--- a/README.md Thu Jan 23 23:34:05 2025 +0100
+++ b/README.md Thu Jan 23 23:48:52 2025 +0100
@@ -1,19 +1,19 @@
# Proximal methods for point source localisation: the implementation
-This package contains [Rust] codes for the manuscript “_Proximal methods for
-point source localisation_” ([arXiv:2212.02991]) by Tuomo Valkonen
-⟨tuomov@iki.fi⟩. It concerns solution of problems of the type
+This package contains the [Rust] codes for the numerical experiments in the articles
+* T. Valkonen, “_Proximal methods for
+point source localisation_”, Journal of Nonsmooth Analysis and Optimization 4 (2023), 10433, [doi:10.46298/jnsao-2023-10433] ([arXiv:2212.02991])
+* T. Valkonen, “_Point source localisation with unbalanced optimal transport_” (2025), submitted.
+
+It concerns solution of problems of the type
$$
\min_{μ ∈ ℳ(Ω)}~ F(μ) + λ \|μ\|_{ℳ(Ω)} + δ_{≥ 0}(μ),
$$
where $F$ is a data term, and $ℳ(Ω)$ is the space of Radon measures on the
(rectangular) domain $Ω ⊂ ℝ^n$. Implemented are $F(μ)=\frac12\|Aμ-b\|_2^2$ 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
-literature. For the 1-norm data term only the μPDPS is applicable.
+simple sensor grid.
## Installation and usage
@@ -49,6 +49,7 @@
[rust-GSL]: https://docs.rs/GSL/6.0.0/rgsl/
[Homebrew]: https://brew.sh
[arXiv:2212.02991]: https://arxiv.org/abs/2212.02991
+ [doi:10.46298/jnsao-2023-10433]: http://doi.org/10.46298/jnsao-2023-10433
### Building and running the experiments
@@ -57,7 +58,7 @@
cargo install --path=.
```
When doing this for the first time, several dependencies will be downloaded.
-Now you can run the default experiment with
+Now you can run the default set of experiments with
```
pointsource_algs -o results
```