Improve clarity in examples section of paper.md

Author: MohamedLaghdafHABIBOULLAH

paper/paper.md

@@ -139,11 +139,7 @@ We will illustrate this in the examples below.
 # Examples
 
 
-We consider two examples where the smooth part $f$ is nonconvex and the nonsmooth part $h$ is either $\ell^{1/2}$ or $\ell_0$ norm with constraints.
-
-We compare the performance of our solvers with (**PANOC**) solver [@stella-themelis-sopasakis-patrinos-2017] implemented in [ProximalAlgorithms.jl](https://github.com/JuliaFirstOrder/ProximalAlgorithms.jl).
-
-We illustrate the capabilities of [RegularizedOptimization.jl](https://github.com/JuliaSmoothOptimizers/RegularizedOptimization.jl) on two nonsmooth and nonconvex problems:
+We consider illustrate the capabilities of [RegularizedOptimization.jl](https://github.com/JuliaSmoothOptimizers/RegularizedOptimization.jl) on two nonsmooth and nonconvex problems:
 
 - **Support Vector Machine (SVM) with $\ell^{1/2}$ penalty** for image classification [@aravkin-baraldi-orban-2024].  
 - **Nonnegative Matrix Factorization (NNMF) with $\ell_0$ penalty and bound constraints** [@kim-park-2008].
@@ -168,7 +164,7 @@ The NNMF problem can be set up in a similar way, replacing the model by nnmf_mod
 
 ### Numerical results
 
-We compare **PANOC** (from [ProximalAlgorithms.jl](https://github.com/JuliaFirstOrder/ProximalAlgorithms.jl)) with **TR**, **R2N**, and **LM** from our library.  
+We compare **PANOC** [@stella-themelis-sopasakis-patrinos-2017](from [ProximalAlgorithms.jl](https://github.com/JuliaFirstOrder/ProximalAlgorithms.jl)) with **TR**, **R2N**, and **LM** from our library.  
 The results are summarized in the combined table below:
 
 ```