Add a function select_sif_problems

Author: amontoison

README.md

@@ -88,6 +88,14 @@ using CUTEst
 available_problems = list_sif_problems()
 ```
 
+If you want to retrieve only problems with specific properties, you can use the function `select_sif_problems`:
+
+```julia
+using CUTEst
+
+filtered_problems = select_sif_problems(; min_var=10, max_var=100, only_linear_con=true)
+```
+
 ## Tutorial
 
 You can check an [Introduction to CUTEst.jl](https://jso.dev/tutorials/introduction-to-cutest/) on our [site](https://jso.dev/).

src/classification.jl

@@ -28,83 +28,76 @@ const origins = ["academic", "modelling", "real"]
 const classdb_origin = Dict("A" => origins[1], "M" => origins[2], "R" => origins[3])
 
 """
-    select_sif_problems(;min_var=1, max_var=Inf, min_con=0, max_con=Inf,
-            objtype=*, contype=*,
-            only_free_var=false, only_bnd_var=false,
-            only_linear_con=false, only_nonlinear_con=false,
-            only_equ_con=false, only_ineq_con=false,
-            custom_filter=*)
+    select_sif_problems(; min_var=1, max_var=Inf, min_con=0, max_con=Inf,
+                          objtype=*, contype=*, only_free_var=false,
+                          only_bnd_var=false, only_linear_con=false,
+                          only_nonlinear_con=false, only_equ_con=false,
+                          only_ineq_con=false, custom_filter=*)
 
-Returns a subset of the CUTEst problems using the classification file
-`classf.json`. This file is export together with the package, so if you have an
-old CUTEst installation, it can lead to inconsistencies.
+Returns a subset of the CUTEst problems using the classification file `classf.json`.
 
-- `min_var` and `max_var` set the number of variables in the problem;
-- `min_con` and `max_con` set the number of constraints in the problem
-(e.g., use `max_con=0` for unconstrained or `min_con=1` for constrained)
-- `only_*` flags are self-explaining. Note that they appear in conflicting
-pairs. Both can be false, but only one can be true.
-- `objtype` is the classification of the objective function according to the
-[MASTSIF classification file](https://www.cuter.rl.ac.uk/Problems/classification.shtml).
-It can be a number, a symbol, a string, or an array of those.
-
-    1, :none or "none" means there is no objective function;
-    2, :constant or "constant" means the objective function is a constant;
-    3, :linear or "linear" means the objective function is a linear functional;
-    4, :quadratic or "quadratic" means the objective function is quadratic;
-    5, :sum_of_squares or "sum_of_squares" means the objective function is a sum of squares
-    6, :other or "other" means the objective function is none of the above.
-
-- `contype` is the classification of the constraints according to the same
-  MASTSIF classification file.
-
-    1, :unc or "unc" means there are no constraints at all;
-    2, :fixed_vars or "fixed_vars" means the only constraints are fixed variables;
-    3, :bounds or "bounds" means the only constraints are bounded variables;
-    4, :network or "network" means the constraints represent the adjacency matrix of a (linear) network;
-    5, :linear or "linear" means the constraints are linear;
-    6, :quadratic or "quadratic" means the constraints are quadratic;
-    7, :other or "other" means the constraints are more general.
+## Keyword arguments
 
-- `custom_filter`: A function to apply additional filtering to the problem data. This data is provided as a dictionary with the following fields:
+- `min_var` and `max_var` set the number of variables in the problem;
 
-    - `"objtype"`: String representing the objective function type. It can be one of the following:
-      - `"none"`, `"constant"`, `"linear"`, `"quadratic"`, `"sum_of_squares"`, `"other"`
+- `min_con` and `max_con` set the number of constraints in the problem (use `max_con=0` for unconstrained or `min_con=1` for constrained);
 
-    - `"contype"`: String representing the constraint type. It can be one of the following:
-      - `"unc"`, `"fixed_vars"`, `"bounds"`, `"network"`, `"linear"`, `"quadratic"`, `"other"`
+- `only_*` flags are self-explaining. Note that they appear in conflicting pairs. Both can be false, but only one can be true.
