v0.8.2

NEWS.md

@@ -1,5 +1,11 @@
 # News for EAGO Releases
 
+## [v0.8.2](https://github.com/PSORLab/EAGO.jl/releases/tag/v0.8.2) (October 27, 2024)
+- Added support for `MOI.ScalarNonlinearFunction`.
+  - Users can now define all constraints using `@constraint` instead of needing to use `@NLconstraint`. This applies to `@objective` as well.
+- Added support for variable names.
+- Updated display.
+
 ## [v0.8.1](https://github.com/PSORLab/EAGO.jl/releases/tag/v0.8.1) (June 15, 2023)
 
 - Resolved an issue where integer and binary variables would sometimes throw a `MathOptInterface.UpperBoundAlreadySet` error.

Project.toml

@@ -1,7 +1,7 @@
 name = "EAGO"
 uuid = "bb8be931-2a91-5aca-9f87-79e1cb69959a"
 authors = ["Matthew Wilhelm <[email protected]>, Robert Gottlieb <[email protected]>, Dimitri Alston <[email protected]>, and Matthew Stuber <[email protected]>"]
-version = "0.8.1"
+version = "0.8.2"
 
 [deps]
 Cassette = "7057c7e9-c182-5462-911a-8362d720325c"

README.md

@@ -104,6 +104,12 @@ The EAGO package has numerous features: a solver accessible from JuMP/MathOptInt
 
 ## Recent News
 
+### [v0.8.2](https://github.com/PSORLab/EAGO.jl/releases/tag/v0.8.2) (October 27, 2024)
+- Added support for `MOI.ScalarNonlinearFunction`.
+  - Users can now define all constraints using `@constraint` instead of needing to use `@NLconstraint`. This applies to `@objective` as well.
+- Added support for variable names.
+- Updated display.
+
 ### [v0.8.1](https://github.com/PSORLab/EAGO.jl/releases/tag/v0.8.1) (June 15, 2023)
 
 - Resolved an issue where integer and binary variables would sometimes throw a `MathOptInterface.UpperBoundAlreadySet` error.

docs/src/index.md

@@ -11,7 +11,7 @@ A development environment for robust and global optimization in Julia.
   - Current Position: Alexion Pharmaceuticals
 - [Robert Gottlieb](https://psor.uconn.edu/person/robert-gottlieb/), Department of Chemical and Biomolecular Engineering, University of Connecticut (UConn)
 - [Dimitri Alston](https://psor.uconn.edu/person/dimitri-alston/), Department of Chemical and Biomolecular Engineering, University of Connecticut (UConn)
-- [Matthew Stuber](https://chemical-biomolecular.engr.uconn.edu/people/faculty/stuber-matthew/), Associate Professor, University of Connecticut (UConn)
+- [Matthew Stuber](https://chemical-biomolecular.engr.uconn.edu/people/faculty/stuber-matthew/), Pratt & Whitney Associate Professor in Advanced Systems Engineering, University of Connecticut (UConn)
 
 If you would like to contribute, [contact us](@ref "How to Contribute to EAGO").
 

docs/src/news.md

@@ -1,5 +1,11 @@
 # News for EAGO Releases
 
+## [v0.8.2](https://github.com/PSORLab/EAGO.jl/releases/tag/v0.8.2) (October 27, 2024)
+- Added support for `MOI.ScalarNonlinearFunction`.
+  - Users can now define all constraints using `@constraint` instead of needing to use `@NLconstraint`. This applies to `@objective` as well.
+- Added support for variable names.
+- Updated display.
+
 ## [v0.8.1](https://github.com/PSORLab/EAGO.jl/releases/tag/v0.8.1) (June 15, 2023)
 
 - Resolved an issue where integer and binary variables would sometimes throw a `MathOptInterface.UpperBoundAlreadySet` error.

src/eago_optimizer/optimize/nonconvex/configure_subsolver.jl

@@ -14,15 +14,16 @@
 
 function set_default_config_udf!(s, m::MOI.AbstractOptimizer, verbosity::Int)
     if verbosity > 0
-        println("EAGO lacks a specialized configuration routine for the subsolver ($(MOI.get(m, MOI.SolverName())))")
-        println("you selected. As a result, EAGO cannot set the subsolver tolerances based on the")
-        println("absolute_tolerance, relative_tolerance, and absolute_constraint_feas_tolerance")
-        println("parameters passed to the EAGO optimizer. Consequently, need to ensure that the tolerances")
-        println("set in the provided subsolver are appropriate (for instance if the absolute_tolerance = 1E-3")
-        println("then the absolute tolerance for a subsolver should be < 1E-4 and any feasibility tolerances")
-        println("should be as conservative as the absolute_constraint_feas_tolerance). If you see this message")
-        println("please submit an issue at https://github.com/PSORLab/EAGO.jl/issues/new/choose requesting")
-        println("that a configuration routine be added for this subsolver.")
+        @warn("""
+        EAGO lacks a specialized configuration routine for the subsolver ($(MOI.get(m, MOI.SolverName())))
+        you selected. As a result, EAGO cannot set the subsolver tolerances based on the
+        absolute_tolerance, relative_tolerance, and absolute_constraint_feas_tolerance
+        parameters passed to the EAGO optimizer. Consequently, you need to ensure that the tolerances
+        set in the provided subsolver are appropriate (for instance if the absolute_tolerance = 1E-3
+        then the absolute tolerance for a subsolver should be < 1E-4 and any feasibility tolerances
+        should be as conservative as the absolute_constraint_feas_tolerance). If you see this message
+        please submit an issue at https://github.com/PSORLab/EAGO.jl/issues/new/choose requesting
+        that a configuration routine be added for this subsolver.""")
     end
     return
 end

