maxtime_cpu is not respected

[chengg04]

This bug is similar to the one mentioned here: https://discourse.julialang.org/t/julia-with-knitro/81686.

A MWE is as follows:

using JuMP, KNITRO, PowerModels

minlp_solver = JuMP.optimizer_with_attributes(KNITRO.Optimizer, "maxtime_cpu" => 30)

solve_ots("pglib_opf_case24_ieee_rts__sad.m", ACPPowerModel, minlp_solver)

The data file pglib_opf_case24_ieee_rts__sad.m is attached below (drop the extension .txt before running).

The code runs fine if one does not set the maxtime_cpu parameter in attributes, so it seems to be an issue with KNITRO.jl/MOI not being able to talk the parameter.

pglib_opf_case24_ieee_rts__sad.m.txt

[odow]

Use:

minlp_solver = JuMP.optimizer_with_attributes(KNITRO.Optimizer, MOI.TimeLimitSec() => 30)

We don't do anything special with parameters, so this might depend on the version of KNITRO that you have installed.

[chengg04]

Thank you for the prompt response, Oscar!

I included the time limit via MOI as you suggested. Here is the code I use now:

using JuMP, KNITRO, PowerModels

minlp_solver = JuMP.optimizer_with_attributes(KNITRO.Optimizer, MOI.TimeLimitSec() => 30)

solve_ots("pglib_opf_case24_ieee_rts__sad.m", ACPPowerModel, minlp_solver)

However, it seems the 30-second time limit is not taken by the solver. I include the solver log below. And here is my versions information: Knitro: 13.0.1, KNITRO.jl: v0.11.1, JuMP.jl: v0.22.3, PowerModels.jl: v 0.19.10. Operating system: macOS Sonoma with M1 chip.

MINLP solver shifted start point to satisfy bounds (32 variables).
WARNING: Problem appears to have nonlinear equalities and be non-convex.
         The Knitro mixed integer solver is designed for convex problems.
         For non-convex problems it is only a heuristic, and the reported
         bounds and optimality claims cannot be verified.

datacheck:               0
hessian_no_f:            1
numthreads:              1
mip_numthreads:          1
Knitro changing mip_method from AUTO to 1.
Knitro changing mip_rootalg from AUTO to 1.
Knitro changing mip_lpalg from AUTO to 3.
Knitro changing mip_branchrule from AUTO to 2.
Knitro changing mip_selectrule from AUTO to 2.
Knitro changing mip_mir from AUTO to 1.
Knitro changing mip_rounding from AUTO to 3.
Knitro changing mip_heuristic_strategy from AUTO to 1.
Knitro changing mip_heuristic_feaspump from AUTO to 1.
Knitro changing mip_heuristic_mpec from AUTO to 1.
Knitro changing mip_heuristic_diving from AUTO to 0.
Knitro changing mip_heuristic_lns from AUTO to 0.
Knitro changing mip_pseudoinit from AUTO to 1.

Problem Characteristics
-----------------------
Objective goal:  Minimize
Objective type:  quadratic
Number of variables:                                304
    bounded below only:                               0
    bounded above only:                               0
    bounded below and above:                        279
    fixed:                                            1
    free:                                            24
Number of binary variables:                          38
Number of integer variables:                          0
Number of constraints:                              353
    linear equalities:                               48
    quadratic equalities:                             1
    gen. nonlinear equalities:                      152
    linear one-sided inequalities:                    0
    quadratic one-sided inequalities:               152
    gen. nonlinear one-sided inequalities:            0
    linear two-sided inequalities:                    0
    quadratic two-sided inequalities:                 0
    gen. nonlinear two-sided inequalities:            0
Number of nonzeros in Jacobian:                    1588
Number of nonzeros in Hessian:                      572

Knitro detected 0 GUB constraints
Knitro derived 0 knapsack covers after examining 0 constraints
Knitro using Branch and Bound method with 1 thread.

       Nodes        Best solution   Best bound      Gap       Time 
   Expl  |  Unexpl      value         value                  (secs)
   ---------------  -------------   ----------      ---      ------
      1       0                        73133.8                2.122
      1       0      76747.8   FP      73133.8      4.71%     2.769
    100       9      76747.8           76590.0      0.21%    86.050
    163      20      76630.6 LEAF      76609.5      0.03%   102.989
    168      17      76613.0 LEAF      76613.3     -0.00%   103.300

EXIT: Optimal solution found (assuming convexity).

HINT: The problem may be a non-convex mixed-integer problem.  Set
      mip_multistart=1 to enable a mixed-integer multistart heuristic,
      which may improve the chances of finding the global solution.

