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authorJaron Kent-Dobias <jaron@kent-dobias.com>2025-08-18 21:47:37 -0300
committerJaron Kent-Dobias <jaron@kent-dobias.com>2025-08-18 21:47:37 -0300
commit337c72cb0c71b50cad612327f292032ce8ff5933 (patch)
tree73fefbb1d608a4ad1abcc8491d37c1066d12b76c
downloadzif-337c72cb0c71b50cad612327f292032ce8ff5933.tar.gz
zif-337c72cb0c71b50cad612327f292032ce8ff5933.tar.bz2
zif-337c72cb0c71b50cad612327f292032ce8ff5933.zip
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+@article{Folena_2020_Rethinking,
+ author = {Folena, Giampaolo and Franz, Silvio and Ricci-Tersenghi, Federico},
+ title = {Rethinking Mean-Field Glassy Dynamics and Its Relation with the Energy Landscape: The Surprising Case of the Spherical Mixed $p$-Spin Model},
+ journal = {Physical Review X},
+ publisher = {American Physical Society},
+ year = {2020},
+ month = {8},
+ volume = {10},
+ pages = {031045},
+ url = {https://link.aps.org/doi/10.1103/PhysRevX.10.031045},
+ doi = {10.1103/PhysRevX.10.031045},
+ issue = {3},
+ numpages = {26}
+}
+
+@article{Folena_2021_Gradient,
+ author = {Folena, Giampaolo and Franz, Silvio and Ricci-Tersenghi, Federico},
+ title = {Gradient descent dynamics in the mixed $p$-spin spherical model: finite-size simulations and comparison with mean-field integration},
+ journal = {Journal of Statistical Mechanics: Theory and Experiment},
+ publisher = {IOP Publishing},
+ year = {2021},
+ month = {3},
+ number = {3},
+ volume = {2021},
+ pages = {033302},
+ url = {https://doi.org/10.1088%2F1742-5468%2Fabe29f},
+ doi = {10.1088/1742-5468/abe29f}
+}
+
+@article{Kent-Dobias_2023_How,
+ author = {Kent-Dobias, Jaron and Kurchan, Jorge},
+ title = {How to count in hierarchical landscapes: a full solution to mean-field complexity},
+ journal = {Physical Review E},
+ publisher = {American Physical Society (APS)},
+ year = {2023},
+ month = {6},
+ number = {6},
+ volume = {107},
+ pages = {064111},
+ url = {https://doi.org/10.1103/PhysRevE.107.064111},
+ doi = {10.1103/PhysRevE.107.064111}
+}
+
+@unpublished{Kent-Dobias_2024_Algorithm-independent,
+ author = {Kent-Dobias, Jaron},
+ title = {Algorithm-independent bounds on complex optimization through the statistics of marginal optima},
+ year = {2024},
+ month = {July},
+ url = {https://arxiv.org/abs/2407.02092},
+ note = {arXiv preprint},
+ archiveprefix = {arXiv},
+ eprint = {2407.02092},
+ primaryclass = {cond-mat.dis-nn}
+}
+
+@article{Kent-Dobias_2024_Conditioning,
+ author = {Kent-Dobias, Jaron},
+ title = {Conditioning the complexity of random landscapes on marginal optima},
+ journal = {Physical Review E},
+ publisher = {American Physical Society (APS)},
+ year = {2024},
+ month = {December},
+ number = {6},
+ volume = {110},