src/eago_optimizer/optimize/nonconvex/display.jl

@@ -29,7 +29,7 @@ function print_solution!(m::GlobalOptimizer)
         elseif m._end_state == GS_NODE_LIMIT
             println("Node Limit Exceeded")
         elseif m._end_state == GS_ITERATION_LIMIT
-            println("Maximum Iteration Exceeded")
+            println("Iteration Limit Exceeded")
         elseif m._end_state == GS_RELATIVE_TOL
             println("Relative Tolerance Achieved")
         elseif m._end_state == GS_ABSOLUTE_TOL
@@ -39,24 +39,14 @@ function print_solution!(m::GlobalOptimizer)
         end
         if m._end_state == GS_OPTIMAL || m._end_state == GS_RELATIVE_TOL || m._end_state == GS_ABSOLUTE_TOL
             println("Optimal Solution Found at Node $(m._solution_node)")
-            if !_is_input_min(m)
-                println("Lower Bound: $(MOI.get(m, MOI.ObjectiveBound()))")
-                println("Upper Bound: $(MOI.get(m, MOI.ObjectiveValue()))")
-            else
-                println("Lower Bound: $(MOI.get(m, MOI.ObjectiveBound()))")
-                println("Upper Bound: $(MOI.get(m, MOI.ObjectiveValue()))")
-            end
+            println("Lower Bound: $(MOI.get(m, MOI.ObjectiveBound()))")
+            println("Upper Bound: $(MOI.get(m, MOI.ObjectiveValue()))")
         elseif m._end_state == GS_INFEASIBLE
             println("No Solution Found")
         else
             println("Best Solution Found at Node $(m._solution_node)")
-            if !_is_input_min(m)
-                println("Lower Bound: $(MOI.get(m, MOI.ObjectiveBound()))")
-                println("Upper Bound: $(MOI.get(m, MOI.ObjectiveValue()))")
-            else
-                println("Lower Bound: $(MOI.get(m, MOI.ObjectiveBound()))")
-                println("Upper Bound: $(MOI.get(m, MOI.ObjectiveValue()))")
-            end
+            println("Lower Bound: $(MOI.get(m, MOI.ObjectiveBound()))")
+            println("Upper Bound: $(MOI.get(m, MOI.ObjectiveValue()))")
         end
         if m._feasible_solution_found
             println("Solution:")
@@ -68,7 +58,7 @@ function print_solution!(m::GlobalOptimizer)
             addlen = maxlen .- length.(variable_names)
             print_list = " ".^addlen.*variable_names
             for i = 1:m._input_problem._variable_count
-            println("  $(print_list[i]) = $(m._continuous_solution[i])")
+            println("   $(print_list[i]) = $(m._continuous_solution[i])")
             end
         end
         println(" ")
@@ -106,22 +96,22 @@ Print status information based on iteration count. The header print frequency is
 based on the `header_iterations` setting, and the data print frequency is based on
 the `output_iterations` setting.
 """
-function print_iteration!(m::GlobalOptimizer)
+function print_iteration!(m::GlobalOptimizer, end_flag::Bool)
 
     if _verbosity(m) > 0
 
         # Print header line every `header_iterations` times and print iteration summary every `output_iterations` times
-        if mod(m._iteration_count, m._parameters.output_iterations) === 0
+        if m._last_printed_iteration != m._iteration_count && (mod(m._iteration_count, m._parameters.output_iterations) === 0 || end_flag)
             if m._iteration_count == m._parameters.output_iterations || mod(m._iteration_count, m._parameters.header_iterations) < m._parameters.output_iterations
                 println("---------------------------------------------------------------------------------------------------------------------------------")
                 println("|  Iteration #  |     Nodes     |  Lower Bound  |  Upper Bound  |      Gap      |     Ratio     |     Timer     |   Time Left   |")
                 println("---------------------------------------------------------------------------------------------------------------------------------")
             end
             # Print start
-            print_str = "|  "
+            print_str = "| "
 
