v0.2.7 updates

CHANGELOG.md

@@ -1,5 +1,12 @@
 # Alpine.jl Change Log
 
+## v0.2.7
+- Support for module shortcut `Alp` in function calls
+- Solver options clean up in `examples` folder
+- Dropped support for unsued functions `_bound_sense`, `update_boundstop_options`
+- Improved code coverage
+- Docs cleanup
+
 ## v0.2.6
 - Bug fix in `algorithm.jl` to handle Gurobi as the MIP solver with updated MOI 
 - Deactivated redundant functions `collect_lb_pool`, `merge_solution_pool` and `track_new_partition_idx`

Project.toml

@@ -2,7 +2,7 @@ name = "Alpine"
 uuid = "07493b3f-dabb-5b16-a503-4139292d7dd4"
 authors = ["Harsha Nagarajan, Site Wang, Kaarthik Sundar and contributors"]
 repo = "https://github.com/lanl-ansi/Alpine.jl.git"
-version = "0.2.6"
+version = "0.2.7"
 
 [deps]
 JuMP = "4076af6c-e467-56ae-b986-b466b2749572"

docs/src/choosingsolver.md

@@ -18,13 +18,13 @@ Alpine's global optimization algorithm requires a few types of optimization prob
 As the development of Alpine continues, supports for solvers such as [Mosek](http://www.mosek.com/), [GLPK](http://www.gnu.org/software/glpk/), [NLopt](http://ab-initio.mit.edu/wiki/index.php/NLopt) and [Xpress](http://www.fico.com/en/products/fico-xpress-optimization-suite) are in the development roadmap.
 
 !!! tip
-    Performance of Alpine is significantly faster and robust while using [Gurobi](https://www.gurobi.com) as the convex mixed-integer programming (MIP) solver. Note that this solver's individual-usage license is available [free](https://www.gurobi.com/academia/academic-program-and-licenses/) for academic purposes. 
+    Performance of Alpine is significantly faster and robust while using [Gurobi](https://www.gurobi.com) as the underlying convex mixed-integer programming (MIP) solver. Note that Gurobi's individual-usage license is available [free](https://www.gurobi.com/academia/academic-program-and-licenses/) for academic purposes. 
 