 
-    - `"regular"`: Boolean indicating whether the problem is regular or not.
+- `objtype` is the classification of the objective function according to the [MASTSIF classification](https://www.cuter.rl.ac.uk/Problems/classification.shtml). It can be a number, a symbol, a string, or an array of those.
+  - 1, :none or "none" means there is no objective function;
+  - 2, :constant or "constant" means the objective function is a constant;
+  - 3, :linear or "linear" means the objective function is a linear functional;
+  - 4, :quadratic or "quadratic" means the objective function is quadratic;
+  - 5, :sum_of_squares or "sum_of_squares" means the objective function is a sum of squares;
+  - 6, :other or "other" means the objective function is none of the above.
 
-    - `"derivative_order"`: Integer representing the order of the highest derivative available.
+- `contype` is the classification of the constraints according to the same MASTSIF classification file.
+  - 1, :unc or "unc" means there are no constraints at all;
+  - 2, :fixed_vars or "fixed_vars" means the only constraints are fixed variables;
+  - 3, :bounds or "bounds" means the only constraints are bounded variables;
+  - 4, :network or "network" means the constraints represent the adjacency matrix of a (linear) network;
+  - 5, :linear or "linear" means the constraints are linear;
+  - 6, :quadratic or "quadratic" means the constraints are quadratic;
+  - 7, :other or "other" means the constraints are more general.
 
-    - `"origin"`: String indicating the origin of the problem. Possible values are `"academic"`, `"modelling"`, or `"real"`.
-
-    - `"has_interval_var"`: Boolean indicating whether the problem includes interval variables.
+- `custom_filter`: A function to apply additional filtering to the problem data. This data is provided as a dictionary with the following fields:
+  - `"objtype"`: String representing the objective function type. It can be one of the following:
+    - `"none"`, `"constant"`, `"linear"`, `"quadratic"`, `"sum_of_squares"`, `"other"`
 
-    - `"variables"`: Dictionary with fields related to variables:
-      - `"can_choose"`: Boolean indicating whether you can change the number of variables via parameters.
-      - `"number"`: Integer representing the number of variables (default if `"can_choose"` is true).
-      - `"fixed"`: Integer representing the number of fixed variables.
-      - `"free"`: Integer representing the number of free variables.
-      - `"bounded_below"`: Integer representing the number of variables bounded only from below.
-      - `"bounded_above"`: Integer representing the number of variables bounded only from above.
-      - `"bounded_both"`: Integer representing the number of variables bounded from both below and above.
+  - `"contype"`: String representing the constraint type. It can be one of the following:
+    - `"unc"`, `"fixed_vars"`, `"bounds"`, `"network"`, `"linear"`, `"quadratic"`, `"other"`
 
-    - `"constraints"`: Dictionary with fields related to constraints:
-      - `"can_choose"`: Boolean indicating whether you can change the number of constraints via parameters.
-      - `"number"`: Integer representing the number of constraints (default if `"can_choose"` is true).
-      - `"equality"`: Integer representing the number of equality constraints.
-      - `"ineq_below"`: Integer representing the number of inequalities of the form `c(x) ≥ cl`.
-      - `"ineq_above"`: Integer representing the number of inequalities of the form `c(x) ≤ cu`.
-      - `"ineq_both"`: Integer representing the number of inequalities of the form `cl ≤ c(x) ≤ cu`.
-      - `"linear"`: Integer representing the number of linear constraints.
-      - `"nonlinear"`: Integer representing the number of nonlinear constraints.
+  - `"regular"`: Boolean indicating whether the problem is regular or not.
+    - `"derivative_order"`: Integer representing the order of the highest derivative available.
 
-For instance, if you'd like to choose only problems with fixed number of variables, you can pass
+  - `"origin"`: String indicating the origin of the problem. Possible values are `"academic"`, `"modelling"`, or `"real"`.
