update readme and docs

README.md

@@ -11,14 +11,19 @@ A Julia package for non-smooth optimization algorithms.
 This package provides algorithms for the minimization of objective functions
 that include non-smooth terms, such as constraints or non-differentiable penalties.
 Implemented algorithms include:
-* (Fast) Proximal gradient methods
-* Douglas-Rachford splitting
-* Three-term splitting
-* Primal-dual splitting algorithms
-* Newton-type methods
-
-This package works well in combination with [ProximalOperators](https://github.com/JuliaFirstOrder/ProximalOperators.jl) (>= 0.15),
-which contains a wide range of functions that can be used to express cost terms.
+- (Fast) Proximal gradient methods
+- Douglas-Rachford splitting
+- Three-term splitting
+- Primal-dual splitting algorithms
+- Newton-type methods
+
+Algorithms rely on:
+- [AbstractDifferentiation.jl](https://github.com/JuliaDiff/AbstractDifferentiation.jl) for automatic differentiation
+(but you can easily bring your own gradients)
+- the [ProximalCore API](https://github.com/JuliaFirstOrder/ProximalCore.jl) for proximal mappings, projections, etc,
+to handle non-differentiable terms
+(see for example [ProximalOperators](https://github.com/JuliaFirstOrder/ProximalOperators.jl)
+for an extensive collection of functions).
 
 ## Documentation
 

docs/src/index.md

@@ -5,16 +5,21 @@ A Julia package for non-smooth optimization algorithms. [Link to GitHub reposito
 This package provides algorithms for the minimization of objective functions
 that include non-smooth terms, such as constraints or non-differentiable penalties.
 Implemented algorithms include:
-* (Fast) Proximal gradient methods
-* Douglas-Rachford splitting
-* Three-term splitting
-* Primal-dual splitting algorithms
-* Newton-type methods
+- (Fast) Proximal gradient methods
+- Douglas-Rachford splitting
+- Three-term splitting
+- Primal-dual splitting algorithms
+- Newton-type methods
 
 Check out [this section](@ref problems_algorithms) for an overview of the available algorithms.
 
-This package works well in combination with [ProximalOperators](https://github.com/JuliaFirstOrder/ProximalOperators.jl) (>= 0.15),
-which contains a wide range of functions that can be used to express cost terms.
+Algorithms rely on:
+- [AbstractDifferentiation.jl](https://github.com/JuliaDiff/AbstractDifferentiation.jl) for automatic differentiation
+(but you can easily bring your own gradients)
+- the [ProximalCore API](https://github.com/JuliaFirstOrder/ProximalCore.jl) for proximal mappings, projections, etc,
+to handle non-differentiable terms
+(see for example [ProximalOperators](https://github.com/JuliaFirstOrder/ProximalOperators.jl)
+for an extensive collection of functions).
 
 !!! note
 
@@ -23,20 +28,11 @@ which contains a wide range of functions that can be used to express cost terms.
 
 ## Installation
 
-Install the latest stable release with
-
 ```julia
 julia> ]
 pkg> add ProximalAlgorithms
 ```
 
-To install the development version instead (`master` branch), do
-
-```julia
-julia> ]
-pkg> add ProximalAlgorithms#master
-```
-
 ## Citing
 
 If you use any of the algorithms from ProximalAlgorithms in your research, you are kindly asked to cite the relevant bibliography.
@@ -45,3 +41,4 @@ Please check [this section of the manual](@ref problems_algorithms) for algorith
 ## Contributing
 
 Contributions are welcome in the form of [issue notifications](https://github.com/JuliaFirstOrder/ProximalAlgorithms.jl/issues) or [pull requests](https://github.com/JuliaFirstOrder/ProximalAlgorithms.jl/pulls). When contributing new algorithms, we highly recommend looking at already implemented ones to get inspiration on how to structure the code.
+