+ pages = {064148},
+ url = {http://dx.doi.org/10.1103/PhysRevE.110.064148},
+ doi = {10.1103/physreve.110.064148},
+ issn = {2470-0053}
+}
+
+@article{Kent-Dobias_2025_On,
+ author = {Kent-Dobias, Jaron},
+ title = {On the topology of solutions to random continuous constraint satisfaction problems},
+ journal = {SciPost Physics},
+ publisher = {Stichting SciPost},
+ year = {2025},
+ month = {May},
+ number = {5},
+ volume = {18},
+ pages = {158},
+ url = {http://dx.doi.org/10.21468/SciPostPhys.18.5.158},
+ doi = {10.21468/scipostphys.18.5.158},
+ issn = {2542-4653}
+}
+
diff --git a/zif.tex b/zif.tex
new file mode 100644
index 0000000..323c6c0
--- /dev/null
+++ b/zif.tex
@@ -0,0 +1,601 @@
+\documentclass[aspectratio=169,dvipsnames]{beamer}
+
+\setbeamerfont{title}{family=\bf}
+\setbeamerfont{frametitle}{family=\bf}
+\setbeamerfont{normal text}{family=\rm}
+\setbeamertemplate{navigation symbols}{}
+\setbeamercolor{titlelike}{parent=structure,fg=cyan}
+
+\usepackage{enumitem}
+\usepackage[utf8]{inputenc}
+\usepackage[T1]{fontenc}
+\usepackage{pifont}
+\usepackage{graphicx}
+\usepackage{xcolor}
+\usepackage{tikz}
+
+\definecolor{ictpblue}{HTML}{0471b9}
+\definecolor{ictpgreen}{HTML}{0c8636}
+
+\definecolor{mb}{HTML}{5e81b5}
+\definecolor{my}{HTML}{e19c24}
+\definecolor{mg}{HTML}{8fb032}
+\definecolor{mr}{HTML}{eb6235}
+
+\setbeamercolor{titlelike}{parent=structure,fg=ictpblue}
+\setbeamercolor{itemize item}{fg=ictpblue}
+
+\usepackage[
+ style=phys,
+ eprint=true,
+ maxnames = 100,
+ terseinits=true
+]{biblatex}
+
+
+\addbibresource{zif.bib}
+
+\title{
+ Understanding the flat parts of random landscapes
+}
+\author{\textbf{Jaron Kent-Dobias}}
+\date{9 September 2025}
+
+\begin{document}
+
+\begin{frame}
+ \maketitle
+
+ \vspace{-8pc}
+ \begin{minipage}[c]{10pc}
+ \centering
+ \includegraphics[height=6pc]{figs/ift-unesp.png}
+
+ \vspace{2em}
+
+ \includegraphics[height=1.5pc]{figs/Simons-Foundation-Logo_blue.png}
+ \end{minipage}
+ \hfill\begin{minipage}[c]{10pc}
+ \centering
+ \includegraphics[height=6pc]{figs/logo-ictp-saifr.jpg}
+
+ \vspace{2em}
+
+ \includegraphics[height=1.5pc]{figs/fapesp.png}
+ \end{minipage}
+ \vspace{2pc}
+\end{frame}
+
+\begin{frame}
+ \frametitle{How to count: Kac--Rice}
+
+ Number of stationary points with $\nabla H(\pmb x)=0$ given by integral
+ over Kac--Rice measure
+ \begin{align*}
+ \#_\text{points}
+ &=\int_\Omega d\pmb x\,\delta\big(\nabla H(\pmb x)\big)\,\big|\det\operatorname{Hess}H(\pmb x)\big|
+ \end{align*}
+ Note absolute value of the determinant: want to account for curvature but not add $-1$
+
+ \bigskip
+
+ Can specify properties of points by inserting $\delta$-functions:
+ \begin{align*}
+ \#_\text{points}\alert<2>{(E)}