+
+  - `"has_interval_var"`: Boolean indicating whether the problem includes interval variables.
+
+  - `"variables"`: Dictionary with fields related to variables:
+    - `"can_choose"`: Boolean indicating whether you can change the number of variables via parameters.
+    - `"number"`: Integer representing the number of variables (default if `"can_choose"` is true).
+    - `"fixed"`: Integer representing the number of fixed variables.
+    - `"free"`: Integer representing the number of free variables.
+    - `"bounded_below"`: Integer representing the number of variables bounded only from below.
+    - `"bounded_above"`: Integer representing the number of variables bounded only from above.
+    - `"bounded_both"`: Integer representing the number of variables bounded from both below and above.
+
+  - `"constraints"`: Dictionary with fields related to constraints:
+    - `"can_choose"`: Boolean indicating whether you can change the number of constraints via parameters.
+    - `"number"`: Integer representing the number of constraints (default if `"can_choose"` is true).
+    - `"equality"`: Integer representing the number of equality constraints.
+    - `"ineq_below"`: Integer representing the number of inequalities of the form `c(x) ≥ cl`.
+    - `"ineq_above"`: Integer representing the number of inequalities of the form `c(x) ≤ cu`.
+    - `"ineq_both"`: Integer representing the number of inequalities of the form `cl ≤ c(x) ≤ cu`.
+    - `"linear"`: Integer representing the number of linear constraints.
+    - `"nonlinear"`: Integer representing the number of nonlinear constraints.
 
 ```julia
-custom_filter = x -> x["variables"]["can_choose"] == false
+filtered_problems1 = select_sif_problems(; min_var=10, max_var=100, only_linear_con=true)
+filtered_problems2 = select_sif_problems(; max_con=0)
+filtered_problems3 = select_sif_problems(; min_con=1)
 ```
 """
 function select_sif_problems(;
@@ -114,12 +107,12 @@ function select_sif_problems(;
   max_con = Inf,
   objtype = objtypes,
   contype = contypes,
-  only_free_var = false,
-  only_bnd_var = false,
-  only_linear_con = false,
-  only_nonlinear_con = false,
-  only_equ_con = false,
-  only_ineq_con = false,
+  only_free_var::Bool = false,
+  only_bnd_var::Bool = false,
+  only_linear_con::Bool = false,
+  only_nonlinear_con::Bool = false,
+  only_equ_con::Bool = false,
+  only_ineq_con::Bool = false,
   custom_filter::Function = x -> true,
 )
   # Checks for conflicting option
@@ -137,9 +130,9 @@ function select_sif_problems(;
     error("contypes $contype not supported")
   end
 
-  data = JSON.parsefile(joinpath(dirname(@__FILE__), "classf.json"))
+  data = JSON.parsefile(joinpath(@__DIR__, "classf.json"))
   problems = keys(data)
-  selection = Vector{String}()
+  selection = String[]
   for p in problems
     pv = data[p]["variables"]
     pc = data[p]["constraints"]
@@ -164,6 +157,9 @@ function select_sif_problems(;
   return selection
 end
 
+# Keep an unexported function `select` to not break the tutorial
+select(; kwargs...) = select_sif_problems(; kwargs...)
+
 canonicalize_ftype(reqtype::Integer, allowedtypes) = [allowedtypes[reqtype]]
 canonicalize_ftype(reqtype::Symbol, allowedtypes) = [string(reqtype)]
 canonicalize_ftype(reqtype::AbstractString, allowedtypes) = [reqtype]
@@ -172,9 +168,6 @@ canonicalize_ftype(reqtype::AbstractVector{T}, allowedtypes) where {T <: Integer
 canonicalize_ftype(reqtype::AbstractVector{Symbol}, allowedtypes) = map(string, reqtype)
 canonicalize_ftype(reqtype, allowedtypes) = reqtype
 
-# Keep an unexported function `select` to not break the tutorial
-select(; kwargs...) = select_sif_problems(; kwargs...)
-
 """
     build_classification()