# HG changeset patch
# User Tuomo Valkonen <tuomov@iki.fi>
# Date 1670331769 -7200
# Node ID 861f6c033646dfc53be4b8e45b19aa6f82223104
# Parent  a3de2575cfc87b18adcec6080cb8fd49c94cf307
Oops, some μ were x in the README.

diff -r a3de2575cfc8 -r 861f6c033646 README.md
--- a/README.md	Tue Dec 06 14:12:38 2022 +0200
+++ b/README.md	Tue Dec 06 15:02:49 2022 +0200
@@ -5,11 +5,11 @@
 point source localisation_” by Tuomo Valkonen ⟨tuomov@iki.fi⟩. It concerns
 solution of problems of the type
 $$
-    \min_{μ ∈ ℳ(Ω)}~ F(x) + λ \|μ\|_{ℳ(Ω)} + δ_{≥ 0}(x),
+    \min_{μ ∈ ℳ(Ω)}~ F(μ) + λ \|μ\|_{ℳ(Ω)} + δ_{≥ 0}(μ),
 $$
 where $F$ is a data term, and $ℳ(Ω)$ is the space of Radon measures on the
-(rectangular) domain $Ω ⊂ ℝ^n$. Implemented are $F(x)=\frac12\|Ax-b\|_2^2$ and
-$F(x)=\|Ax-b\|_1$ for the forward operator $A \in 𝕃(ℳ(Ω); ℝ^m)$ modelling a
+(rectangular) domain $Ω ⊂ ℝ^n$. Implemented are $F(μ)=\frac12\|Aμ-b\|_2^2$ and
+$F(μ)=\|Aμ-b\|_1$ for the forward operator $A \in 𝕃(ℳ(Ω); ℝ^m)$ modelling a
 simple sensor grid. For the 2-norm-squared data term implemented are the
 algorithms μFB, μFISTA, and μPDPS from the aforementioned manuscript along with
 comparison relaxed and fully corrective conditional gradient methods from the