+ &=\int_\Omega d\pmb x\,\delta\big(\nabla H(\pmb x)\big)\,\big|\det\operatorname{Hess}H(\pmb x)\big|
+ \alert<2>{\,\delta\big(H(\pmb x)-NE\big)}
+ \end{align*}
+
+ \emph{How do we condition on marginal minima?}
+\end{frame}
+
+\begin{frame}
+ \frametitle{Conditioning on the type of minimum: the spherical models}
+ \begin{columns}
+ \begin{column}{0.5\textwidth}
+ In spherical spin glasses, all points have the same Hessian spectral density: semicircle with radius $\mu_\text m$, but different shifts
+ \[
+ \mu=\frac1N\operatorname{Tr}\operatorname{Hess} H(\pmb x)
+ \]
+
+ \bigskip
+
+ Condition on marginal minima by inserting
+ \[
+ \delta\big(\operatorname{Tr}\operatorname{Hess}H(\pmb x)-N\mu_\text{m}\big)
+ \]
+
+ \end{column}
+ \begin{column}{0.5\textwidth}
+ \begin{overlayarea}{\textwidth}{14.5em}
+ \only<1-2>{\includegraphics[width=\columnwidth]{figs/mu_0.75.pdf}\\\hphantom{ello}\includegraphics[width=0.8\columnwidth]{figs/land_0.75.pdf}}
+ \only<3>{\includegraphics[width=\columnwidth]{figs/mu_1.5.pdf}\\\hphantom{ello}\includegraphics[width=0.8\columnwidth]{figs/land_1.5.pdf}}
+ \only<4>{\includegraphics[width=\columnwidth]{figs/mu_2.25.pdf}\\\hphantom{ello}\includegraphics[width=0.8\columnwidth]{figs/land_2.25.pdf}}
+ \only<5>{\includegraphics[width=\columnwidth]{figs/mu_3.5.pdf}\\\hphantom{ello}\includegraphics[width=0.8\columnwidth]{figs/land_3.5.pdf}}
+ \only<6>{\includegraphics[width=\columnwidth]{figs/mu_2.pdf}\\\hphantom{ello}\includegraphics[width=0.8\columnwidth]{figs/land_2.pdf}}
+ \end{overlayarea}
+ \end{column}
+ \end{columns}
+\end{frame}
+
+\begin{frame}
+ \frametitle{Less simple mean-field models}
+ \framesubtitle{The mixed spherical spin glasses}
+
+ \begin{columns}
+ \begin{column}{0.41\textwidth}
+ \alert<1-2>{Pure: all procedures reach $E_\text{th}$}
+
+ \medskip
+
+ \alert<3-4>{Mixed: different procedures reach different energies}
+
+ \medskip
+
+ Dynamical endpoints bounded by complexity of marginal minima
+
+ \bigskip
+
+ \tiny
+ \fullcite{Folena_2020_Rethinking}
+
+ \smallskip
+
+ \fullcite{Folena_2021_Gradient}
+
+ \smallskip
+
+ \fullcite{Kent-Dobias_2023_How}
+ \end{column}
+ \begin{column}{0.59\textwidth}
+ \begin{overlayarea}{\columnwidth}{0.8\textheight}
+ \only<1>{%
+ \includegraphics[width=0.8\columnwidth]{figs/complexity_3_marginal.pdf}
+ }%
+ \only<2>{%
+ \bigskip
+
+ \begin{minipage}[b]{0.83\columnwidth}
+ \centering
+ \includegraphics[width=0.18\columnwidth]{figs/land_0.75.pdf}
+ \includegraphics[width=0.18\columnwidth]{figs/land_0.75.pdf}
+ \includegraphics[width=0.18\columnwidth]{figs/land_0.75.pdf}
+ \includegraphics[width=0.18\columnwidth]{figs/land_0.75.pdf}
+ \includegraphics[width=0.18\columnwidth]{figs/land_0.75.pdf}
+
+ \includegraphics[width=0.18\columnwidth]{figs/land_1.5.pdf}
