From ccb827847d31639dc45868ab90e10f359d4fea6a Mon Sep 17 00:00:00 2001 From: Dr Rafael Bailo Date: Tue, 30 Apr 2024 11:57:44 +0100 Subject: [PATCH] Fix code indentation --- paper.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/paper.md b/paper.md index 4509f0e..20eeb6b 100644 --- a/paper.md +++ b/paper.md @@ -139,9 +139,9 @@ Most of the [CBXPy](https://pdips.github.io/CBXpy/) implementation uses basic Py The library is available on [GitHub](https://github.com/pdips/CBXpy) and can be installed via `pip`. It is licensed under the MIT license. Below, we provide a short example on how to optimise a function with CBXPy. ```Python - from cbx.dynamics import CBO # import the CBO class - f = lambda x: x[0]**2 + x[1]**2 # define the function to minimise - x = CBO(f, d=2).optimize() # run the optimisation +from cbx.dynamics import CBO # import the CBO class +f = lambda x: x[0]**2 + x[1]**2 # define the function to minimise +x = CBO(f, d=2).optimize() # run the optimisation ``` More examples and details on the implementation are available in the [documentation](https://pdips.github.io/CBXpy/). @@ -154,9 +154,9 @@ More examples and details on the implementation are available in the [documentat Basic function minimisation can be performed by running: ```julia - using ConsensusBasedX # load the ConsensusBasedX package - f(x) = x[1]^2 + x[2]^2 # define the function to minimise - x = minimise(f, D = 2) # run the minimisation +using ConsensusBasedX # load the ConsensusBasedX package +f(x) = x[1]^2 + x[2]^2 # define the function to minimise +x = minimise(f, D = 2) # run the minimisation ``` The library is available on [GitHub](https://github.com/PdIPS/ConsensusBasedX.jl). It has been registered in the [general Julia registry](https://github.com/JuliaRegistries/General), and therefore it can be installed by running `]add ConsensusBasedX`. It is licensed under the MIT license. More examples and full instructions are available in the [documentation](https://pdips.github.io/ConsensusBasedX.jl/).