From a58ba3a746e24387f0920721dddacda45f1c4feb Mon Sep 17 00:00:00 2001
From: Kostas Vilkelis <kostasvilkelis@gmail.com>
Date: Wed, 8 May 2024 03:34:52 +0200
Subject: [PATCH] explain guess structure

---
 docs/source/tutorial/hubbard_1d.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/docs/source/tutorial/hubbard_1d.md b/docs/source/tutorial/hubbard_1d.md
index 95240a4..1f84840 100644
--- a/docs/source/tutorial/hubbard_1d.md
+++ b/docs/source/tutorial/hubbard_1d.md
@@ -108,6 +108,8 @@ The object `full_model` now contains all the information needed to solve the mea
 To find a mean-field solution, we first require a starting guess.
 In cases where the non-interacting Hamiltonian is highly degenerate, there exists several possible mean-field solutions, many of which are local and not global minima of the energy landscape.
 Here the problem is simple enough that we can generate a random guess for the mean-field solution through the {autolink}`~pymf.tb.utils.generate_guess` function.
+It creates a random Hermitian tight-binding dictionary based on the hopping keys provided and the number of degrees of freedom within the unit cell.
+Because the mean-field solution cannot contain hoppings longer than the interaction itself, we use `h_0` keys as an input to {autolink}`~pymf.tb.utils.generate_guess`.
 Finally, to solve the model, we use the {autolink}`~pymf.solvers.solver` function which by default employes a root-finding algorithm to find a self-consistent mean-field solution.
 
 ```{code-cell} ipython3
-- 
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