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Author: cvanaret

docs/getting_started.md

@@ -1,84 +1,83 @@
 # Getting started
 
-## In Python
-
-```python
-import unopy
-Inf = float("inf")
-
-# hs015.mod
-def objective(x):
-	return 100.*(x[1] - x[0]**2)**2 + (1. - x[0])**2
-	
-def constraints(x, constraint_values):
-	constraint_values[:] = [x[0]*x[1], x[0] + x[1]**2]
-	
-def objective_gradient(x, gradient):
-	gradient[:] = [400.*x[0]**3 - 400.*x[0]*x[1] + 2.*x[0] - 2.,
-				   200.*(x[1] - x[0]**2)]
-
-def jacobian(x, jacobian_values):
-	jacobian_values[:] = [x[1], 1., x[0], 2.*x[1]]
-
-def lagrangian_hessian(x, objective_multiplier, multipliers, hessian_values):
-	hessian_values[:] = [objective_multiplier*(1200*x[0]**2 - 400.*x[1] + 2.),
-						-400.*objective_multiplier*x[0] - multipliers[0],
-						200.*objective_multiplier - 2.*multipliers[1]]
-
-def lagrangian_hessian_operator(x, evaluate_at_x, objective_multiplier,
-                                multipliers, vector, result):
-	hessian00 = objective_multiplier*(1200*x[0]**2. - 400.*x[1] + 2.)
-	hessian10 = -400.*objective_multiplier*x[0] - multipliers[0]
-	hessian11 = 200.*objective_multiplier - 2.*multipliers[1]
-	result[:] = [hessian00*vector[0] + hessian10*vector[1],
-				hessian10*vector[0] + hessian11*vector[1]]
-
-if __name__ == '__main__':
-	number_variables = 2
-	number_constraints = 2
-
-	model = unopy.Model(unopy.PROBLEM_NONLINEAR, number_variables,
-                        unopy.ZERO_BASED_INDEXING)
-	model.set_variables_lower_bounds([-Inf, -Inf])
-	model.set_variables_upper_bounds([0.5, Inf])
-	model.set_objective(unopy.MINIMIZE, objective, objective_gradient)
-	model.set_constraints(number_constraints, constraints, [1., 0.], [Inf, Inf],
-                          4, [0, 1, 0, 1], [0, 0, 1, 1], jacobian)
-	model.set_lagrangian_hessian(3, unopy.LOWER_TRIANGLE, [0, 1, 1], [0, 0, 1],
-                                 lagrangian_hessian)
-    model.set_lagrangian_sign_convention(unopy.MULTIPLIER_NEGATIVE)
-	model.set_initial_primal_iterate([-2., 1.])
-
-	# solver creation
-	uno_solver = unopy.UnoSolver()
-	uno_solver.set_preset("filtersqp")
-
-	# solve with the filtersqp preset
-    print("Solving with Uno", unopy.current_uno_version())
-	result = uno_solver.optimize(model)
-	print("Objective at solution:", result.solution_objective)
-```
+=== "Python"
+
+    ```py
+    import unopy
+    Inf = float("inf")
+    
+    # hs015.mod
+    def objective(x):
+        return 100.*(x[1] - x[0]**2)**2 + (1. - x[0])**2
+        
+    def constraints(x, constraint_values):
+        constraint_values[:] = [x[0]*x[1], x[0] + x[1]**2]
+        
+    def objective_gradient(x, gradient):
+        gradient[:] = [400.*x[0]**3 - 400.*x[0]*x[1] + 2.*x[0] - 2.,
+                       200.*(x[1] - x[0]**2)]
+    
+    def jacobian(x, jacobian_values):
+        jacobian_values[:] = [x[1], 1., x[0], 2.*x[1]]
+    
+    def lagrangian_hessian(x, objective_multiplier, multipliers, hessian_values):
+        hessian_values[:] = [objective_multiplier*(1200*x[0]**2 - 400.*x[1] + 2.),
+                            -400.*objective_multiplier*x[0] - multipliers[0],
+                            200.*objective_multiplier - 2.*multipliers[1]]
+    
+    def lagrangian_hessian_operator(x, evaluate_at_x, objective_multiplier,
+                                    multipliers, vector, result):
+        hessian00 = objective_multiplier*(1200*x[0]**2. - 400.*x[1] + 2.)
+        hessian10 = -400.*objective_multiplier*x[0] - multipliers[0]
+        hessian11 = 200.*objective_multiplier - 2.*multipliers[1]
+        result[:] = [hessian00*vector[0] + hessian10*vector[1],
+                    hessian10*vector[0] + hessian11*vector[1]]
+    
+    if __name__ == '__main__':
+        number_variables = 2
+        number_constraints = 2
+    
+        model = unopy.Model(unopy.PROBLEM_NONLINEAR, number_variables,
+                            unopy.ZERO_BASED_INDEXING)
+        model.set_variables_lower_bounds([-Inf, -Inf])
+        model.set_variables_upper_bounds([0.5, Inf])
+        model.set_objective(unopy.MINIMIZE, objective, objective_gradient)
+        model.set_constraints(number_constraints, constraints, [1., 0.], [Inf, Inf],
+                              4, [0, 1, 0, 1], [0, 0, 1, 1], jacobian)
+        model.set_lagrangian_hessian(3, unopy.LOWER_TRIANGLE, [0, 1, 1], [0, 0, 1],
+                                     lagrangian_hessian)
+        model.set_lagrangian_sign_convention(unopy.MULTIPLIER_NEGATIVE)
+        model.set_initial_primal_iterate([-2., 1.])
+    
+        # solver creation
+        uno_solver = unopy.UnoSolver()
+        uno_solver.set_preset("filtersqp")
+    
+        # solve with the filtersqp preset
+        print("Solving with Uno", unopy.current_uno_version())
+        result = uno_solver.optimize(model)
+        print("Objective at solution:", result.solution_objective)
+    ```
+
+=== "Julia"
+
+    ```julia
+    using UnoSolver, JuMP
+    
+    jump_model = Model(() -> UnoSolver.Optimizer(preset="filtersqp"))
+    x0 = [-2, 1]
+    uvar = [0.5, Inf]
+    @variable(jump_model, x[i = 1:2] ≤ uvar[i], start = x0[i])
+    @objective(jump_model, Min, 100 * (x[2] - x[1]^2)^2 + (1 - x[1])^2)
+    @constraint(jump_model, x[1] * x[2] - 1 ≥ 0)
+    @constraint(jump_model, x[1] + x[2]^2 ≥ 0)
+    
+    optimize!(jump_model)
+    
+    termination_status(jump_model)  # solver termination status
+    objective_value(jump_model)     # objective value
+    value.(x)                       # primal solution
+    ```
 
