ScalingStacks

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Assume that (xn)(x_{n}) is a generic sequence of points such that hL¯​(xn)h_{\overline{L}}(x_{n}) tends to 00. By Theorem 3.3.1, hM¯​(xn)h_{\overline{M}}(x_{n}) converges to

1ℓ​(c^1​(L¯)​c^1​(M¯)|X).\frac{1}{\ell}({\widehat{c}}_{1}(\overline{L}){\widehat{c}}_{1}(\overline{M})|X). (3.4.2)

Except when both lower bounds are zero, this is strictly bigger than the lower bound of the proposition, which is equal to

1ℓ+m​(c^1​(L¯)​c^1​(M¯)|X).\frac{1}{\ell+m}({\widehat{c}}_{1}(\overline{L}){\widehat{c}}_{1}(\overline{M})|X).

In other words, the greedy obvious method to find points of small height for L¯+M¯\overline{L}+\overline{M} that first minimizes the height hL¯h_{\overline{L}}, only works up to the factor (ℓ+m)/ℓ>1(\ell+m)/\ell>1.

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