5.11. SADDLE POINTS∗ 133
Note that in a metric space, the above definitions of upper and lower semicontinuity interms of sequences are equivalent to the definitions that
f (x)≥ limr→0
sup{ f (y) : y ∈ B(x,r)} , f (x)≤ limr→0
inf{ f (y) : y ∈ B(x,r)}
respectively.Here is a technical lemma which will make the proof of the saddle point theorem
shorter. It seems fairly interesting also.
Lemma 5.11.3 Suppose H : A×B→R is strictly convex in the first argument and con-cave in the second argument where A,B are compact convex nonempty subsets of Banachspaces E,F respectively and x→H (x,y) is lower semicontinuous while y→H (x,y) is up-per semicontinuous. Let H (g(y) ,y) ≡ minx∈A H (x,y). Then g(y) is uniquely defined andalso for t ∈ [0,1] , limt→0 g(y+ t (z− y)) = g(y).
Proof: First suppose both z,w yield the definition of g(y) . Then
H(
z+w2
,y)<
12
H (z,y)+12
H (w,y)
which contradicts the definition of g(y). As to the existence of g(y) this is nothing morethan the theorem that a lower semicontinuous function defined on a compact set achievesits minimum.
Now consider the last claim about “hemicontinuity”, continuity along a line. For allx ∈ A, it follows from the definition of g that
H (g(y+ t (z− y)) ,y+ t (z− y))≤ H (x,y+ t (z− y))
By concavity of H in the second argument,
(1− t)H (g(y+ t (z− y)) ,y)+ tH (g(y+ t (z− y)) ,z) (5.6)≤ H (x,y+ t (z− y)) (5.7)
Now let tn → 0. Does g(y+ tn (z− y))→ g(y)? Suppose not. By compactness, each ofg(y+ tn (z− y)) is in a compact set and so there is a further subsequence, still denoted bytn such that g(y+ tn (z− y))→ x̂ ∈ A. Then passing to a limit in 5.7, one obtains, using theupper semicontinuity in one and lower semicontinuity in the other the following inequality.
H (x̂,y) ≤ lim infn→∞
(1− tn)H (g(y+ tn (z− y)) ,y)
+ lim infn→∞
tnH (g(y+ tn (z− y)) ,z)
≤ lim infn→∞
((1− tn)H (g(y+ tn (z− y)) ,y)+tnH (g(y+ tn (z− y)) ,z)
)≤ lim sup
n→∞
H (x,y+ tn (z− y))≤ H (x,y)
This shows that x̂ = g(y) because this holds for every x. Since tn → 0 was arbitrary, thisshows that in fact limt→0+ g(y+ t (z− y)) = g(y) ■
Now with this preparation, here is the min-max theorem.