Merge pull request #21 from OptimalTransportNetworks/development

Development

Author: SebKrantz

README.md

@@ -15,9 +15,7 @@ The model/software uses duality principles to optimize over the space of network
 
 For more information about the model see [this folder](https://github.com/SebKrantz/OptimalTransportNetworkToolbox/tree/main/docs/paper_materials) and the [MATLAB User Guide](https://raw.githubusercontent.com/SebKrantz/OptimalTransportNetworkToolbox/main/docs/User%20Guide.pdf). 
 
-The model is the first of its kind and a pathbreaking contribution towards the welfare maximizing planning of transport infrastructure. Its creation has been funded by the European Union through an [ERC Research Grant](https://cordis.europa.eu/project/id/804095). The author of this Julia library has no personal connections to the authors, but has used their Matlab library for research purposes and belives that it deserves an accessible open-source implementation. Community efforts to further improve the code are welcome. In particular, there is a probabilistic extenstion to solving the model using MCMC methods which may be more suitable for large networks, implemented in:
-
-Kreindler, G., Gaduh, A., Graff, T., Hanna, R., & Olken, B. A. (2023). Optimal Public Transportation Networks: Evidence from the World's Largest Bus Rapid Transit System in Jakarta (No. w31369). National Bureau of Economic Research.
+The model is the first of its kind and a pathbreaking contribution towards the welfare maximizing planning of transport infrastructure. Its creation has been funded by the European Union through an [ERC Research Grant](https://cordis.europa.eu/project/id/804095). Community efforts to further improve the code are welcome. 
 
 ## Example
 

docs/src/index.md

@@ -10,9 +10,7 @@ The model/software uses duality principles to optimize over the space of network
 
 For more information about the model see [this folder](https://github.com/SebKrantz/OptimalTransportNetworkToolbox/tree/main/docs/paper_materials) and the [MATLAB User Guide](https://raw.githubusercontent.com/SebKrantz/OptimalTransportNetworkToolbox/main/docs/User%20Guide.pdf). 
 
-The model is the first of its kind and a pathbreaking contribution towards the welfare maximizing planning of transport infrastructure. Its creation has been funded by the European Union through an [ERC Research Grant](https://cordis.europa.eu/project/id/804095). The author of this Julia library has no personal connections to the authors, but has used their Matlab library for research purposes and belives that it deserves an accessible open-source implementation. Community efforts to further improve the code are welcome. In particular, there is a probabilistic extenstion to solving the model using MCMC methods which may be more suitable for large networks, implemented in:
-
-Kreindler, G., Gaduh, A., Graff, T., Hanna, R., & Olken, B. A. (2023). Optimal Public Transportation Networks: Evidence from the World's Largest Bus Rapid Transit System in Jakarta (No. w31369). National Bureau of Economic Research.
+The model is the first of its kind and a pathbreaking contribution towards the welfare maximizing planning of transport infrastructure. Its creation has been funded by the European Union through an [ERC Research Grant](https://cordis.europa.eu/project/id/804095). The author of this Julia library has no personal connections to the authors, but has used their Matlab library for research purposes and belives that it deserves an accessible open-source implementation. Community efforts to further improve the code are welcome. 
 
 ## Example
 

src/main/init_parameters.jl

@@ -31,8 +31,8 @@ Returns a `param` dict with the model parameters. These are independent of the g
 - `optimizer = Ipopt.Optimizer`: Optimizer to be used
 - `optimizer_attr::Dict`: Dict of attributes passed to the optimizer (e.g. `Dict(:tol => 1e-5)`)
 - `model_attr::Dict`: Dict of tuples (length 2) passed to the model (e.g. `Dict(:backend => (MOI.AutomaticDifferentiationBackend(), MathOptSymbolicAD.DefaultBackend()))` to use Symbolic AD)
-- `model::Function`: For custom models => a function that taks an optimizer and an 'auxdata' structure as created by create_auxdata() as input and returns a fully parameterized JuMP model
-- `recover_allocation::Function`: For custom models => a function that takes a solution and 'auxdata' structure as input and returns the allocation variables. In particular, it should return a dict with symbol keys returning at least objects :welfare => scalar welfare measure, :Pjn => prices, :PCj => aggregate condumption, and :Qjkn => flows. 
+- `model::Function`: For custom models => a function that taks an optimizer and an 'auxdata' structure as created by `create_auxdata()` as input and returns a fully parameterized JuMP model
+- `recover_allocation::Function`: For custom models => a function that takes a solved JuMP model and 'auxdata' structure as input and returns the allocation variables. In particular, it should return a dict with symbol keys returning at least objects :welfare => scalar welfare measure, :Pjn => prices, :PCj => aggregate consumption, and :Qjkn => flows. 
 
 # Examples
 ```julia
@@ -48,6 +48,7 @@ function init_parameters(; alpha = 0.5, beta = 1, gamma = 1, K = 1, sigma = 5, r
 
     if !isempty(kwargs)
         for (key, value) in kwargs
+            println("Non-standard parameter '$(key)' added")
             param[key] = value
         end
     end