Merge pull request #176 from JuliaStochOpt/ci

Update continuous integration

Author: frapac

.travis.yml

@@ -6,7 +6,9 @@ julia:
   - 1.4
 notifications:
   email: false
-
+branches:
+  only:
+    - master
 after_success:
   # push coverage results to Coveralls
   - julia -e 'using Pkg; cd(Pkg.dir("StochDynamicProgramming")); Pkg.add("Coverage"); using Coverage; Coveralls.submit(Coveralls.process_folder())'

Project.toml

@@ -1,11 +1,8 @@
 name = "StochDynamicProgramming"
 uuid = "f6024387-75e5-5fdf-b095-327b05c0905f"
-authors = ["mrchaos <[email protected]>"]
-version = "0.5.0"
+version = "0.6.0"
 
 [deps]
-Cbc = "9961bab8-2fa3-5c5a-9d89-47fab24efd76"
-Clp = "e2554f3b-3117-50c0-817c-e040a3ddf72d"
 CutPruners = "65d46eb8-70e9-5a30-bf48-2afa3a021b8f"
 DelimitedFiles = "8bb1440f-4735-579b-a4ab-409b98df4dab"
 Distributed = "8ba89e20-285c-5b6f-9357-94700520ee1b"
@@ -23,10 +20,13 @@ SpecialFunctions = "276daf66-3868-5448-9aa4-cd146d93841b"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 
 [compat]
-julia = "^1.3.0"
+julia = "^1.3"
+JuMP = "0.21"
 
 [extras]
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
+Cbc = "9961bab8-2fa3-5c5a-9d89-47fab24efd76"
+Clp = "e2554f3b-3117-50c0-817c-e040a3ddf72d"
 
 [targets]
-test = ["Test"]
+test = ["Test", "Cbc", "Clp"]

README.md

@@ -1,72 +1,78 @@
 # StochDynamicProgramming
 
 
-**WARNING:** *This package is currently in development. Any help or feedback is appreciated.*
-
-
-**Latest release:** v0.5.0
-
 | **Documentation** | **Build Status** | **Social** |
 |:-----------------:|:----------------:|:----------:|
 | | [![Build Status][build-img]][build-url] | [![Gitter][gitter-img]][gitter-url] |
 | [![][docs-stable-img]][docs-stable-url] |  [![Codecov branch][codecov-img]][codecov-url] | [<img src="https://upload.wikimedia.org/wikipedia/commons/thumb/a/af/Discourse_logo.png/799px-Discourse_logo.png" width="64">][discourse-url] |
 
-
-
-This is a Julia package for optimizing controlled stochastic dynamic system (in discrete time). It offers three methods of resolution :
+This is a Julia package for optimizing controlled stochastic dynamic system,
+in discrete time. It offers three methods of resolution :
 
 - *Stochastic Dual Dynamic Programming* (SDDP) algorithm.
 - *Extensive formulation*.
 - *Stochastic Dynamic Programming*.
 
+It is built on top of [JuMP](https://github.com/JuliaOpt/JuMP.jl).
+
+StochDynamicProgramming asks the user to provide explicit the cost `c(t, x, u, w)` and
+dynamics `f(t, x, u, w)` functions. Also, the package was developed back
+in 2016, and some parts of its API are not idiomatic, in a Julia sense.
+For other implementations of the SDDP algorithm in Julia, we advise to
+have a look at these two packages:
+
+* [SDDP.jl](https://github.com/odow/SDDP.jl)
+* [StructDualDynProg.jl](https://github.com/JuliaStochOpt/StructDualDynProg.jl)
+
+
 
-It is built upon [JuMP]
+## What problems solves this package ?
 
-## What problem can we solve with this package ?
+StochDynamicProgramming targets problems with
 
 - Stage-wise independent discrete noise
 - Linear dynamics
-- Linear or convex piecewise linear cost
+- Linear or convex piecewise linear costs
 
 Extension to non-linear formulation are under development.
-Extension to more complex alea dependance are under developpment.
 
-## Why Extensive formulation ?
+
+### Why SDDP?
+
+SDDP is a dynamic programming algorithm relying on cutting planes. The algorithm requires convexity
+of the value function but does not discretize the state space. The complexity is linear in the
+number of stage, and can accomodate higher dimension state spaces than standard dynamic programming.
+The algorithm returns exact lower bound and estimated upper bound as well as approximate optimal
+control strategies.
+
+### Why Extensive formulation ?
 
 An extensive formulation approach consists in representing the stochastic problem as a deterministic
-one with more variable and call a standard deterministic solver. Mainly usable in a linear
+one and then calling a standard deterministic solver. It is mainly usable in a linear
 setting. Computational complexity is exponential in the number of stages.
 
-## Why Stochastic Dynamic Programming ?
+### Why Stochastic Dynamic Programming ?
 
 Dynamic Programming is a standard tool to solve stochastic optimal control problem with
-independent noise. The method require discretisation of the state space, and is exponential
-in the dimension of the state space.
+independent noise. The method requires discretizing the state space, and its
+complexity is exponential in the dimension of the state space.
 
-## Why SDDP?
-
-SDDP is a dynamic programming algorithm relying on cutting planes. The algorithm require convexity
-of the value function but does not discretize the state space. The complexity is linear in the
-number of stage, and can accomodate higher dimension state than standard dynamic programming.
-The algorithm return exact lower bound and estimated upper bound as well as approximate optimal
-control strategies.
 
 ## Installation
-Installing StochDynamicProgramming is an easy process.
-Currently, the package depends upon `StochasticDualDynamicProgramming.jl`, which is not
-yet registered in Julia's METADATA. To install the package,
-open Julia and enter
+
+StochDynamicProgramming is a registered Julia package.
+To install the package, open Julia and enter
 
 ```julia
-julia> Pkg.update()
-julia> Pkg.add("StochDynamicProgramming")
+julia> ]
+pkg> add StochDynamicProgramming
 
 ```
 
 
 ## Usage
 
-IJulia Notebooks will be provided to explain how this package work.
+IJulia Notebooks are provided to explain how this package works.
 A first example on a two dams valley [here](http://nbviewer.jupyter.org/github/leclere/StochDP-notebooks/blob/master/notebooks/damsvalley.ipynb).
 
 

test/runtests.jl

@@ -8,7 +8,7 @@
 
 
 using StochDynamicProgramming
-using Clp, JuMP, Nullables #,Gurobi
+using Clp, JuMP, Nullables
 using Printf
 using Statistics
 using Test