add ignores

.gitignore

@@ -14,3 +14,5 @@ test.jl
 
 *.mps
 *.aux
+/docs/src/tutorials/conic_lower.md
+/docs/src/tutorials/lower_duals.md

docs/src/tutorials/conic_lower.md

@@ -5,7 +5,8 @@ EditURL = "<unknown>/src/tutorials/conic_lower.jl"
 # Conic Bilevel and Mixed Mode
 
 Here we present a simple bilevel program with a conic lower level model
-described in example 3.3 from \cite{chi2014models}.
+described in example 3.3 from
+[Chi, et al. 2014](https://journalofinequalitiesandapplications.springeropen.com/articles/10.1186/1029-242X-2014-168).
 
 ```math
 \begin{align}
@@ -75,25 +76,28 @@ Mixing the two of them can be done with Mixed Mode.
 
 The following code describes how to solve the problem with a MISOCP based solver.
 
+```julia
 using Xpress
 using QuadraticToBinary
 set_optimizer(model,
     ()->QuadraticToBinary.Optimizer{Float64}(Xpress.Optimizer(),lb=-10,ub=10))
 BilevelJuMP.set_mode(model,
-    BilevelJuMP.MixedMode(default = BilevelJuMP.FortunyAmatMcCarlMode(primal_big_M = 100, dual_big_M = 100)))
+    BilevelJuMP.MixedMode(default = BilevelJuMP.IndicatorMode()))
 BilevelJuMP.set_mode(con4, BilevelJuMP.ProductMode(1e-5))
 optimize!(model)
+```
+
+!!! info
+    This code was not executed because Xpress requires a commercial license.
+    Other solvers supporting MISOCP could be used such as Gurobi and CPLEX.
 
 We set the reformulation method as Mixed Mode and selected Indicator
 constraints to be the default for the case in which we do not explicitly
 specify the reformulation.
 Then we set product mode for the second order cone reformulation.
 
-```@example conic_lower
-#As described in Appendix \ref{ap-dual}, binary expansions require bounded
-```
-
-variables, hence the \texttt{QuadraticToBinary} meta-solver accepts fallback
+Binary expansions require bounded
+variables, hence the `QuadraticToBinary` meta-solver accepts fallback
 to upper and lower bounds (\texttt{ub} and \texttt{lb}),
 used for variables with no explicit bounds.
 

docs/src/tutorials/lower_duals.md

@@ -12,16 +12,17 @@ This modeling feature enables the implementation of workflows where one
 constraint. In particular, in the energy sector, it is common to model the
 energy prices as the dual variable associated with the energy demand
 equilibrium constraint. One example of an application that uses this feature
-is Fanzeres et al. (2019), which focuses on strategic bidding in
+is [Fanzeres et al. (2019)](https://doi.org/10.1016/j.ejor.2018.07.027),
+which focuses on strategic bidding in
 auction-based energy markets. A small and simplified example of the modeled
 problem would be the model:
 
 ```math
 \begin{align}
     &\max_{\lambda, q_S} \quad \lambda \cdot g_S \\
-    &\st \quad 0 \leq q_S \leq 100\\
-    &\hspace{28pt} (g_S, \lambda) \in \argmin_{g_S, g_{1}, g_{2}, g_D} 50 g_{R1} + 100  g_{R2} + 1000 g_{D}\\
-            & \hspace{70pt} \st \quad g_S \leq q_S \\
+    &\textit{s.t.} \quad 0 \leq q_S \leq 100\\
+    &\hspace{28pt} (g_S, \lambda) \in \arg\min_{g_S, g_{1}, g_{2}, g_D} 50 g_{R1} + 100  g_{R2} + 1000 g_{D}\\
+            & \hspace{70pt} \textit{s.t.} \quad g_S \leq q_S \\
             & \hspace{88pt} \quad  0 \leq g_S \leq 100 \\
             & \hspace{88pt}\quad  0 \leq g_{1} \leq 40 \\
             & \hspace{88pt}\quad  0 \leq g_{2} \leq 40 \\