+ \includegraphics[width=0.18\columnwidth]{figs/land_1.5.pdf}
+ \includegraphics[width=0.18\columnwidth]{figs/land_1.5.pdf}
+ \includegraphics[width=0.18\columnwidth]{figs/land_1.5.pdf}
+
+ \includegraphics[width=0.18\columnwidth]{figs/land_2.pdf}
+ \includegraphics[width=0.18\columnwidth]{figs/land_2.pdf}
+ \includegraphics[width=0.18\columnwidth]{figs/land_2.pdf}
+
+ \includegraphics[width=0.18\columnwidth]{figs/land_2.25.pdf}
+ \includegraphics[width=0.18\columnwidth]{figs/land_2.25.pdf}
+
+ \includegraphics[width=0.18\columnwidth]{figs/land_3.5.pdf}
+ \end{minipage}
+ \raisebox{0.75em}{\includegraphics[width=0.15\columnwidth]{figs/energy_arrow.pdf}}
+ }
+ \only<3>{%
+ \includegraphics[width=0.8\columnwidth]{figs/complexity_34_marginal.pdf}
+ }%
+ \only<4>{%
+ \bigskip
+
+ \begin{minipage}[b]{0.83\columnwidth}
+ \centering
+ \includegraphics[width=0.18\columnwidth]{figs/land_0.75.pdf}
+ \includegraphics[width=0.18\columnwidth]{figs/land_0.75.pdf}
+ \includegraphics[width=0.18\columnwidth]{figs/land_0.75.pdf}
+ \includegraphics[width=0.18\columnwidth]{figs/land_1.5.pdf}
+ \includegraphics[width=0.18\columnwidth]{figs/land_1.5.pdf}
+
+ \includegraphics[width=0.18\columnwidth]{figs/land_0.75.pdf}
+ \includegraphics[width=0.18\columnwidth]{figs/land_1.5.pdf}
+ \includegraphics[width=0.18\columnwidth]{figs/land_1.5.pdf}
+ \includegraphics[width=0.18\columnwidth]{figs/land_2.pdf}
+
+ \includegraphics[width=0.18\columnwidth]{figs/land_1.5.pdf}
+ \includegraphics[width=0.18\columnwidth]{figs/land_2.pdf}
+ \includegraphics[width=0.18\columnwidth]{figs/land_2.25.pdf}
+
+ \includegraphics[width=0.18\columnwidth]{figs/land_2.25.pdf}
+ \includegraphics[width=0.18\columnwidth]{figs/land_3.5.pdf}
+
+ \includegraphics[width=0.18\columnwidth]{figs/land_2.25.pdf}
+ \end{minipage}
+ \raisebox{0.75em}{\includegraphics[width=0.15\columnwidth]{figs/energy_arrow_o1.pdf}}
+ }%
+ \end{overlayarea}
+ \end{column}
+ \end{columns}
+\end{frame}
+
+\begin{frame}
+ \frametitle{Marginal minima in generic models}
+ \begin{columns}
+ \begin{column}{0.5\textwidth}
+ \alert<1>{In spherical spin glasses, all points have the same Hessian spectral density: semicircle with radius $\mu_\text m$, but different shifts}
+
+ \bigskip
+
+ In generic models, spectral density depends on stationarity, energy, etc!
+
+ \bigskip
+
+ \alert<2>{Example: multi-spherical model
+ \[
+ H(\{\boldsymbol s_1,\boldsymbol s_2\})
+ =H_p^{(1)}(\boldsymbol s_1)+H_p^{(2)}(\boldsymbol s_2)+\epsilon\boldsymbol s_1\cdot\boldsymbol s_2
+ \]}
+ \hspace{-0.25em}In most models we don't understand the Hessian at all
+ \end{column}
+ \begin{column}{0.5\textwidth}
+ \begin{overprint}
+ \onslide<1>\includegraphics[width=\columnwidth]{figs/mu_0.75.pdf}
