Link checker

.github/ISSUE_TEMPLATE/10-bug-report.yml

@@ -11,7 +11,7 @@ body:
         Please, before submitting, make sure that:
 
         - There is not an [existing issue](https://github.com/JuliaSmoothOptimizers/DCISolver.jl/issues) with the same question
-        - You have read the [contributing guide](https://JuliaSmoothOptimizers.github.io/DCISolver.jl/dev/90-contributing/)
+        - You have read the [contributing guide](https://github.com/JuliaSmoothOptimizers/DCISolver.jl/blob/main/docs/src/90-contributing.md)
         - You are following the [code of conduct](https://github.com/JuliaSmoothOptimizers/DCISolver.jl/blob/main/CODE_OF_CONDUCT.md)
         The form below should help you in filling out this issue.
   - type: textarea

.github/ISSUE_TEMPLATE/20-feature-request.yml

@@ -9,7 +9,7 @@ body:
         Please, before submitting, make sure that:
 
         - There is not an [existing issue](https://github.com/JuliaSmoothOptimizers/DCISolver.jl/issues) with the same question
-        - You have read the [contributing guide](https://JuliaSmoothOptimizers.github.io/DCISolver.jl/dev/90-contributing/)
+        - You have read the [contributing guide](https://github.com/JuliaSmoothOptimizers/DCISolver.jl/blob/main/docs/src/90-contributing.md)
         - You are following the [code of conduct](https://github.com/JuliaSmoothOptimizers/DCISolver.jl/blob/main/CODE_OF_CONDUCT.md)
         The form below should help you in filling out this issue.
   - type: textarea

.github/ISSUE_TEMPLATE/30-usage.yml

@@ -11,7 +11,7 @@ body:
 
         - You have checked the [documentation](https://JuliaSmoothOptimizers.github.io/DCISolver.jl) and haven't found enough information
         - There is not an [existing issue](https://github.com/JuliaSmoothOptimizers/DCISolver.jl/issues) with the same question
-        - You have read the [contributing guide](https://JuliaSmoothOptimizers.github.io/DCISolver.jl/dev/90-contributing/)
+        - You have read the [contributing guide](https://github.com/JuliaSmoothOptimizers/DCISolver.jl/blob/main/docs/src/90-contributing.md)
         - You are following the [code of conduct](https://github.com/JuliaSmoothOptimizers/DCISolver.jl/blob/main/CODE_OF_CONDUCT.md)
         The form below should help you in filling out this issue.
   - type: textarea

.github/ISSUE_TEMPLATE/99-general.yml

@@ -9,7 +9,7 @@ body:
         Please, before submitting, make sure that:
 
         - There is not an [existing issue](https://github.com/JuliaSmoothOptimizers/DCISolver.jl/issues) with the same question
-        - You have read the [contributing guide](https://JuliaSmoothOptimizers.github.io/DCISolver.jl/dev/90-contributing/)
+        - You have read the [contributing guide](https://github.com/JuliaSmoothOptimizers/DCISolver.jl/blob/main/docs/src/90-contributing.md)
         - You are following the [code of conduct](https://github.com/JuliaSmoothOptimizers/DCISolver.jl/blob/main/CODE_OF_CONDUCT.md)
         The form below should help you in filling out this issue.
   - type: textarea

.lychee.toml

@@ -3,6 +3,7 @@ exclude = [
   "@cite",
   "^https://github.com/.*/releases/tag/v.*$",
   "^https://doi.org/FIXME$",
+  "^https://doi.org/10.1137/070679557$",
   "^https://JuliaSmoothOptimizers.github.io/DCISolver.jl/stable$",
   "zenodo.org/badge/DOI/FIXME$"
 ]

README.md

@@ -18,7 +18,7 @@ optimization problems of the form
 
 It uses other JuliaSmoothOptimizers packages for development.
 In particular, [NLPModels.jl](https://github.com/JuliaSmoothOptimizers/NLPModels.jl) is used for defining the problem, and [SolverCore](https://github.com/JuliaSmoothOptimizers/SolverCore.jl) for the output.
-It uses [LDLFactorizations.jl](https://github.com/JuliaSmoothOptimizers/LDLFactorizations.jl) by default to compute the factorization in the tangent step. [HSL.jl](https://github.com/JuliaSmoothOptimizers/HSL.jl) provides alternative linear solvers if [libHSL](https://licences.stfc.ac.uk/product/libhsl) can be downloaded.
+It uses [LDLFactorizations.jl](https://github.com/JuliaSmoothOptimizers/LDLFactorizations.jl) by default to compute the factorization in the tangent step. [HSL.jl](https://github.com/JuliaSmoothOptimizers/HSL.jl) provides alternative linear solvers if libHSL can be downloaded.
 The feasibility steps are factorization-free and use iterative methods from [Krylov.jl](https://github.com/JuliaSmoothOptimizers/Krylov.jl)
 
 ## References
@@ -39,7 +39,7 @@ If you use DCISolver.jl in your work, please cite using the reference given in [
 
 ## Contributing
 
-If you want to make contributions of any kind, please first that a look into our [contributing guide directly on GitHub](docs/src/90-contributing.md) or the [contributing page on the website](https://JuliaSmoothOptimizers.github.io/DCISolver.jl/dev/90-contributing/)
+If you want to make contributions of any kind, please first take a look at our [contributing guide directly on GitHub](docs/src/90-contributing.md).
 
