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authorkurchan.jorge <kurchan.jorge@gmail.com>2020-12-30 11:03:54 +0000
committeroverleaf <overleaf@localhost>2020-12-30 12:23:30 +0000
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Spin-glasses have long been considered the paradigm of many variable `complex
landscapes,' a subject that includes neural networks and optimization problems,
-most notably constraint satisfaction. The most tractable family of these
+most notably constraint satisfaction \cite{mezard2009information}. The most tractable family of these
are the mean-field spherical $p$-spin models \cite{Crisanti_1992_The} (for a
review see \cite{Castellani_2005_Spin-glass}) defined by the energy
\begin{equation} \label{eq:bare.hamiltonian}
@@ -447,8 +447,8 @@ dynamics, are a problem we hope to address in future work.
This paper provides a first step towards the study of a complex landscape with
complex variables. The next obvious one is to study the topology of the
- critical points, their basins of attraction following gradient ascent (the
- Lefschetz thimbles), and descent (the anti-thimbles) \cite{Witten_2010_A,
+ critical points, the sets reached following gradient descent (the
+ Lefschetz thimbles), and ascent (the anti-thimbles) \cite{Witten_2010_A,
Witten_2011_Analytic, Cristoforetti_2012_New, Behtash_2017_Toward,
Scorzato_2016_The}, which act as constant-phase integrating `contours.'
Locating and counting the saddles that are joined by gradient lines---the