+ \onslide<2>\includegraphics[width=0.9\columnwidth]{figs/msg_marg_spectra.pdf}
+ \end{overprint}
+ \end{column}
+ \end{columns}
+\end{frame}
+
+\begin{frame}
+ \frametitle{Towards generic marginal complexity}
+ \begin{columns}
+ \begin{column}{0.6\textwidth}
+ \begin{itemize}[leftmargin=4em]
+ \item[\color{ictpgreen}\bf Trick \#1:] condition stationary points on \emph{value of smallest eigenvalue}
+ \end{itemize}
+ {
+ \small
+ \begin{align*}
+ \hspace{-1em}&\delta(\lambda_\text{min}(A)) \\
+ &=\lim_{\beta\to\infty}\int
+ \frac{d\pmb s\,\delta(N-\|\pmb s\|^2)e^{-\beta\pmb s^TA\pmb s}}
+ {\int d\pmb s'\,\delta(N-\|\pmb s'\|^2)e^{-\beta\pmb s'^TA\pmb s'}}
+ \delta\left(\frac{\pmb s^TA\pmb s}N\right)
+ \end{align*}
+ }
+
+ \bigskip
+
+ Only works if you happen to have the correct shift $\mu$
+
+ \bigskip
+
+ \tiny
+ \fullcite{Kent-Dobias_2024_Conditioning}
+
+ \end{column}
+ \begin{column}{0.4\textwidth}
+ \begin{overprint}
+ \onslide<1>\includegraphics[width=\textwidth]{figs/spectrum_less.pdf}
+ \onslide<2>\includegraphics[width=\textwidth]{figs/spectrum_more.pdf}
+ \onslide<3>\includegraphics[width=\textwidth]{figs/spectrum_eq.pdf}
+ \end{overprint}
+ \end{column}
+ \end{columns}
+\end{frame}
+
+
+\begin{frame}
+ \frametitle{Towards generic marginal complexity}
+ \begin{columns}
+ \begin{column}{0.5\textwidth}
+ \begin{itemize}[leftmargin=4em]
+ \item[\color{ictpgreen}\bf Trick \#1:] condition stationary points on \emph{value of smallest eigenvalue}
+ \end{itemize}
+ {
+ \small
+ \begin{align*}
+ \hspace{-3em}&\delta(\lambda_\text{min}(A)) \\
+ \hspace{-3em}&=\lim_{\beta\to\infty}\int
+ \frac{d\pmb s\,\delta(N-\|\pmb s\|^2)e^{-\beta\pmb s^TA\pmb s}}
+ {\int d\pmb s'\,\delta(N-\|\pmb s'\|^2)e^{-\beta\pmb s'^TA\pmb s'}}
+ \delta\left(\frac{\pmb s^TA\pmb s}N\right)
+ \end{align*}
+ }
+
+ \medskip
+
+ \begin{itemize}[leftmargin=4em]
+ \item[\color{ictpgreen}\bf Trick \#2:] adjust $\mu\propto\operatorname{Tr}\operatorname{Hess}H$ until order-$N$ large deviation breaks
+ \end{itemize}
+
+ \bigskip
+
+ \tiny
+ \fullcite{Kent-Dobias_2024_Conditioning}
+
+ \end{column}
+ \begin{column}{0.5\textwidth}
+ \hspace{0.9em}
+ \includegraphics[scale=0.8]{figs/spectrum_less.pdf}
+ \hspace{-1.6em}
+ \includegraphics[scale=0.8]{figs/spectrum_eq.pdf}
+ \hspace{-1.6em}
+ \includegraphics[scale=0.8]{figs/spectrum_more.pdf}
+ \\
+ \includegraphics[scale=0.8]{figs/large_deviation.pdf}
+
+ \vspace{-1em}
+
+ \small
+ \begin{align*}
+ \hspace{-0.5em}G_0(\mu)=\frac 1N\log\Big\langle\delta\big(\lambda_\text{min}(A-\lambda\mu)\big)\Big\rangle_{A\in\text{GOE}(N)}
+ \end{align*}
+ \end{column}
+ \end{columns}
+\end{frame}
+
+\begin{frame}
+ \frametitle{Marginal complexity: example}
+ \begin{columns}
+ \begin{column}{0.5\textwidth}
+ Example: model of nonlinear least squares:
