Replace tutorial.md by link to tutorial (#124)

JuliaSmoothOptimizers · Jul 24, 2023 · f68fd13 · f68fd13
1 parent 4c53c42
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Showing 1 changed file with 1 addition and 58 deletions.
diff --git a/docs/src/tutorial.md b/docs/src/tutorial.md
@@ -6,61 +6,4 @@
 Pages = ["tutorial.md"]
 ```
 
-## Fine-tune CaNNOLeS
-
-CaNNOLeS.jl exports the function `cannoles`:
-```
-   cannoles(nlp :: AbstractNLPModel; kwargs...)
-```
-
-Find below a list of the main options of `cannoles`.
-
-### Tolerances on the problem
-
-```
-| Parameters           | Type          | Default         | Description                                        |
-| -------------------- | ------------- | --------------- | -------------------------------------------------- |
-| atol                 | AbstractFloat | √eps(T)         | absolute tolerance.                                |
-| rtol                 | AbstractFloat | √eps(T)         | relative tolerance.                                |
-| unbounded_threshold  | AbstractFloat | -1e5            | below this threshold the problem is unbounded.     |
-| max_eval             | Integer       | 100000          | evaluation limit, e.g. `neval_residual(nls) + neval_cons(nls) > max_eval` |
-| max_time             | AbstractFloat | 30.             | maximum number of seconds.                         |
-| max_inner            | Integer       | 10000           | maximum number of iterations.                      |
-```
-
-### Algorithmic parameters
-
-```
-| Parameters                  | Type           | Default           | Description                                        |
-| --------------------------- | -------------- | ----------------- | -------------------------------------------------- |
-| x                           | AbstractVector | copy(nls.meta.x0) | initial guess. |
-| λ                           | AbstractVector | T[]               | initial guess for the Lagrange mutlipliers. |
-| method                      | Symbol         | :Newton           | method to compute direction, `:Newton`, `:LM`, `:Newton_noFHess`, or `:Newton_vanishing`. |
-| linsolve                    | Symbol         | :ma57             | solver use to compute the factorization: `:ma57`, `:ma97`, `:ldlfactorizations` |
-| check_small_residual        | Bool           | false             | |
-| always_accept_extrapolation | Bool           | false             | |
-| δdec                        | Real           | T(0.1)            | |
-```
-
-## Examples
-
-```@example ex1
-using CaNNOLeS, ADNLPModels
-
-# Rosenbrock
-nls = ADNLSModel(x -> [x[1] - 1; 10 * (x[2] - x[1]^2)], [-1.2; 1.0], 2)
-stats = cannoles(nls, atol = 1e-5, x = ones(2))
-```
-
-```@example ex1
-# Constrained
-nls = ADNLSModel(
-  x -> [x[1] - 1; 10 * (x[2] - x[1]^2)],
-  [-1.2; 1.0],
-  2,
-  x -> [x[1] * x[2] - 1],
-  [0.0],
-  [0.0],
-)
-stats = cannoles(nls, max_time = 10.)
-```
+You can check an [Introduction to CaNNOLeS.jl tutorial](https://jso.dev/tutorials/introduction-to-cannoles/) on our site, [JSO.dev](https://jso.dev).