From 8d855304912280c9d628f4ab5cbfc8ef6c65f816 Mon Sep 17 00:00:00 2001
From: ArseniyKholod <119304909+ArseniyKholod@users.noreply.github.com>
Date: Fri, 3 Nov 2023 18:33:24 +0200
Subject: [PATCH] Update differentiable_programming.jl

---
 docs/literate/src/files/differentiable_programming.jl | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/literate/src/files/differentiable_programming.jl b/docs/literate/src/files/differentiable_programming.jl
index 33427803afc..0960ba25d9f 100644
--- a/docs/literate/src/files/differentiable_programming.jl
+++ b/docs/literate/src/files/differentiable_programming.jl
@@ -43,7 +43,7 @@ scatter(real.(λ), imag.(λ), label="central flux")
 # As you can see here, the maximal real part is close to zero.
 
 relative_maximum = maximum(real, λ) / maximum(abs, λ)
-@test 3.0e-10 < relative_maximum < 8.0e-10 #src
+@test 3.0e-10 < relative_maximum < 9.0e-10 #src
 
 # Interestingly, if we add dissipation by switching to the `flux_lax_friedrichs`
 # at the interfaces, the maximal real part of the eigenvalues increases.
@@ -87,7 +87,7 @@ scatter(real.(λ), imag.(λ), label="central flux")
 # Here, the maximal real part is basically zero to machine accuracy.
 
 relative_maximum = maximum(real, λ) / maximum(abs, λ)
-@test 1.0e-17 < relative_maximum < 1.0e-15 #src
+@test 1.0e-17 < relative_maximum < 2.0e-15 #src
 
 # Moreover, the eigenvectors are not as ill-conditioned as in 2D.