+ \[
+ H(\pmb s)=\frac12\sum_{i=1}^{M}V_i(\pmb s)^2
+ \]
+ for spherical $\pmb s\in\mathbb R^N$ and $M=\alpha N$ inhomogeneous random functions $V_i$
+ \[
+ V_i(\boldsymbol s)=H_2^{(i)}(\boldsymbol s)+H_3^{(i)}(\boldsymbol s)
+ \]
+
+ \bigskip
+
+ \tiny
+ \fullcite{Kent-Dobias_2024_Conditioning}
+
+ \smallskip
+
+ \fullcite{Kent-Dobias_2024_Algorithm-independent}
+ \end{column}
+ \begin{column}{0.5\textwidth}
+ \begin{overprint}
+ \onslide<1>\vspace{4em}\includegraphics[width=\textwidth]{figs/most_squares_complex.pdf}
+ \onslide<2>\vspace{-1.75em}\includegraphics[width=\textwidth]{figs/most_squares_complexity.pdf}
+
+ \vspace{-1.95em}
+
+ \hspace{-0.25em}\colorbox{white}{\includegraphics[width=\textwidth]{figs/most_squares_stability.pdf}}
+ \end{overprint}
+ \end{column}
+ \end{columns}
+\end{frame}
+
+\begin{frame}
+ \frametitle{The Euler characteristic \boldmath{$\chi$}}
+ \begin{columns}
+ \begin{column}{0.5\textwidth}
+ The Euler characteristic $\chi(\Omega)$ is a topological invariant of a manifold $\Omega$
+
+ \medskip
+
+ Defined by tiling the manifold, then taking the alternating sum
+ \begin{align*}
+ \chi(\Omega_{\text{cow}})
+ &=
+ {\only<2,5->{\color{Red}}\#_\text{vertices}}
+ &&\hspace{-1em}-
+ {\only<3,5->{\color{ictpgreen}}\#_\text{edges}}
+ &&\hspace{-1em}+
+ {\only<4,5->{\color{ictpblue}}\#_\text{faces}}
+ \\
+ &\color{White}\only<2->{\color{Black}}=
+ {\only<2,5->{\color{Red}}2904}
+ &&\hspace{-1em}\color{White}\only<3->{\color{Black}}-
+ {\only<3,5->{\color{ictpgreen}}8706}
+ &&\hspace{-1em}\color{White}\only<4->{\color{Black}}+
+ {\only<4,5->{\color{ictpblue}}5804} \\
+ &\color{White}\only<5->{\color{Black}}=2
+ \end{align*}
+ \[
+ \color{White}\only<6->{\color{Black}}\chi(\Omega_\text{football})
+ ={\only<6->{\color{Red}}60}-{\only<6->{\color{ictpgreen}}90}+{\only<6->{\color{ictpblue}}32}=2
+ \]
+
+ \color{White}\only<7>{\color{Black}}Cow is homeomorphic to a sphere
+ \end{column}
+ \begin{column}{0.5\textwidth}
+ \begin{overprint}
+ \onslide<1,5>\includegraphics[width=\textwidth]{figs/cow.png}
+ \onslide<2>\includegraphics[width=\textwidth]{figs/cow_vert.png}
+ \onslide<3>\includegraphics[width=\textwidth]{figs/cow_edge.png}
+ \onslide<4>\includegraphics[width=\textwidth]{figs/cow_face.png}
+ \onslide<6->\hspace{2em}\includegraphics{figs/Football_Pallo_valmiina-cropped.jpg}
+ \end{overprint}
+ \end{column}
+ \end{columns}
+\end{frame}
+
+\begin{frame}
+ \frametitle{The Euler characteristic \boldmath{$\chi$}}
+ \framesubtitle{Characteristics of the characteristic}
+
+ \begin{columns}
+ \begin{column}{0.5\textwidth}
+ For closed, connected 2-dimensional manifolds, related to genus $g$ by
+ $\chi=2-2g$
+
+ \medskip
+
+ General properties:
+ \vspace{-0.5em}
+ \[
