Switch Nonlinear API to JuMP.GenericNonlinearExpr

Project.toml

@@ -4,33 +4,28 @@ authors = ["Joshua Pulsipher and Weiqi Zhang"]
 version = "0.5.8"
 
 [deps]
-AbstractTrees = "1520ce14-60c1-5f80-bbc7-55ef81b5835c"
 DataStructures = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 FastGaussQuadrature = "442a2c76-b920-505d-bb47-c5924d526838"
 JuMP = "4076af6c-e467-56ae-b986-b466b2749572"
-LeftChildRightSiblingTrees = "1d6d02ad-be62-4b6b-8a6d-2f90e265016e"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 MutableArithmetics = "d8a4904e-b15c-11e9-3269-09a3773c0cb0"
 Reexport = "189a3867-3050-52da-a836-e630ba90ab69"
-SpecialFunctions = "276daf66-3868-5448-9aa4-cd146d93841b"
 
 [compat]
-AbstractTrees = "0.4"
 DataStructures = "0.14.2 - 0.18"
 Distributions = "0.19 - 0.25"
 FastGaussQuadrature = "0.3.2 - 0.4, 0.5"
-JuMP = "1.2"
-LeftChildRightSiblingTrees = "0.2"
+JuMP = "1.5"
 MutableArithmetics = "1"
 Reexport = "0.2, 1"
-SpecialFunctions = "0.8 - 0.10, 1, 2"
 julia = "^1.6"
 
 [extras]
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Suppressor = "fd094767-a336-5f1f-9728-57cf17d0bbfb"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
+Pkg = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
 
 [targets]
-test = ["Test", "Random", "Suppressor"]
+test = ["Test", "Random", "Suppressor", "Pkg"]

docs/Project.toml

@@ -10,15 +10,16 @@ Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 SpecialFunctions = "276daf66-3868-5448-9aa4-cd146d93841b"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
+Pkg = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
 
 [compat]
-HiGHS = "1"
 Distributions = "0.25"
 Documenter = "0.27"
 InfiniteOpt = "0.5"
-JuMP = "^1.11.1"
-Ipopt = "1"
+Ipopt = "1.4"
+HiGHS = "1"
+julia = "1.6"
+JuMP = "1.15"
 Literate = "2.14"
 Plots = "1"
-julia = "1.6"
 SpecialFunctions = "2"

docs/src/develop/extensions.md

@@ -871,23 +871,19 @@ function _make_expression(
     return _make_expression(opt_model, measure_function(expr))
 end
 # AffExpr/QuadExpr
-function _make_expression(opt_model::Model, expr::Union{GenericAffExpr, GenericQuadExpr})
+function _make_expression(opt_model::Model, expr::Union{GenericAffExpr, GenericQuadExpr, GenericNonlinearExpr})
     return map_expression(v -> _make_expression(opt_model, v), expr)
 end
-# NLPExpr
-function _make_expression(opt_model::Model, expr::NLPExpr)
-    return add_nonlinear_expression(opt_model, map_nlp_to_ast(v -> _make_expression(opt_model, v), expr))
-end
 
