The setting is the same as in this question. Precisely, we have a strictly convex hypersurface $S\subseteq(\mathbb{R}^{2n},\omega_0)$, where $\omega_0$ is the standard symplectic form, and we define $F$ as follows. We assumed $0\in S$, so any ray issuing from the origin intersects $S$ in precisely one point $\xi$. Thus, for $x\neq0$, there is a unique $\lambda$ such that $\xi=\lambda^{-1}x\in S$. We define $F(x)=\lambda$ for $x\neq0$ and $F(0)=0$. We thus obtain a $\mathcal{C}^2$ function, which makes $H_F$ (the Hessian matrix) symmetric. As seen in the linked question, by Euler's theorem for homogeneous functions we deduce a formula which in turn gives $H_F(x)x=0$ for all $x$. So for no $x$ is this hessian nonsingular. However, according to Hofer-Zehnder (p. 25, preliminaries to the existence theorem for periodic orbits on convex regular compact energy surfaces), we can, by convexity of $S$, conclude that the restriction to the tangent space of $S$ of the bilinear form induced by the Hessian, i.e. $H_F|TS$, is positive. I assume this means $H_F|TS$ is symmetric and positive definite. We have seen it is symmetric by regularity and the Schwarz lemma. But how do I deduce it is positive definite? And above all, how does convexity enter the proof? I really don't know where to start here…
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Is the symplectic form relevant here? – Amitai Yuval Sep 09 '15 at 10:57
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@AmitaiYuval I mentioned it for the sake of completeness of context. I guess it might be irrelevant. – MickG Sep 09 '15 at 11:00
1 Answers
Sketch: We want to prove that for any $p\in S$ and $v\in T_pS$, the second derivative of $F$ at $p$ in the direction $v$ is positive. Note that for any given such $p,v,$ everything that matters to us happens in the $2$-dimensional space $\mathrm{span}(p,v).$ Hence, it is enough to solve the problem for $n=1$.
So, we have a convex curve $S$ in $\mathbb{R}^2$, and we have some $p\in S$. Let $$l=\{p+t\cdot v|t\in\mathbb{R}\}$$ denote the line tangent to $S$ at $p$. (In other words, $0\neq v\in T_pS$). By definition of $F$, we have $F(p)=1$. By convexity, we have $F(q)>1$ for every $p\neq q\in l$. This means that the function $F|_l$ admits a minimum at $p$, and hence, the second derivative in the direction $v$ is positive. (Well, non-negative, anyway. All you have to do now is use strict convexity in order to show that the second derivative does not vanish at $p$).
Edit: We show that strong convexity of a hypersurface is independent of the defining function. Let $S\subset\mathbb{R}^n$ be defined locally both by $\{f=0\}$ and $\{g=0\}$, where $f,g:U\to\mathbb{R}$ are smooth and both derivatives $df, dg$, don't vanish on $S$. Let $p\in S\cap U$. It follows that there is a smooth $h:U\to\mathbb{R}$, such that $g=hf$. Since $dg_p\neq0,$ it follows from the Leibniz rule that necessarily $h(p)\neq0$.
We assume now that $S$ is strongly convex with respect to $f$, that is, $\nabla^2f_p$ is positive definite on the tangent space $T_pS$. Let $v\in T_pS$. Then$$dg(v)=dh(v)f+hdf(v),$$and at $p$ we have$$\nabla^2g_p(v)=\nabla^2h_p(v)f(p)+2dh_p(v)df_p(v)+h(p)\nabla^2f_p(v).$$By the assumptions, the first and second terms vanish. The last term has the sign of $h(p)$. Thus, if $g$ is such that $h$ is positive, then $\nabla^2g_p$ is positive definite on $T_pS$, and if $h$ is negative, $\nabla^2g_p$ is negative definite on the tangent space.
