes at the lowest elevation through the mountains. Note that this mountain pass is almost always a saddle point.

For a proof, see section 8.5 of Evans.

Weaker formulation

Let X be Banach space. The assumptions of the theorem are:

  • ΦC(X,𝐑) and have a Gateaux derivative Φ:XX* which is continuous when X and X* are endowed with strong topology and weak* topology respectively.
  • There exists r>0 such that one can find certain x>r with
max(Φ(0),Φ(x))<infx=rΦ(x)=:m(r).

In this case there is a critical point xX of Φ satisfying m(r)Φ(x). Moreover, if we define

Γ={cC([0,1],X)c(0)=0,c(1)=x}

then

Φ(x)=infcΓmax0t1Φ(c(t)).

For a proof, see section 5.5 of Aubin and Ekeland.

References

  1. Ambrosetti, Antonio; Rabinowitz, Paul H. (1973). "Dual variational methods in critical point theory and applications". Journal of Functional Analysis 14 (4): 349–381. doi:10.1016/0022-1236(73)90051-7. 

Further reading