 For more details, see the [Python documentation](./interfaces/python).
-
-## In Julia
-
-```julia
-using UnoSolver, JuMP
-
-jump_model = Model(() -> UnoSolver.Optimizer(preset="filtersqp"))
-x0 = [-2, 1]
-uvar = [0.5, Inf]
-@variable(jump_model, x[i = 1:2] ≤ uvar[i], start = x0[i])
-@objective(jump_model, Min, 100 * (x[2] - x[1]^2)^2 + (1 - x[1])^2)
-@constraint(jump_model, x[1] * x[2] - 1 ≥ 0)
-@constraint(jump_model, x[1] + x[2]^2 ≥ 0)
-
-optimize!(jump_model)
-
-termination_status(jump_model)  # solver termination status
-objective_value(jump_model)     # objective value
-value.(x)                       # primal solution
-```
-
 For more details, see the [Julia documentation](./interfaces/julia).

mkdocs.yml

@@ -4,6 +4,7 @@ theme:
   features:
     - navigation.footer
     - navigation.top
+    - content.code.copy
 
 nav:
   - Introduction: 'index.md'
@@ -35,6 +36,15 @@ markdown_extensions:
       generic: true
   - footnotes
   - admonition
+  - pymdownx.highlight:
+      anchor_linenums: true
+      line_spans: __span
+      pygments_lang_class: true
+  - pymdownx.inlinehilite
+  - pymdownx.snippets
+  - pymdownx.superfences
+  - pymdownx.tabbed:
+      alternate_style: true
 
 plugins:
   - gh-admonitions