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8 changes: 8 additions & 0 deletions .github/workflows/action.yml
Original file line number Diff line number Diff line change
Expand Up @@ -25,3 +25,11 @@ jobs:
channel: ${{ matrix.julia-version }}
- uses: julia-actions/julia-buildpkg@latest
- uses: julia-actions/julia-runtest@latest
- uses: julia-actions/julia-processcoverage@v1
- uses: codecov/codecov-action@v5
with:
files: lcov.info
token: ${{ secrets.CODECOV_TOKEN }}
# The org-wide token cannot imply a repository; the slug says
# which repo this upload is for.
slug: ${{ github.repository }}
6 changes: 3 additions & 3 deletions README.md
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@@ -1,10 +1,10 @@
# MadNCL.jl

[![Run tests](https://github.com/MadNLP/MadNCL.jl/actions/workflows/action.yml/badge.svg)](https://github.com/MadNLP/MadNCL.jl/actions/workflows/action.yml)
[![Run tests](https://github.com/madsuite-org/MadNCL.jl/actions/workflows/action.yml/badge.svg)](https://github.com/madsuite-org/MadNCL.jl/actions/workflows/action.yml)

An implementation of [Algorithm NCL](https://link.springer.com/chapter/10.1007/978-3-319-90026-1_8) in Julia.

MadNCL is built as [MadNLP](https://github.com/MadNLP/MadNLP.jl)'s extension, and supports the solution of
MadNCL is built as [MadNLP](https://github.com/madsuite-org/MadNLP.jl)'s extension, and supports the solution of
large-scale nonlinear programs on GPUs. MadNCL is particularly good at solving infeasible
or degenerate optimization problems.

Expand Down Expand Up @@ -75,7 +75,7 @@ results = madncl(nlp)
## GPU support

MadNCL supports natively the solution of nonlinear programs on the GPU using MadNLPGPU.
To evaluate your model on the GPU, we recommend using [ExaModels](https://github.com/exanauts/ExaModels.jl).
To evaluate your model on the GPU, we recommend using [ExaModels](https://github.com/madsuite-org/ExaModels.jl).
For instance, you can implement the instance `elec` from the [COPS benchmark](https://www.mcs.anl.gov/~more/cops/) directly as:

```julia
Expand Down
10 changes: 10 additions & 0 deletions codecov.yml
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@@ -0,0 +1,10 @@
# Coverage numbers without CI redness: the project/patch checks report their
# figures but never fail — a coverage drop is information, not a broken build.
coverage:
status:
project:
default:
informational: true
patch:
default:
informational: true
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