The name is absent



Hence, K is not an optimal steady state. Thus, it must be the case that every optimal path converges to 0.

Proof of Proposition 4. Suppose not. Then there exists an optimal program (yt,xt,at)0~, y0 = y, where x0 >
0 so that y1 = f(x0) > 0. Since the invasion is currently controlled from every y0 e (0,f(y)), it follows from
Lemma 4 that C (
- , y) ≥Dx(x0) + δ[Ca(f(x0)-X1,f(x0))+Cy(f(x0)-X1,f(x0)]‰). The convexity of C in its
first argument and the convexity of D then imply

Ca(y, y) ≥ Dx(0) + δ[Ca(f(X0)-X1,f(X0))+Cy(f(X0)-X1,f(X0))]fx(X0)

This contradicts the inequality in the antecedent of the proposition. ■

Proof of Proposition 5. a. Suppose X(y) = {0}. Consider the alternative of increasing the remaining
invasion to and then eradicating it in the following period. By the principle of optimality
C(y,y)+D(0)+δ[C(0,0)+D(0)]+δ2V(0) ≤ C(y-ε,y) + D(ε) + δC(f(ε),f(ε)) + D(0)+δ2V(0). This implies
Ca(y,y) ≤ Dx(0) + δ[Ca(0,0)+Cy(0,0)]fx(0) = Dx(0) + δCa(0,0)fx(0), where the equality follows from B3.
This is a contradiction to the condition in 5a.

b. The condition in 5b implies 0 > -Ca(0,y) + Dx(y) + δsup0x<y,0a<fw [Ca(a,f(x))+Cy(a,f(x))]fx(x) ≥
-Ca(y-x,y) + Dx(x) + δ[Ca(A(f(x)),f(x))+Cy(A(f(x),f(x))]f
x(x) where the last inequality is due to B4. But if
x < y, Lemma 4a implies -C
a(y-x,y) + D..(x) + δ[Ca(A(f(x)),f(x))+Cy(A(f(x),f(x))]fx(x) ≥ 0, a
contradiction. ■

Proof of Proposition 6. Caa + Cay ≥ 0 implies Ca(0,0) ≤ Ca(y,y) for all y. Hence the condition in 5a holds
for all y and X(y) > 0 for all y. To prove the second part we want to show that there exists a
sufficiently close to zero, such that X(y) = y for all y e (0,ξ). Let x e X(y) and suppose that x < y. By
Lemma 4a

27



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