Docs redux Part II

LICENSE.md

@@ -1,6 +1,6 @@
 The SDDP.jl package is licensed under the Mozilla Public License, Version 2.0:
 
-> Copyright (c) 2017: Oscar Dowson.
+> Copyright (c) 2017-2018: Oscar Dowson.
 >
 >
 > Mozilla Public License, version 2.0

README.md

@@ -4,32 +4,11 @@
 |:-----------------:|:--------------------:|:----------------:|
 | [![][docs-latest-img]][docs-latest-url] | [![Build Status][build-img]][build-url] | [![Codecov branch][codecov-img]][codecov-url]
 
-## Installation
-This package is unregistered so you will need to `Pkg.clone` it as follows:
-```julia
-Pkg.clone("https://github.com/odow/SDDP.jl.git")
-```
-
 ## Documentation
 
-The documentation is still very incomplete, however the user-facing API from the examples should
-be stable enough to use.
-
-**If you are stuggling to figure out how to use something, raise a Github issue!**
-
-However, you can find some documentation at https://odow.github.io/SDDP.jl/latest/
-
-In addition, most functions are documented, and this can be accessed via the Julia
-help. e.g.:
-```julia
-julia>? @state
-```
+You can find the documentation at https://odow.github.io/SDDP.jl/latest/.
 
-Some other resources include:
- - many examples: https://github.com/odow/SDDP.jl/tree/master/examples
- - a paper on Optimization-Online:
-http://www.optimization-online.org/DB_HTML/2017/12/6388.html 
- - an example of a large-scale model here: https://github.com/odow/MilkPOWDER
+**If you are struggling to figure out how to use something, raise a Github issue!**
 
 ## Examples
 
@@ -43,21 +22,6 @@ Bonus points for models where you know the optimal first stage objective value.
 We need your bug reports! We've only stressed a few code paths on real-world models.
 If you run into any problems, [file an issue here](https://github.com/odow/SDDP.jl/issues/new).
 
-## FAQ
-
-**Q.** How do I make the constraint coefficients random?
-
-**A.** Due to the design of JuMP, it's difficult to efficiently modify constraint
-coefficients. Therefore, you can only vary the right-hand-side of a constraint
-using the `@rhsnoise` macro.
-
-As a work around, we suggest you either reformulate the model so the uncertainty
-appears in the RHS, or model the uncertainty as a markov process. Take a look at
-the [asset management example](https://github.com/odow/SDDP.jl/blob/master/examples/AssetManagement/asset_management.jl)
-to see an example of this. Make sure you keep in mind that a new value function
-is built at each markov state which increases the computation time and memory
-requirements.
-
 ## Other Packages
 
 `SDDP.jl` isn't the only Julia package for solving multi-stage stochastic programs.

docs/make.jl

@@ -17,10 +17,11 @@ makedocs(
             "tutorial/05_risk.md",
             "tutorial/06_cut_selection.md",
             "tutorial/07_plotting.md",
-            "tutorial/08_odds_and_ends.md"
+            "tutorial/08_odds_and_ends.md",
+            "tutorial/09_nonlinear.md",
+            "tutorial/10_parallel.md"
         ],
         "Readings" => "readings.md",
-        "Old Manual" => "oldindex.md",
         "Reference" => "apireference.md"
     ],
     assets = [

docs/src/apireference.md

@@ -49,6 +49,8 @@ DynamicPriceInterpolation
 solve
 MonteCarloSimulation
 BoundConvergence
+Asynchronous
+Serial
 ```
 ## Understanding the solution
 ```@docs

docs/src/index.md

@@ -12,9 +12,6 @@ optimization, the SDDP algorithm, Julia, and JuMP.
     If you don't have that background, you may want to brush up on some
     [Readings](@ref).
 
-!!! note
-    You can find the old, terribly incomplete documentation at [Old Manual](@ref).
-
 ## Getting started
 
 This package is unregistered so you will need to `Pkg.clone` it as follows:
@@ -27,7 +24,7 @@ If you want to use the parallel features of SDDP.jl, you should start Julia with
 some worker processes (`julia -p N`), or add by running `julia> addprocs(N)` in
 a running Julia session.
 
-Once you've got SDDP.jl installed, you should read some tutorials, beginnng with
+Once you've got SDDP.jl installed, you should read some tutorials, beginning with
 [Tutorial One: first steps](@ref).
 
 ## Citing SDDP.jl
@@ -42,3 +39,19 @@ If you use SDDP.jl, we ask that you please cite the following [paper](http://www
 	year = {2017}
 }
 ```
+
+## FAQ
+
+**Q.** How do I make the constraint coefficients random?
+
+**A.** Due to the design of JuMP, it's difficult to efficiently modify constraint
+coefficients. Therefore, you can only vary the right hand-side of a constraint
+using the `@rhsnoise` macro.
+
+As a work around, we suggest you either reformulate the model so the uncertainty
+appears in the RHS, or model the uncertainty as a Markov process.
+[Tutorial Four: Markovian policy graphs](@ref) explains how to implement this.
+You might also want to take a look at the [asset management example](https://github.com/odow/SDDP.jl/blob/master/examples/AssetManagement/asset_management.jl)
+to see an example of this. Make sure you keep in mind that a new value function
+is built at each Markov state which increases the computation time and memory
+requirements.