Update documentation

docs-src/index.md

@@ -1,6 +1,8 @@
 # MIPLearn
 
-**MIPLearn** is an extensible framework for *Learning-Enhanced Mixed-Integer Optimization*, an approach targeted at discrete optimization problems that need to be repeatedly solved with only minor changes to input data. The package uses Machine Learning (ML) to automatically identify patterns in previously solved instances of the problem, or in the solution process itself, and produces hints that can guide a conventional MIP solver towards the optimal solution faster. For particular classes of problems, this approach has been shown to provide significant performance benefits (see [benchmark results](benchmark.md#benchmark-results) and [references](about.md#references) for more details).
+**MIPLearn** is an extensible framework for **Learning-Enhanced Mixed-Integer Optimization**, an approach targeted at discrete optimization problems that need to be repeatedly solved with only minor changes to input data.
+
+The package uses Machine Learning (ML) to automatically identify patterns in previously solved instances of the problem, or in the solution process itself, and produces hints that can guide a conventional MIP solver towards the optimal solution faster. For particular classes of problems, this approach has been shown to provide significant performance benefits (see [benchmark results](problems.md) and [references](about.md#references) for more details).
 
 ### Features
 
@@ -22,5 +24,5 @@
 
 ### Souce Code
 
-* [https://github.com/iSoron/miplearn](https://github.com/iSoron/miplearn)
+* [https://github.com/ANL-CEEESA/MIPLearn](https://github.com/ANL-CEEESA/MIPLearn)
 

docs-src/problems.md

@@ -30,9 +30,7 @@ Given a simple undirected graph $G=(V,E)$ and weights $w \in \mathbb{R}^V$, the
 
 The class `MaxWeightStableSetGenerator` can be used to generate random instances of this problem, with user-specified probability distributions. When the constructor parameter `fix_graph=True` is provided, one random Erdős-Rényi graph $G_{n,p}$ is generated during the constructor, where $n$ and $p$ are sampled from user-provided probability distributions `n` and `p`. To generate each instance, the generator independently samples each $w_v$ from the user-provided probability distribution `w`. When `fix_graph=False`, a new random graph is generated for each instance, while the remaining parameters are sampled in the same way.
 
-### Benchmark challenges
-
-#### Challenge A
+### Challenge A
 
 * Fixed random Erdős-Rényi graph $G_{n,p}$ with $n=200$ and $p=5\%$
 * Random vertex weights $w_v \sim U(100, 150)$
@@ -96,7 +94,7 @@ By default, all generated prices, weights and capacities are rounded to the near
     * Freville, Arnaud, and Gérard Plateau. *An efficient preprocessing procedure for the multidimensional 0–1 knapsack problem.* Discrete applied mathematics 49.1-3 (1994): 189-212.
     * Fréville, Arnaud. *The multidimensional 0–1 knapsack problem: An overview.* European Journal of Operational Research 155.1 (2004): 1-21.
     
-#### Challenge A
+### Challenge A
 
 * 250 variables, 10 constraints, fixed weights
 * $w \sim U(0, 1000), \gamma \sim U(0.95, 1.05)$