-To solve nonlinear program with only continuous variables to global optimality, here is an example to utilize different sub-solvers for the best performance of Alpine:
+To solve any nonlinear program with only continuous variables to global optimality, here is an example to utilize different sub-solvers with default options set for Alpine:
 
 ```julia
-using JuMP
-using Alpine 
+using Alpine
+using JuMP 
 using Gurobi 
 using Ipopt
 

examples/run_examples.jl

@@ -3,54 +3,27 @@ using CPLEX
 using Gurobi
 using JuMP 
 using Ipopt
-using Cbc
-using Pavito
 using Juniper
+# using Cbc
+# using Pavito
 
 include("JuMP_models.jl")
+include("solver.jl")
 
-# MIP solver - commercial 
-const gurobi = optimizer_with_attributes(Gurobi.Optimizer, 
-                                        MOI.Silent() => true, 
-                                        "Presolve" => 1) 
 
-const cplex = optimizer_with_attributes(CPLEX.Optimizer, 
-                                        MOI.Silent() => true, 
-                                        "CPX_PARAM_PREIND" => false) 
-
-# MIP solver - open-source
-const cbc = optimizer_with_attributes(Cbc.Optimizer, 
-                                      MOI.Silent() => true) 
-
-# Choose the MIP solver here (for Alpine/Juniper)
-mip_solver = gurobi  
-
-# Local solver
-const ipopt = optimizer_with_attributes(Ipopt.Optimizer, 
-                                        MOI.Silent() => true, 
-                                        "sb" => "yes", 
-                                        "max_iter" => 9999)
-
-# Convex MINLP solver
-const pavito = optimizer_with_attributes(Pavito.Optimizer, 
-                                        MOI.Silent() => true,
-                                        "mip_solver" => cbc, 
-                                        "cont_solver" => ipopt, 
-                                        "mip_solver_drives" => false)
-
-const juniper = optimizer_with_attributes(Juniper.Optimizer, 
-                                          MOI.Silent() => true, 
-                                          "mip_solver" => mip_solver, 
-                                          "nl_solver" => ipopt)
+# Choose underlying solvers for Alpine
+mip_solver = get_gurobi()
+nlp_solver = get_ipopt()
+minlp_solver = get_juniper(mip_solver, nlp_solver)
 
 # Global solver
 const alpine = optimizer_with_attributes(Alpine.Optimizer, 
-                                         "minlp_solver" => juniper,
-                                         "nlp_solver" => ipopt,  
-                                         "mip_solver" => mip_solver,
-                                         "presolve_bt" => true,
-                                         "presolve_bt_max_iter" => 5,
-                                         "disc_ratio" => 10)
+                                        #  "minlp_solver" => minlp_solver,
+                                        "nlp_solver" => nlp_solver,  
+                                        "mip_solver" => mip_solver,
+                                        "presolve_bt" => true,
+                                        "presolve_bt_max_iter" => 5,
+                                        "disc_ratio" => 10)
 
 # Try different integer values ( >=4 ) for `disc_ratio` to have better Alpine run times 
 # Choose `presolve_bt` to `false` if you prefer the OBBT presolve to be turned off. 

examples/solver.jl

@@ -0,0 +1,43 @@
+# MIP solver - commercial 
+function get_gurobi()
+    return optimizer_with_attributes(Gurobi.Optimizer, 
+                                            MOI.Silent() => true, 
+                                            "Presolve" => 1) 
+end
+
+function get_cplex()
+    return optimizer_with_attributes(CPLEX.Optimizer, 
+                                            MOI.Silent() => true, 
+                                            "CPX_PARAM_PREIND" => 1) 
+end
+
+# MIP solver - open-source
+function get_cbc()
+    return optimizer_with_attributes(Cbc.Optimizer, 
+                                     MOI.Silent() => true) 
+end
+
+# Local solver
+function get_ipopt()
+    return optimizer_with_attributes(Ipopt.Optimizer, 
+                                    MOI.Silent() => true, 
+                                    "sb" => "yes", 
+                                    "max_iter" => Int(1E4))
+end
+
+# Convex MINLP solver
+function get_pavito(mip_solver, cont_solver)
+    return optimizer_with_attributes(Pavito.Optimizer, 
+                                    MOI.Silent() => true,
+                                    "mip_solver" => mip_solver, 
+                                    "cont_solver" => cont_solver, 
+                                    "mip_solver_drives" => false)
+end
+
+# Non-convex Local MINLP solver
+function get_juniper(mip_solver, nl_solver)
+    return optimizer_with_attributes(Juniper.Optimizer, 
+                                    MOI.Silent() => true, 
+                                    "mip_solver" => mip_solver, 
+                                    "nl_solver" => nl_solver)
+end

src/Alpine.jl

@@ -3,17 +3,17 @@ __precompile__()
 module Alpine
 
 using JuMP
-using LinearAlgebra
-using Random
-using Statistics
+
+import LinearAlgebra as LA
+import Random
+import Statistics
 
 const ALPINE_DEBUG = false
-const ALP = Alpine
-const LA = LinearAlgebra
+const Alp = Alpine
 
 include("const.jl")
 
-# Engine for High-level Algorithmic Control and User-interface
+# Engine for high-level algorithmic control and user-interface
 include("matrix_opt_interface.jl")
 include("moi_function2expr.jl")
 include("solver.jl")
@@ -22,7 +22,7 @@ include("solver.jl")
 include("nlexpr.jl")
 include("operators.jl")
 
-# Main Algorithm
+# Main algorithm
 include("algorithm.jl")
 include("presolve.jl")
 include("amp.jl")
@@ -37,7 +37,7 @@ include("tmc.jl")
 include("bounds.jl")
 include("utility.jl")
 
-# Othes
+# Logging
 include("log.jl")
 
 end # module