 ---
 

docs/src/2-benchmark.md

@@ -35,7 +35,7 @@ cutest_problems = (CUTEstModel(p) for p in pnames)
 length(cutest_problems) # number of problems
 ```
 
-We compare here DCISolver with [Ipopt](https://link.springer.com/article/10.1007/s10107-004-0559-y) (Wächter, A., & Biegler, L. T. (2006). On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical programming, 106(1), 25-57.), via the [NLPModelsIpopt.jl](https://github.com/JuliaSmoothOptimizers/NLPModelsIpopt.jl) thin wrapper, with DCISolver on a subset of CUTEst problems.
+We compare here DCISolver with [Ipopt](https://doi.org/10.1007/s10107-004-0559-y) (Wächter, A., & Biegler, L. T. (2006). On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical programming, 106(1), 25-57.), via the [NLPModelsIpopt.jl](https://github.com/JuliaSmoothOptimizers/NLPModelsIpopt.jl) thin wrapper, with DCISolver on a subset of CUTEst problems.
 
 ``` @example ex1
 using DCISolver, NLPModelsIpopt
@@ -135,7 +135,7 @@ print_pp_column(:neval_jac, stats) # with respect to number of jacobian evaluati
 
 ## CUTEst benchmark with Knitro
 
-In this second part, we present the result of a similar benchmark with a maximum of 10000 variables and constraints (82 problems), and including the solver [`KNITRO`](https://link.springer.com/chapter/10.1007/0-387-30065-1_4) (Byrd, R. H., Nocedal, J., & Waltz, R. A. (2006). K nitro: An integrated package for nonlinear optimization. In Large-scale nonlinear optimization (pp. 35-59). Springer, Boston, MA.) via [`NLPModelsKnitro.jl`](https://github.com/JuliaSmoothOptimizers/NLPModelsKnitro.jl). The script is included in [/benchmark/script10000_knitro.jl)](https://github.com/JuliaSmoothOptimizers/DCISolver.jl/blob/main/benchmark/script10000_knitro.jl). We report here a performance profile with respect
-to the elapsed time to solve the problems and to the sum of evaluations of objective and constrain functions, see [/benchmark/figures.jl)](https://github.com/JuliaSmoothOptimizers/DCISolver.jl/benchmark/figures.jl) for the code generating the profile wall.
+In this second part, we present the result of a similar benchmark with a maximum of 10000 variables and constraints (82 problems), and including the solver [`KNITRO`](https://doi.org/10.1007/0-387-30065-1_4) (Byrd, R. H., Nocedal, J., & Waltz, R. A. (2006). K nitro: An integrated package for nonlinear optimization. In Large-scale nonlinear optimization (pp. 35-59). Springer, Boston, MA.) via [`NLPModelsKnitro.jl`](https://github.com/JuliaSmoothOptimizers/NLPModelsKnitro.jl). The script is included in [/benchmark/script10000_knitro.jl)](https://github.com/JuliaSmoothOptimizers/DCISolver.jl/blob/main/benchmark/script10000_knitro.jl). We report here a performance profile with respect
+to the elapsed time to solve the problems and to the sum of evaluations of objective and constraint functions, see [/benchmark/figures.jl](https://github.com/JuliaSmoothOptimizers/DCISolver.jl/blob/main/benchmark/figures.jl) for the code generating the profile wall.
 
 ![Benchmark](./assets/ipopt_knitro_dcildl_82.png)

docs/src/91-developer.md

@@ -111,7 +111,6 @@ We try to keep a linear history in this repo, so it is important to keep your br
 ### Before creating a pull request
 
 !!! tip "Atomic git commits"
-    Try to create "atomic git commits" (recommended reading: [The Utopic Git History](https://blog.esciencecenter.nl/the-utopic-git-history-d44b81c09593)).
 
 - Make sure the tests pass.
 - Make sure the pre-commit tests pass.

docs/src/index.md

@@ -37,7 +37,7 @@ We refer to [jso.dev](https://jso.dev) for tutorials on the NLPModel API. This f
 
 The DCI algorithm is an iterative method that has the flavor of a projected gradient algorithm and could be characterized as
 a relaxed feasible point method with dynamic control of infeasibility. It is a combination of two steps: a tangent step and a feasibility step.
-It uses [LDLFactorizations.jl](https://github.com/JuliaSmoothOptimizers/LDLFactorizations.jl) by default to compute the factorization in the tangent step. [HSL.jl](https://github.com/JuliaSmoothOptimizers/HSL.jl) provides alternative linear solvers if [libHSL](https://licences.stfc.ac.uk/product/libhsl) can be downloaded.
+It uses [LDLFactorizations.jl](https://github.com/JuliaSmoothOptimizers/LDLFactorizations.jl) by default to compute the factorization in the tangent step. [HSL.jl](https://github.com/JuliaSmoothOptimizers/HSL.jl) provides alternative linear solvers if libHSL can be downloaded.
 The feasibility steps are factorization-free and use iterative methods from [Krylov.jl](https://github.com/JuliaSmoothOptimizers/Krylov.jl).
 
 ## Example