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\title{
  Structural barriers to random optimization
}
\author{\textbf{Jaron Kent-Dobias}\\Simons--FAPESP Young Investigator}
\date{19 February 2025}

\begin{document}

\begin{frame}
  \maketitle

  \vspace{-6pc}
  \begin{minipage}[c]{10pc}
    \centering
    \includegraphics[height=6pc]{figs/ift-unesp.png}
  \end{minipage}
  \hfill\begin{minipage}[c]{10pc}
    \centering
    \includegraphics[height=6pc]{figs/logo-ictp-saifr.jpg}
  \end{minipage}
  \vspace{2pc}
\end{frame}

\begin{frame}
  \frametitle{Overview}

  \begin{columns}
    \begin{column}{\textwidth}

      \Large\color{ictpblue}{\textbf{Introduction and background of existing work}}
      \large

      \medskip

      \hspace{1em}\color{ictpgreen}{\textbf{Introduction to random optimization problems}}

      \bigskip

      \hspace{1em}\color{ictpgreen}{\textbf{Worst-case versus typical-case performance}}

      \bigskip

      \hspace{1em}\color{ictpgreen}{\textbf{Structural barriers to typical-case performance of the best algorithms}}

      \bigskip

      \Large\color{ictpblue}{\textbf{Original contributions}}
      \large

      \medskip

      \hspace{1em}\color{ictpgreen}{\textbf{Structural barriers to performance of common algorithms}}

      \bigskip

      \hspace{1em}\color{ictpgreen}{\textbf{Structural barriers to performance of the most common algorithm}}

      \bigskip

      \hspace{1em}\color{ictpgreen}{\textbf{Connections to other problems}}

    \end{column}
  \end{columns}
\end{frame}

\begin{frame}
  \frametitle{Complexity of random landscapes}
  \begin{columns}
    \begin{column}{0.4\textwidth}
      Complexity $\Sigma=\frac1N\overline{\log\#_\text{points}}$ describes typical number of stationary points

      \bigskip

      Complexity of marginal minima crucial for understanding dynamics in mixed $p$-spin models

      \bigskip

      Lucky accident: natural parameter
      sets type of stationary point
    \end{column}
    \begin{column}{0.6\textwidth}
      \begin{overprint}
        \onslide<1>\includegraphics[width=\textwidth]{figs/folena_2020.png}
        \onslide<2>\includegraphics[width=\textwidth]{figs/folena_2020_2.png}
      \end{overprint}

        \smallskip

        \tiny\fullcite{Folena_2020_Rethinking}
    \end{column}
  \end{columns}
\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*}
      How can \emph{marginality} be specified?
\end{frame}

\begin{frame}
  \frametitle{Hessian shifts and stationary point stability}
  \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
      \[
        \delta\big(\operatorname{Tr}\operatorname{Hess}H(\pmb x)-N\mu_\text{m}\big)
      \]

      \medskip

      \alert<7>{In generic models, spectral density depends on stationarity, energy, etc!}
    \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}}
        \only<7>{\includegraphics[width=0.9\columnwidth]{figs/msg_marg_spectra.pdf}}
      \end{overlayarea}
    \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: non-Gaussian landscapes
      \[
        H(\pmb x)=\frac12\sum_{i=1}^{\alpha N}V_i(\pmb x)^2
      \]
      for spherical $\pmb x$ and Gaussian functions $V_i$
      \[
        \overline{V_i(\pmb x)V_j(\pmb x')}=\delta_{ij}f\bigg(\frac{\pmb x\cdot\pmb x'}N\bigg)
      \]

      \vspace{-2em}

      \begin{overprint}
        \onslide<1-2>\[
          f(q)=\tfrac12q^2+\tfrac12q^3
        \]
        \onslide<3>\[
          f(q)=\kappa q+(1-\kappa)q^2
        \]
      \end{overprint}

      \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}}
        \onslide<3>\vspace{-1em}

        \includegraphics[width=\textwidth]{figs/most_squares_nonzoom.pdf}

        \vspace{-0.4em}

        \includegraphics[width=\textwidth]{figs/most_squares_zoom.pdf}

        \vspace{1em}
      \end{overprint}
    \end{column}
  \end{columns}
\end{frame}

\begin{frame}
  \frametitle{Which marginal minima attract the dynamics?}
  \begin{columns}
    \begin{column}{0.5\textwidth}
      `Best case' performance: lowest marginal minima without the \emph{Overlap Gap Property}

      \smallskip

      \tiny
      \fullcite{Gamarnik_2021-10_The}

      \normalsize
      \medskip

      `Worst case' performance: ???

