start cleaning out the various names for optimization

docs/src/examples/02c-constraint-modifications.jl

@@ -43,7 +43,12 @@ fermentation = ctmodel.fluxes.EX_ac_e.value + ctmodel.fluxes.EX_etoh_e.value
 forced_mixed_fermentation =
     ctmodel * :fermentation^C.Constraint(fermentation, (10.0, 1000.0)) # new modified model is created
 
-vt = flux_balance(forced_mixed_fermentation, Tulip.Optimizer; modifications = [silence])
+vt = optimized_constraints(
+    forced_mixed_fermentation,
+    objective = forced_mixed_fermentation.objective.value,
+    optimizer = Tulip.Optimizer,
+    modifications = [silence],
+)
 
 @test isapprox(vt.objective, 0.6337, atol = TEST_TOLERANCE) #src
 
@@ -52,13 +57,25 @@ vt = flux_balance(forced_mixed_fermentation, Tulip.Optimizer; modifications = [s
 
 ctmodel.fluxes.ATPM.bound = (1000.0, 10000.0)
 
-vt = flux_balance(ctmodel, Tulip.Optimizer; modifications = [silence])
+#TODO explicitly show here how false sharing looks like
+
+vt = optimized_constraints(
+    ctmodel,
+    objective = ctmodel.objective.value,
+    optimizer = Tulip.Optimizer,
+    modifications = [silence],
+)
 
 @test isnothing(vt) #src
 
 # Models can also be piped into the analysis functions
 
 ctmodel.fluxes.ATPM.bound = (8.39, 10000.0) # revert
-vt = ctmodel |> flux_balance(Tulip.Optimizer; modifications = [silence])
+vt = optimized_constraints(
+    ctmodel,
+    objective = ctmodel.objective.value,
+    optimizer = Tulip.Optimizer,
+    modifications = [silence],
+)
 
 @test isapprox(vt.objective, 0.8739, atol = TEST_TOLERANCE) #src

docs/src/examples/03-qp-problems.jl

@@ -1,8 +1,8 @@
 
 # # Quandratic objective flux balance analysis type problems
 
-# We will use [`parsimonious_flux_balance_analysis`](@ref) and
-# [`minimize_metabolic_adjustment_analysis`](@ref) to find the optimal flux
+# We will use [`parsimonious_flux_balance`](@ref) and
+# [`minimize_metabolic_adjustment`](@ref) to find the optimal flux
 # distribution in the *E. coli* "core" model.
 
 # If it is not already present, download the model and load the package:
@@ -22,11 +22,11 @@ model = A.load(J.JSONFBCModel, "e_coli_core.json") # load the model
 
 # Use the convenience function to run standard pFBA
 
-vt = X.parsimonious_flux_balance_analysis(model, Clarabel.Optimizer)
+vt = X.parsimonious_flux_balance(model, Clarabel.Optimizer)
 
 # Or use the piping functionality
 
-model |> parsimonious_flux_balance_analysis(Clarabel.Optimizer; modifications = [X.silence])
+model |> parsimonious_flux_balance(Clarabel.Optimizer; modifications = [X.silence])
 
 @test isapprox(vt.objective, 0.87392; atol = TEST_TOLERANCE) #src
 @test isapprox(sum(x^2 for x in values(vt.fluxes)), 11414.2119; atol = QP_TEST_TOLERANCE) #src
@@ -57,15 +57,12 @@ vt = X.C.constraint_values(ctmodel, X.J.value.(opt_model[:x])) # ConstraintTrees
 
 ref_sol = Dict("ATPS4r" => 33.0, "CYTBD" => 22.0)
 
-vt = X.minimize_metabolic_adjustment_analysis(model, ref_sol, Gurobi.Optimizer)
+vt = X.minimize_metabolic_adjustment(model, ref_sol, Gurobi.Optimizer)
 
 # Or use the piping functionality
 
-model |> X.minimize_metabolic_adjustment_analysis(
-    ref_sol,
-    Clarabel.Optimizer;
-    modifications = [X.silence],
-)
+model |>
+X.minimize_metabolic_adjustment(ref_sol, Clarabel.Optimizer; modifications = [X.silence])
 
 @test isapprox(vt.:momaobjective, 0.81580806; atol = TEST_TOLERANCE) #src
 

src/analysis/flux_balance.jl

@@ -32,7 +32,7 @@ solution = flux_balance(model, GLPK.optimizer)
 """
 function flux_balance(model::A.AbstractFBCModel, optimizer; kwargs...)
     constraints = fbc_model_constraints(model)
-    optimize_constraints(
+    optimized_constraints(
         constraints;
         objective = constraints.objective.value,
         optimizer,

src/solver.jl

@@ -56,7 +56,7 @@ Make an JuMP model out of `constraints` using [`optimization_model`](@ref)
 the model, and return either `nothing` if the optimization failed, or `output`
 substituted with the solved values (`output` defaults to `constraints`.
 """
-function optimize_constraints(
+function optimized_constraints(
     constraints::C.ConstraintTreeElem,
     args...;
     modifications = [],
@@ -70,3 +70,5 @@ function optimize_constraints(
     J.optimize!(om)
     is_solved(om) ? C.constraint_values(output, J.value.(om[:x])) : nothing
 end
+
+export optimized_constraints