 # output
-_make_expression (generic function with 8 methods)
+_make_expression (generic function with 7 methods)
 
 ```
 For simplicity in example, above we assume that only `DistributionDomain`s are 
 used, there are not any `PointVariableRef`s, and all `MeasureRef`s correspond to 
 expectations. Naturally, a full extension should include checks to enforce that 
-such assumptions hold. Notice that [`map_expression`](@ref) and 
-[`map_nlp_to_ast`](@ref) are useful for converting expressions.
+such assumptions hold. Notice that [`map_expression`](@ref) is useful for 
+converting expressions.
 
 Now let's extend [`build_optimizer_model!`](@ref) for `DeterministicModel`s. 
 Such extensions should build an optimizer model in place and in general should 
@@ -908,13 +904,13 @@ function InfiniteOpt.build_optimizer_model!(
     # clear the model for a build/rebuild
     determ_model = InfiniteOpt.clear_optimizer_model_build!(model)
 
-    # add the registered functions if there are any
-    add_registered_to_jump(determ_model, model)
+    # add user-defined nonlinear operators if there are any
+    add_operators_to_jump(determ_model, model)
 
     # add variables
     for vref in all_variables(model)
         if index(vref) isa InfiniteVariableIndex
-            start = NaN # easy hack
+            start = NaN # simple hack for sake of example
         else
             start = start_value(vref)
             start = isnothing(start) ? NaN : start
@@ -932,29 +928,14 @@ function InfiniteOpt.build_optimizer_model!(
 
     # add the objective
     obj_func = _make_expression(determ_model, objective_function(model))
-    if obj_func isa NonlinearExpression
-        set_nonlinear_objective(determ_model, objective_sense(model), obj_func)
-    else
-        set_objective(determ_model, objective_sense(model), obj_func)
-    end
+    set_objective(determ_model, objective_sense(model), obj_func)
 
     # add the constraints
-    for cref in all_constraints(model, Union{GenericAffExpr, GenericQuadExpr, NLPExpr})
+    for cref in all_constraints(model, Union{GenericAffExpr, GenericQuadExpr, GenericNonlinearExpr})
         constr = constraint_object(cref)
         new_func = _make_expression(determ_model, constr.func)
-        if new_func isa NonlinearExpression
-            if constr.set isa MOI.LessThan
-                ex = :($new_func <= $(constr.set.upper))
-            elseif constr.set isa MOI.GreaterThan
-                ex = :($new_func >= $(constr.set.lower))
-            else # assume it is MOI.EqualTo
-                ex = :($new_func == $(constr.set.value))
-            end
-            new_cref = add_nonlinear_constraint(determ_model, ex)
-        else
-            new_constr = build_constraint(error, new_func, constr.set)
-            new_cref = add_constraint(determ_model, new_constr, name(cref))
-        end
+        new_constr = build_constraint(error, new_func, constr.set)
+        new_cref = add_constraint(determ_model, new_constr, name(cref))
         deterministic_data(determ_model).infconstr_to_detconstr[cref] = new_cref
     end
 
@@ -975,13 +956,11 @@ print(optimizer_model(model))
 # output
 Min z + y[1] + y[2]
 Subject to
- 2 y[1] - z ≤ 42.0
+ sin(z) - -1.0 ≥ 0
+ 2 y[1] - z ≤ 42
  y[2]² = 1.5
- y[1] ≥ 0.0
- y[2] ≥ 0.0
- subexpression[1] - 0.0 ≥ 0
-With NL expressions
- subexpression[1]: sin(z) - -1.0
+ y[1] ≥ 0
+ y[2] ≥ 0
 ```
 Note that better variable naming could be used with the reformulated infinite 
 variables. Moreover, in general extensions of [`build_optimizer_model!`](@ref) 

docs/src/guide/derivative.md

@@ -53,7 +53,7 @@ julia> d3 = @∂(q, t^2)
 ∂/∂t[∂/∂t[q(t)]]
 
 julia> d_expr = @deriv(y * q - 2t, t)
-∂/∂t[y(t, ξ)]*q(t) + ∂/∂t[q(t)]*y(t, ξ) - 2
+∂/∂t[y(t, ξ)]*q(t) + y(t, ξ)*∂/∂t[q(t)] - 2
 ```
 Thus, we can define derivatives in a variety of forms according to the problem at 
 hand. The last example even shows how the product rule is correctly applied. 
@@ -216,7 +216,7 @@ defined up above with its alias name `dydt2`. This macro can also tackle complex
 expressions using the appropriate calculus such as:
 ```jldoctest deriv_basic 
 julia> @deriv(∫(y, ξ) * q, t)
-∂/∂t[∫{ξ ∈ [-1, 1]}[y(t, ξ)]]*q(t) + ∂/∂t[q(t)]*∫{ξ ∈ [-1, 1]}[y(t, ξ)]
+∂/∂t[∫{ξ ∈ [-1, 1]}[y(t, ξ)]]*q(t) + ∫{ξ ∈ [-1, 1]}[y(t, ξ)]*∂/∂t[q(t)]
 ```
 Thus, demonstrating the convenience of using `@deriv`.