      \smallskip
      \tiny\fullcite{Folena_2023_On}

      \normalsize
      \medskip

      \textcolor{mb}{\textbf{\boldmath{$E_\text{gs}$:}} ground state, energy of lowest minima}

      \smallskip

      \textcolor{mg}{\textbf{\boldmath{$E_\text{alg}$:}} algorithmic bound, set by OGP}

      \smallskip

      \textcolor{my}{\textbf{\boldmath{$E_\text{th}$:}} `threshold', marginal minima dominate}

      \medskip

      Gradient descent destination depends on \emph{global} property: basin of attraction size;
      stationary point analysis is only \emph{local}

      \medskip

    \end{column}
    \begin{column}{0.5\textwidth}
      \begin{overprint}
        \onslide<1>\includegraphics[width=\textwidth]{figs/folena_2023.png}
        \onslide<2>\includegraphics[width=\textwidth]{figs/folena_new_2.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{Characteristics of the Euler 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}
  \begin{columns}
    \begin{column}{0.16\textwidth}
      \Large
      \textbf{\color{ictpblue}\boldmath{$\chi$} for constant energy level sets}
      \vspace{11em}
    \end{column}
    \begin{column}{0.7\textwidth}
      \begin{overprint}
        \onslide<1>\centering\rotatebox{90}{\includegraphics[height=\textwidth]{figs/Stillinger-0.png}}
        \onslide<2>\centering\rotatebox{90}{\includegraphics[height=\textwidth]{figs/Stillinger-1.png}}
        \onslide<3>\centering\rotatebox{90}{\includegraphics[height=\textwidth]{figs/Stillinger-2.png}}
      \end{overprint}
    \end{column}
    \begin{column}{0.16\textwidth}
      \begin{overprint}
        \onslide<2>\centering High energy

        \vspace{0.5em}

        $\chi(\Omega)\ll0$

        \vspace{0.5em}

        hole\\
        dominated
        \onslide<3>\centering Low energy

        \vspace{0.5em}

        $\chi(\Omega)\gg0$

        \vspace{0.5em}

        component\\
        dominated
      \end{overprint}
      \vspace{15em}
    \end{column}
  \end{columns}
\end{frame}

\begin{frame}
  \frametitle{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*}
      \[
        \hspace{-1em}\operatorname{sgn}\big(\det\operatorname{Hess}(\pmb x)\big)
        =
        \operatorname{sgn}\left(\prod_{i=1}^D\lambda_i\right)
        =(-1)^{\text{index}}
      \]
      \[
        \chi(\Omega)
        =\int_\Omega d\pmb x\,\delta\big(\nabla h(\pmb x)\big)
        \,\det\operatorname{Hess}h(\pmb x)
      \]
      \emph{Kac--Rice without the absolute value!}
    \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{Computing the Euler characteristic of level sets}

  \begin{columns}
    \begin{column}{0.6\textwidth}
      Pick whatever height function $h:\Omega\to\mathbb R$ you like: $h(\pmb x)=\frac1N\pmb
      x_0\cdot\pmb x$ for arbitrary $\pmb x_0$.
      \[
        \chi(\Omega)
        =\int_\Omega d\pmb x\,\delta\big(\nabla h(\pmb x)\big)\,\det\operatorname{Hess}h(\pmb x)
      \]
      Level set $\Omega$ defined by $H(\pmb x)=EN$ and $\|\pmb x\|^2=N$

      \bigskip

      Lagrange multipliers replace differential geometry:
      \[
        L(\pmb x,\pmb\omega)=h(\pmb x)+\omega_0(\|\pmb x\|^2-N)+\omega_1(H(\pmb x)-EN)
      \]
    \end{column}
    \begin{column}{0.4\textwidth}
      \begin{overprint}
        \onslide<1>\includegraphics[width=\textwidth]{figs/function-0.png}
        \onslide<2>\includegraphics[width=\textwidth]{figs/function-1.png}
        \onslide<3>\includegraphics[width=\textwidth]{figs/function-2.png}
      \end{overprint}
    \end{column}
  \end{columns}
      \[
        \chi(\Omega)
        =\int_{\mathbb R^{N+2}} d\pmb x\,d\pmb\omega\,\delta\big(\begin{bmatrix}\frac{\partial L}{\partial\pmb x}&\frac{\partial L}{\partial\pmb\omega}\end{bmatrix}\big)
        \,\det\begin{bmatrix}\frac{\partial^2L}{\partial\pmb x^2}&\frac{\partial^2L}{\partial\pmb x\partial\pmb\omega}\\\frac{\partial^2L}{\partial\pmb x\partial\pmb\omega}&\frac{\partial^2L}{\partial\pmb\omega^2}\end{bmatrix}
      \]
\end{frame}

