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Documentation revamp #102

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2 changes: 2 additions & 0 deletions Project.toml
Original file line number Diff line number Diff line change
Expand Up @@ -7,6 +7,7 @@ version = "0.6.1"
ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
DensityInterface = "b429d917-457f-4dbc-8f4c-0cc954292b1d"
DifferentiableFrankWolfe = "b383313e-5450-4164-a800-befbd27b574d"
DocStringExtensions = "ffbed154-4ef7-542d-bbb7-c09d3a79fcae"
LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
RequiredInterfaces = "97f35ef4-7bc5-4ec1-a41a-dcc69c7308c6"
Expand All @@ -25,6 +26,7 @@ InferOptFrankWolfeExt = "DifferentiableFrankWolfe"
ChainRulesCore = "1"
DensityInterface = "0.4.0"
DifferentiableFrankWolfe = "0.2"
DocStringExtensions = "0.9"
LinearAlgebra = "<0.0.1,1"
Random = "<0.0.1,1"
RequiredInterfaces = "0.1.3"
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6 changes: 3 additions & 3 deletions README.md
Original file line number Diff line number Diff line change
Expand Up @@ -12,20 +12,20 @@
InferOpt.jl is a toolbox for using combinatorial optimization algorithms within machine learning pipelines.

It allows you to create differentiable layers from optimization oracles that do not have meaningful derivatives.
Typical examples include mixed integer linear programs or graph algorithms.
Typical examples include mixed integer linear programs or graph algorithms, but any discrete function is compatible.

## Getting started

To install the stable version, open a Julia REPL and run the following command:

```julia
julia> using Pkg; Pkg.add("InferOpt")
julia> import Pkg; Pkg.add("InferOpt")
```

To install the development version, run this command instead:

```julia
julia> using Pkg; Pkg.add(url="https://github.com/axelparmentier/InferOpt.jl")
julia> import Pkg; Pkg.add(url="https://github.com/axelparmentier/InferOpt.jl")
```

## Citing us
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