From 608c0e40c26f34204aaa101a1a6d0ab4c19f6a0b Mon Sep 17 00:00:00 2001
From: Jaron Kent-Dobias <jaron@kent-dobias.com>
Date: Mon, 10 Mar 2025 16:15:22 -0300
Subject: Changes addressing report #3, weakness #1.

Added a paragraph to the text discussing the content of previous work on
this model.
---
 topology.tex | 14 ++++++++++++++
 1 file changed, 14 insertions(+)

(limited to 'topology.tex')

diff --git a/topology.tex b/topology.tex
index 56af0c6..32f1a6a 100644
--- a/topology.tex
+++ b/topology.tex
@@ -171,6 +171,20 @@ studied properties of the cost function
   \mathscr C(\mathbf x)=\frac12\sum_{k=1}^M\big[V_k(\mathbf x)-V_0\big]^2
 \end{equation}
 which achieves zero only for configurations that satisfy all the constraints.
+Introduced in Ref.~\cite{Fyodorov_2019_A}, the existence of solutions and the
+geometric structure of the cost function were studied for the linear problem in
+a series of papers \cite{Fyodorov_2019_A, Fyodorov_2020_Counting,
+Fyodorov_2022_Optimization} and later reviewed \cite{Vivo_2024_Random}. Some
+work on the equilibrium measure of the cost function in the nonlinear problem
+was made in Ref.~\cite{Tublin_2022_A}, and the problem was solved in
+Ref.~\cite{Urbani_2023_A}. Subsequent work has studied varied dynamics applied
+to the cost function, including gradient descent, Hessian descent, Langevin,
+stochastic gradient descent, and approximate message passing
+\cite{Kamali_2023_Dynamical, Kamali_2023_Stochastic, Montanari_2023_Solving,
+Montanari_2024_On}. Finally, some progress has been made on aspects of the
+geometric structure of the cost function in the nonlinear problem
+\cite{Kent-Dobias_2024_Conditioning, Kent-Dobias_2024_Algorithm-independent}.
+
 From the perspective of the cost function, the set of solutions looks like a network of flat canyons at the bottom of the cost landscape.
 Here we dispense with the cost function and study the set of solutions
 directly. This set can be written as
-- 
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