\begin{frame}
  \begin{columns}
    \begin{column}{\textwidth}
      \includegraphics[width=\textwidth]{figs/slice.png}
    \end{column}
  \end{columns}
\end{frame}

\begin{frame}
  \frametitle{Results: \boldmath{$3+s$} mixed spherical models}
  \begin{columns}
    \begin{column}{0.5\textwidth}
      \begin{align*}
        H(\pmb x)=\lambda_s\sum_{i_1,i_2,i_3}^NJ_{i_1,i_2,i_3}x_{i_1}x_{i_2}x_{i_3} \hspace{4em} \\
        +(1-\lambda_s)\sum_{i_1,\ldots,i_s}^NJ_{i_1,\ldots,i_s}x_{i_1}\cdots x_{i_s}
      \end{align*}

      \textcolor{mb}{\textbf{\boldmath{$E_\text{gs}$:}} ground state, energy of lowest minima}

      \smallskip

      \textcolor{mg}{\textbf{\boldmath{$E_\text{alg}$:}} algorithmic bound, set by OGP}

      \smallskip

      \textcolor{my}{\textbf{\boldmath{$E_\text{th}$:}} `threshold', marginal minima dominate}

      \smallskip

      \textcolor{mr}{\textbf{\boldmath{$E_\text{sh}$:}} `shattering', $\chi$ changes sign}

      \bigskip

      \tiny
      \fullcite{Kent-Dobias_2024_On}
    \end{column}
    \begin{column}{0.5\textwidth}
      \includegraphics[width=\textwidth]{figs/folena_new.pdf}
    \end{column}
  \end{columns}
\end{frame}

\begin{frame}
  \frametitle{Preliminary results: other models?}
  \begin{columns}
    \begin{column}{0.5\textwidth}
      Example: non-Gaussian landscapes
      \[
        H(\pmb x)=\frac12\sum_{i=1}^{\alpha N}V_i(\pmb x)^2
      \]
      for spherical $\pmb x$ and Gaussian functions $V_i$

      \medskip

      $E_\text{sh}$ consistent with gradient descent? More work needed...

      \bigskip\tiny

      \fullcite{Kent-Dobias_2024_Algorithm-independent}

      \smallskip

      \fullcite{Kent-Dobias_2024_On}
    \end{column}
    \begin{column}{0.5\textwidth}
        \vspace{-1em}

        \begin{overprint}
          \onslide<1>\includegraphics[width=\textwidth]{figs/most_squares_nonzoom.pdf}
          \onslide<2>\includegraphics[width=\textwidth]{figs/extrapolation.pdf}
        \end{overprint}

        \vspace{-0.4em}

        \includegraphics[width=\textwidth]{figs/most_squares_zoom_2.pdf}

        \vspace{1em}
    \end{column}
  \end{columns}
\end{frame}

\begin{frame}
  \frametitle{Outlook, other applications, future directions}
  \begin{columns}
    \begin{column}{0.5\textwidth}
      Euler characteristic reveals structure of problems with no energy function:\\ e.g., the set of $\pmb x$ such that
      \[
        V_i(\pmb x)=\sqrt NV_0 \qquad i=1,\ldots,\alpha N
      \]
      for independent Gaussian $V_i$

      \medskip

      \tiny
      \fullcite{Kent-Dobias_2024_On}

      \bigskip\normalsize

      \textcolor{ictpgreen}{\textbf{To Do:}}

      Resolve GD question: better DMFT, direct reasoning for relationship to topology

      \medskip

      Extend topological arguments beyond GD
    \end{column}
    \begin{column}{0.5\textwidth}
      \includegraphics[width=\textwidth]{figs/spheres.png}

      \medskip

      \includegraphics[width=\textwidth]{figs/phases.png}
    \end{column}
  \end{columns}
\end{frame}

\end{document}