+ \chi(\Omega)=0 \text{ for odd-dimensional $\Omega$}
+ \]
+ \vspace{-1.6em}
+ \[
+ \chi(S^D)=2\text{ for even }D
+ \]
+ \[
+ \chi(\Omega_1\sqcup\Omega_2)=\chi(\Omega_1)+\chi(\Omega_2)
+ \]
+ \[
+ \chi(\Omega_1\times\Omega_2)=\chi(\Omega_1)\times\chi(\Omega_2)
+ \]
+
+ \smallskip
+
+ Examples:
+ \vspace{-0.5em}
+ \[\chi(M\text{ even-$D$ spheres})=2M\]
+ \vspace{-1.6em}
+ \[\chi(S^1\times\text{anything})=0\]
+ \end{column}
+ \begin{column}{0.5\textwidth}
+ \includegraphics[width=\textwidth]{figs/genus.png}
+ \end{column}
+ \end{columns}
+\end{frame}
+
+\begin{frame}
+ \frametitle{The Euler characteristic \boldmath{$\chi$}}
+ \framesubtitle{Computing the Euler characteristic}
+ \begin{columns}
+ \begin{column}{0.5\textwidth}
+ Morse theory: gradient flow on an arbitrary ``height'' function $h$ makes a complex
+ \begin{align*}
+ \chi(\Omega)
+ &=
+ {\only<2,5>{\color{Red}}\#_\text{vertices}}
+ -
+ {\only<3,5>{\color{ictpgreen}}\#_\text{edges}}
+ +
+ {\only<4,5>{\color{ictpblue}}\#_\text{faces}}
+ +\cdots \\
+ &=
+ {\only<6>{\color{ictpblue}}\#_\text{index 0}}
+ -
+ {\only<6>{\color{ictpgreen}}\#_\text{index 1}}
+ +
+ {\only<6>{\color{Red}}\#_\text{index 2}}
+ +\cdots \\
+ &=\sum_{i=0}^D(-1)^i\#_\text{index i}
+ \end{align*}
+ \end{column}
+ \begin{column}{0.5\textwidth}
+ \begin{overprint}
+ \onslide<1>\includegraphics[width=\textwidth]{figs/other_sphere.png}
+ \onslide<2>\includegraphics[width=\textwidth]{figs/other_sphere_vert.png}
+ \onslide<3>\includegraphics[width=\textwidth]{figs/other_sphere_edge.png}
+ \onslide<4>\includegraphics[width=\textwidth]{figs/other_sphere_face.png}
+ \onslide<5>\includegraphics[width=\textwidth]{figs/other_sphere_all.png}
+ \onslide<6>\includegraphics[width=\textwidth]{figs/other_sphere_crit.png}
+ \end{overprint}
+ \end{column}
+ \end{columns}
+\end{frame}
+
+\begin{frame}
+ \frametitle{The Euler characteristic \boldmath{$\chi$}}
+ \framesubtitle{Computing the Euler characteristic}
+
+ \begin{columns}
+ \begin{column}{0.6\textwidth}
+ \[
+ \Omega=\left\{
+ \pmb a\in\mathbb R^N\mid\|\pmb a\|^2=N, V_0=\hat f(J^i\mid\pmb a)\;\forall\;1\leq i\leq M
+ \right\}
+ \]
+ Pick whatever height function $h:\Omega\to\mathbb R$ you like: $h(\pmb a)=\frac1N\pmb
+ a_0\cdot\pmb a$ for arbitrary $\pmb a_0$
+ \[
+ \#_{\substack{\text{critical}\\\text{points}}}
+ =\int_\Omega d\pmb x\,
+ \delta\big(\nabla h(\pmb x)\big)\,
+ \big|\det\operatorname{Hess}h(\pmb x)\big|
+ \]
+ \[
+ \hspace{-1em}\operatorname{sgn}\big(\det\operatorname{Hess}(\pmb x)\big)
+ =
+ \operatorname{sgn}\left(\prod_{i=1}^D\lambda_i\right)
+ =(-1)^{\text{index}}
+ \]
+ \[
+ \chi(\Omega)
+ =\sum_{i=0}^D(-1)^i\#_\text{index i}
+ =\int_\Omega d\pmb x\,\delta\big(\nabla h(\pmb x)\big)\,\det\operatorname{Hess}h(\pmb x)
+ \]
+ \end{column}