             # Print iteration number
-            max_len = 12
+            max_len = 13
             temp_str = string(m._iteration_count)
             len_str = length(temp_str)
             print_str *= (" "^(max_len - len_str))*temp_str*" | "
@@ -132,24 +122,15 @@ function print_iteration!(m::GlobalOptimizer)
             len_str = length(temp_str)
             print_str *= (" "^(max_len - len_str))*temp_str*" | "
 
-            # Determine lower and upper bound
-            if _is_input_min(m)
-                lower = m._global_lower_bound
-                upper = m._global_upper_bound
-            else
-                lower = m._global_lower_bound
-                upper = m._global_upper_bound
-            end
-
             # Print lower bound
             max_len = 13
-            temp_str = @sprintf "%.3E" lower
+            temp_str = @sprintf "%.3E" m._global_lower_bound
             len_str = length(temp_str)
             print_str *= (" "^(max_len - len_str))*temp_str*" | "
 
             # Print upper bound
             max_len = 13
-            temp_str = @sprintf "%.3E" upper
+            temp_str = @sprintf "%.3E" m._global_upper_bound
             len_str = length(temp_str)
             print_str *= (" "^(max_len - len_str))*temp_str*" | "
 
@@ -167,17 +148,23 @@ function print_iteration!(m::GlobalOptimizer)
 
             # Print run time
             max_len = 13
-            temp_str = @sprintf "%.3E" m._run_time
+            temp_str = @sprintf "%.2F" m._run_time
             len_str = length(temp_str)
             print_str *= (" "^(max_len - len_str))*temp_str*" | "
 
             # Print time remaining
             max_len = 13
-            temp_str = @sprintf "%.3E" m._time_left
+            temp_str = @sprintf "%.2F" m._time_left
             len_str = length(temp_str)
             print_str *= (" "^(max_len - len_str))*temp_str*" |"
 
             println(print_str)
+
+            # Update printed iteration
+            m._last_printed_iteration = m._iteration_count
+        end
+        if end_flag
+            println("---------------------------------------------------------------------------------------------------------------------------------")
         end
     end
 
@@ -225,8 +212,8 @@ end
 $(FUNCTIONNAME)
 
 Print noteworthy information prior to running branch-and-bound. Currently prints
-a note about flipping `max(f)` to `-min(-f)` internally, if a maximization problem
-is inputted and `verbosity>=3`.
+a note about flipping `max(f)` to `-min(-f)` internally, if the input is a 
+maximization problem and `verbosity>=3`.
 """
 function print_preamble!(m::GlobalOptimizer)
     if _verbosity(m) >= 3

src/eago_optimizer/optimize/optimize_nonconvex.jl

@@ -372,8 +372,7 @@ Pseudocode description of the algorithm, as implemented here:
 """
 function global_solve!(m::GlobalOptimizer)
 
-    # Set counts to 1
-    m._iteration_count = 1
+    # Set initial node count
     m._node_count = 1
 
     # Prepare to run branch-and-bound
@@ -384,6 +383,8 @@ function global_solve!(m::GlobalOptimizer)
     # Run branch and bound; terminate when the stack is empty or when some
     # tolerance or limit is hit
     while !termination_check(m)
+        # Update iteration counter
+        m._iteration_count += 1
 
         # Fathom nodes from the stack, then pick a node and temporarily remove
         # it from the stack
@@ -440,10 +441,9 @@ function global_solve!(m::GlobalOptimizer)
         m._run_time = time() - m._start_time
         m._time_left = m._parameters.time_limit - m._run_time
 
-        # Log and print information as needed and update the iteration counter
+        # Log and print information as needed
         log_iteration!(m)
-        print_iteration!(m)
-        m._iteration_count += 1
+        print_iteration!(m, false)
     end
 
     # Since the algorithm has terminated, convert EAGO's end status into
@@ -452,6 +452,7 @@ function global_solve!(m::GlobalOptimizer)
     set_result_status!(m)
 
     # Print final information about the solution
+    print_iteration!(m, true)
     print_solution!(m)
 end
 

src/eago_optimizer/types/global_optimizer.jl

@@ -706,6 +706,8 @@ Base.@kwdef mutable struct GlobalOptimizer{Q,S,T<:ExtensionType} <: MOI.Abstract
     _last_upper_problem_time::Float64 = 0.0
     "Updated each iteration to track the time of the postprocess step"
     _last_postprocessing_time::Float64 = 0.0
+    "Updated each time an iteration is printed"
+    _last_printed_iteration::Int = 0
 
     # Reset in initial_parse! in parse.jl
     "A field to track convergence progress across iterations"

test/optimizer.jl

@@ -716,7 +716,8 @@ end
     end
     @test_nowarn EAGO.print_results!(m, true)
     @test_nowarn EAGO.print_results!(m, false)
-    @test_nowarn EAGO.print_iteration!(m)
+    @test_nowarn EAGO.print_iteration!(m, false)
+    @test_nowarn EAGO.print_iteration!(m, true)
     @test_nowarn EAGO.print_node!(m)
     @test_nowarn EAGO.print_problem_summary!(d, "Display Test")
 end