Edit 2: The above argument works whenever $f$ and $g$ are at least $C^3$. We now treat the case where they are only $C^2$ (then $h$ is $C^2$ far away from $S$, but only $C^1$ on $S$). Since $g$ is $C^2$, we have$$\nabla^2g_p(v)=\lim_{x\to p}\nabla^2g_x(v)=\lim_{x\to p}\nabla^2h_x(v)f(x)+2dh_x(v)df_x(v)+h(x)\nabla^2f_x(v),$$ where we let $x$ approach $p$ from outside of $S$, and so the limit is well defined. It suffices then to prove$$\lim_{x\to p}\nabla^2h_x(v)f(x)=0.$$For that, we change the coordinates and assume $S=\{x_n=0\}.$ Note that when we do so, the expression for the Hessian tensor includes a correction involving the Christoffel symbols. However, this correction is bounded, as it involves only the first derivatives of $h$, and hence vanishes when multiplied by $f$, as $x$ gets closer to $p$. Consequently, we may consider the standard Hessian in our new coordinates.
Outside of $S$, we have $h=g/f$. Hence, for $1\leq i\leq n-1$,$$h_i=\frac{g_if-gf_i}{f^2}.$$Differentiating again, \begin{align} h_{ii}&=\frac{(g_{ii}f+g_if_i-g_if_i-gf_{ii})f^2-(g_if-gf_i)2ff_i}{f^4}\\ &=\frac{g_{ii}}{f}-\frac{gf_{ii}}{f^2}-\frac{2g_if_i}{f^2}+\frac{2gf_i^2}{f^3}, \end{align} and hence, \begin{align} h_{ii}f&=g_{ii}-\frac{g}{f}f_{ii}-2f_i\frac{g_if-gf_i}{f^2}\\ &=g_{ii}-hf_{ii}-2f_ih_i. \end{align} Since on $S$ both $f$ and $g$ are constant, all the terms vanish on $S$. It follows that$$\lim_{x\to p}\nabla^2h_x(v)f(x)=0,$$and the proof is complete.

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By convexity if $q\in l$ and $q\neq p$ $q$ is out of the curve, outside the region the curve bounds, and hence $F(q)>1$, OK. – MickG Sep 09 '15 at 12:05
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So we have a minimum, thus non-negative derivative. But I can't really see how the fact that any segment with endpoints on $S$ must be contained in the interior of the region $S$ bounds except for the endpoints, i.e. the strict convexity of $S$, should help in proving the second derivative doesn't vanish at $p$. I mean, I can tell that the level sets of this function, being it homogeneous of rank 1 for positive multipliers, are simply dilations of $S$, so all strictly convex. But even this doesn't seem useful for what I want to prove. Strict convexity implies the tangent line to $p$ has to… – MickG Sep 09 '15 at 12:10
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…intersect $S$ only at $p$, and never the interior. Which gives us $F(q)>1$ for all $q\neq p$ in $l$, which we already know. What more can I extract out of strict convexity that convexity doesn't give me? – MickG Sep 09 '15 at 12:12
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@MickG Oooh, if this is your definition for strict convexity, then the claim doesn't hold. You can construct a counterexample using any function $f:(-\epsilon,\epsilon)\to\mathbb{R}$ such that $f(0)=1$ is a minimum, for every $t\neq0,;f''(t)>0$, but $f''(0)=0$. – Amitai Yuval Sep 09 '15 at 12:24
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In fact, I only ever heard of strict convexity in this book, so since the book lacked a definition I followed the those of Wikipedia. – MickG Sep 09 '15 at 12:44
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What definition would you give for strict convexity of $S$ @AmitaiYuval? – MickG Sep 19 '15 at 13:45
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@MickG Well, like you, maybe, I had never encountered the term before reading your post. However, for the claim to hold, I guess strict convexity has to have something to do with second derivative. Option: locally, the hypersurface $S$ is the graph of a function. Say $S$ is strictly convex if the Hessian of this function is positive definite at every point. (if it is only non-negative definite, say $S$ is just convex). – Amitai Yuval Sep 19 '15 at 20:51
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It is interesting that strict convexity of functions is defined in a similar way to Wikipedia's definition for regions… – MickG Sep 19 '15 at 20:55