+ \begin{column}{0.4\textwidth}
+ \begin{overprint}
+ \onslide<1>\includegraphics[width=\textwidth]{figs/function_1.png}
+ \onslide<2>\includegraphics[width=\textwidth]{figs/function_2.png}
+ \onslide<3>\includegraphics[width=\textwidth]{figs/function_3.png}
+ \end{overprint}
+ \end{column}
+ \end{columns}
+\end{frame}
+
+\begin{frame}
+ \frametitle{A simple model of nonlinear least squares}
+ \framesubtitle{Results}
+ \begin{columns}
+ \begin{column}{0.5\textwidth}
+ $M$ data points, $N$ parameters, $\alpha=M/N$
+ \[
+ V_0=\hat f(J\mid \pmb a)
+ =\sum_{i_1=1}^N\cdots\sum_{i_p=1}^NJ_{i_1,\ldots,i_p}a_{i_1}\cdots a_{i_p}
+ \]
+ Simplest case: $p=1$, $\Omega$ is intersection of random hyperplanes with the sphere
+
+ \medskip
+
+ Results in $\chi(\Omega)=2$ or $\chi(\Omega)=0$ depending on whether solutions exist
+
+ \medskip
+
+ \hspace{2em}\includegraphics[width=0.33\textwidth]{figs/connected.pdf}
+ \hfill
+ \includegraphics[width=0.33\textwidth]{figs/gone.pdf}\hspace{2em}
+ \end{column}
+ \begin{column}{0.5\textwidth}
+ \begin{overprint}
+ \onslide<1>\includegraphics[width=\textwidth]{figs/intersections_1.pdf}
+ \onslide<2>\includegraphics[width=\textwidth]{figs/intersections_2.pdf}
+ \onslide<3>\includegraphics[width=\textwidth]{figs/intersections_3.pdf}
+ \onslide<4>\includegraphics[width=\textwidth]{figs/intersections_4.pdf}
+ \onslide<5>\includegraphics[width=\textwidth]{figs/phases_1.pdf}
+ \end{overprint}
+ \end{column}
+ \end{columns}
+\end{frame}
+
+\begin{frame}
+ \frametitle{A simple model of nonlinear least squares}
+ \framesubtitle{Results}
+ \begin{columns}
+ \begin{column}{0.5\textwidth}
+ $M$ data points, $N$ parameters, $\alpha=M/N$
+ \[
+ V_0=\hat f(J\mid \pmb a)
+ =\sum_{i_1=1}^N\cdots\sum_{i_p=1}^NJ_{i_1,\ldots,i_p}a_{i_1}\cdots a_{i_p}
+ \]
+
+ For $p\geq2$, different phases with $|\chi(\Omega)|\gg1$ with varying sign
+
+ \medskip
+
+ \includegraphics[width=0.23\textwidth]{figs/middle.pdf}
+ \includegraphics[width=0.23\textwidth]{figs/complex.pdf}
+ \includegraphics[width=0.23\textwidth]{figs/shattered.pdf}
+ \includegraphics[width=0.23\textwidth]{figs/gone.pdf}
+ \end{column}
+ \begin{column}{0.5\textwidth}
+ \begin{overprint}
+ \onslide<1>\centering\includegraphics[width=0.8\textwidth]{figs/middle.pdf}\\$\chi(\Omega)\ll0$
+ \onslide<2>\centering\includegraphics[width=0.8\textwidth]{figs/complex.pdf}\\$\chi(\Omega)\ll0$
+ \onslide<3>\centering\includegraphics[width=0.8\textwidth]{figs/shattered.pdf}\\$\chi(\Omega)\gg0$
+ \onslide<4>\includegraphics[width=\textwidth]{figs/phases_2.pdf}
+ \onslide<5>\includegraphics[width=\textwidth]{figs/phases_3.pdf}
+ \onslide<6>\includegraphics[width=\textwidth]{figs/phases_4.pdf}
+ \end{overprint}
+ \end{column}
+ \end{columns}
+\end{frame}
+
+\end{document}