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A function should be strictly convex iff its epigraph is. But the epigraph is iff the level sets are, I think. ANd stricly convex means the Hessian is pd outside saddle points or extrema. Could this work? – MickG Sep 19 '15 at 21:03
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(Strict) convexity of a function and its epigraph are coincident by definition. If $f$ is convex, it cannot have two minima, for if it did, between them it should have a maximum. Indeed, suppose we take $f$ convex and with two minima $x_1,x_2$. COnsider the segment $x_1+t(x_2-x_1)$. Restrict $f$ to it. Or rather, restrict $f$ to the line through the two minima. We get a function of $t$, $g(t)=f(x_1+t(x_2-x_1)$, which will also have two minima, meaning its derivative has to take two zeros. But more than that, if we start from $x_1$, we have a positive derivative on the right, then on the left… – MickG Sep 19 '15 at 21:25
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… of $x_2$ we have a negative derivative. I am assuming $x_1<x_2$. But I'm getting a bit confused, I should be taking $t=0$ and $t=1$. Anyway the above gives us another zero of the derivative, with a change of sign, ergo a maximum. So at a certain point on that segment, $f$ has a directional maximum. But then that restriction $g$ is not convex, so we can find a segment with endpoints on the graph of $g$ which is not contained in the epigraph, and that segment will be transported to a similar segment for $f$, violating strict convexity. So voilà: the minimum is unique. – MickG Sep 19 '15 at 21:27
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Let $H_f(x)$ be non-pd. It means there is a direction $v$ along which $g(t)=f(x+tv)$ has zero second derivative. But $g$ must be strictly convex. So if we prove that for real-valued single-variable functions strict convexity implies the second derivative is strictly positive outside the global minimum, we conclude the Hessian of a convex function must be pd outside the global minimum. – MickG Sep 19 '15 at 21:30
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So suppose $g:\mathbb{R}\to\mathbb{R}$ is strictly convex and $f''(x_0)=0$. I think only the following can happen: 1) $x_0$ is a minimum, and then it is the unique global minimum; 2) $x_0$ is a maximum, but that is not allowed to convex functions; 3) $x_0$ is an inflexion point, but then on one side the function is not convex. And that's a qed $\square$. – MickG Sep 19 '15 at 21:32
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Now if we prove that strict convexity of level sets implies strict convexity of functions, then we are done, since 1 is clearly not a global minimum for our function. Let us make the reasonable hypothesis that if we consider $\mathbb{R}^{n+1}$ and the preimage of a strictly convex set from $\mathbb{R}^n$ with the $n+1$-th projection map, this is strictly convex in $\mathbb{R}^{n+1}$. Mm I'm not sure this leads me anywhere… – MickG Sep 19 '15 at 21:35
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OK no, strict convexity of the level sets doesn't imply that of the function. Just take any downward-facing parabola: the level sets are the endpoints of segments, so they are strictly convex, yet the epigraph is concave. – MickG Sep 19 '15 at 21:38
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So now we know a strictly convex function has positive definite Hessian everywhere except at the global minimum, which is unique. – MickG Sep 19 '15 at 21:42
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I think the second derivative can vanish in other points too. Take a convex function $\mathbb{R}\to\mathbb{R}$ with second derivative vanishing only at the minimum. Now add a linear term to your function. The second derivative doesn't change. But the point where it vanishes is no longer a minimum. – Amitai Yuval Sep 19 '15 at 22:06
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But does it stay strictly convex when you do that? A linear term is convex, but not strictly, so I wouldn't expect adding a linear term to preserve strict convexity. Indeed, degree-1 homogeneity impedes strict convexity according to the book. So you may be right that this claim on strictly convex functions won't help us with our deg-1 homogeneous $F$. – MickG Sep 20 '15 at 08:06
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I'm starting to think the positive definiteness of the Hessian of $F$ restricted to that tangent space has to do with the particular construction of this $F$, and that it doesn't hold in general. – MickG Sep 29 '15 at 21:18
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Suppose "strictly convex" means "there is a defining function for $S$ whose Hessian is p.d. on the tangent to $S$". How does that imply this particular defining function $F$ (yeah, a defining function should be 0 on $S$, but anyways…) has p.d. Hessian on the tangent? @AmitaiYuval – MickG Oct 15 '15 at 18:09
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I think HZ defines strict convexity to be what Krantz calls strong convexity, i.e. having a defining function with Hessian p.d. on the tangent space to the boundary. This p.d.-ness is geometric, i.e. if one defining function has this p.d.-ness, all have. Add this, plus a proof of the just-stated fact, into your answer, and I will accept. – MickG Oct 17 '15 at 13:18
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@MickG For some obscure reason, I accepted the challenge. Have a look at the edit. – Amitai Yuval Oct 17 '15 at 17:16
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Now the function $h$ has to be $\frac gf$ outside $S$, so if the two functions are not smooh it might not be smooth. For example, in the case of the $F$ in the questions, it might not be smooth. But we only need regularity just short of $\mathcal C^2$, so no problem. How do I show it has this regularity on $S$, where both $g$ and $f$ are zero? @AmitaiYuval – MickG Oct 17 '15 at 18:40
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What you're asking now, dear @MickG, is a different question in real analysis. I think you should post it separately. I also think the answer I provided here is satisfactory. If you do post the other question, please let me know, I might even answer it too. You're invited to accept both answers (you can start with this one). – Amitai Yuval Oct 17 '15 at 20:03
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OK I will post another question, but I guess the link to it belongs into your answer too. I will certainly put it in the comments. – MickG Oct 17 '15 at 20:05
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Uh, are we not neglecting mixed derivatives somewhere? I mean, Christoffel symbols apart, $\nabla^2h_x(v)$ should be $g_{ij}(x)v_iv_j$, so shouldn't we prove all terms with mixed indices vanish on $S$? – MickG Oct 18 '15 at 14:17
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I obviously meant $g_{ij}v_iv_j$. Anyway, $h_{ij}=(\frac{g_if-gf_i}{f^2})j=\frac{g{ij}f}{f^2}+\frac{g_if_j}{f^2}-\frac{2g_iff_j}{f^3}-\frac{g_jf_i}{f^2}-\frac{gf_{ij}}{f^2}+\frac{2gf_if_j}{f^3}$, so $h_{ij}f=g_{ij}+\frac{g_if_j}{f}-\frac{2g_if_j}{f}-\frac{g_jf_i}{f}-\frac{gf_{ij}}{f}+\frac{2gf_if_j}{f^2}$, so all terms vanish as in the $ii$ case, right? – MickG Oct 18 '15 at 16:31
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Let me rewrite the above expression: $g_{ij}-hf_{ij}+\frac{g_if_j-g_jf_i}{f}-\frac2f(g_if_j-hf_if_j)$. – MickG Oct 18 '15 at 16:38
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Two mistakes: 1) "I obviously meant" *$h_{ij}(x)v_iv_j$, 2) the last term should have $fg_if_j-hf_if_j$ in the brackets. – MickG Oct 18 '15 at 16:41
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1@MickG This is not needed. We say the vector $v$ is mapped under the coordinate change to $\partial/\partial x_i$. Then the Hessian of $v$ is equal to the corresponding Hessian of $\partial/\partial x_i$. No need to consider any mixed derivatives. – Amitai Yuval Oct 18 '15 at 16:41
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To rephrase that, what we have proved is that under any change of coordinates with $S$ becoming ${x_n=0}$ the diagonal terms tend to 0, so just change coordinates and any vector will yield something tending to 0. Right? – MickG Oct 18 '15 at 16:43
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