Final Statistics for MIP
------------------------
Final objective value               =  7.66130083679263e+04
Final bound value                   =  7.66132742057831e+04
Final optimality gap (abs / rel)    =  -2.66e-01 / -3.47e-06 (-0.00%)
# of nodes processed                =  168 (40.469s)
# of strong branching evaluations   =  63 (34.181s)
# of function evaluations           =  152284 (8.629s)
# of gradient evaluations           =  70783 (1.115s)
# of hessian evaluations            =  71618 (18.889s)
# of hessian-vector evaluations     =  0
# of subproblems processed          =  301 (102.627s)
Total program time (secs)           =  103.30029 (102.676 CPU time)
Time spent in evaluations (secs)    =  0.51630

Cuts statistics (computed / added)
----------------------------------
Knapsack cuts                       =  0 / 0
Mixed-Integer Rounding cuts         =  0 / 0

Heuristics statistics (calls / successes / time)
------------------------------------------------
Feasibility pump                    =  1 / 1 / 0.647s
Rounding heuristic                  =  0 / 0 / 0.017s
MPEC heuristic                      =  2 / 1 / 27.364s

===========================================================================

Dict{String, Any} with 8 entries:
  "solve_time"         => 102.676
  "optimizer"          => "Knitro"
  "termination_status" => LOCALLY_SOLVED
  "dual_status"        => FEASIBLE_POINT
  "primal_status"      => FEASIBLE_POINT
  "objective"          => 76613.0
  "solution"           => Dict{String, Any}("baseMVA"=>100.0, "branch"=>Dict{String, Any}("24"=>Dict{String, Any}("qf"=>-0.291815, "qt"=>0.255001, "br_status"=…
  "objective_lb"       => 76613.3

[odow]

Interesting. It's useful to have an example that fails.

This looks like an issue in the upstream KNITRO solver, because all we do here is pass the options.

I'll take a look

[odow]

KNITRO.jl: v0.11.1

Wait. The most recent version of KNITRO.jl is v0.14.2. Can you try updating???

[odow]

Ah. I can't test this locally because I only have a limited size license

I think this is just a bug in KNITRO

julia> model = KNITRO.Optimizer();
##### This license is only intended for use by JuMP dev. #####
##### License is valid until Dec 31, 2024 #####

julia> p = Ref{Cint}(0)
Base.RefValue{Int32}(0)

julia> KN_get_param_id(model.inner, "maxtime_cpu", p)
-521

julia> p
Base.RefValue{Int32}(0)

julia> KN_get_param_id(model.inner, "maxtimecpu", p)
0

julia> p
Base.RefValue{Int32}(1024)

julia> KN_set_double_param_by_name(model.inner, "maxtime_cpu", 30.0)
0

julia> KN_set_double_param_by_name(model.inner, "maxtimecpu", 30.0)
-516

KN_get_param_id requires maxtimecpu but KN_set_double_param_by_name requires maxtime_cpu.

[chengg04]

Thank you, Oscar! Is there a way to fix this bug in KNITRO.jl source code?

[odow]

Can you try

using JuMP, KNITRO, PowerModels
minlp_solver = JuMP.optimizer_with_attributes(KNITRO.Optimizer, MOI.TimeLimitSec() => 30.0)
solve_ots("pglib_opf_case24_ieee_rts__sad.m", ACPPowerModel, minlp_solver)

[chengg04]

It seems the time limit is still not taken, after using the most updated KNITRO.jl version.

After updating, I am now using the following packages: KNITRO.jl: v0.14.2, JuMP.jl: v1.22.2, PowerModels.jl: v0.21.2.

I ran the following code (I reduced the time limit to 15 seconds):

using JuMP, KNITRO, PowerModels

minlp_solver = JuMP.optimizer_with_attributes(KNITRO.Optimizer, MOI.TimeLimitSec() => 15)

solve_ots("pglib_opf_case24_ieee_rts__sad.m", ACPPowerModel, minlp_solver)

And here is the solver log, which does not seem to indicate that the time limit is taken:

MINLP solver shifted start point to satisfy bounds (32 variables).
WARNING: Problem appears to have nonlinear equalities and be non-convex.
         The Knitro mixed integer solver is designed for convex problems.
         For non-convex problems it is only a heuristic, and the reported
         bounds and optimality claims cannot be verified.

datacheck:               0
hessian_no_f:            1
numthreads:              1
mip_numthreads:          1
Knitro changing mip_method from AUTO to 1.
Knitro changing mip_rootalg from AUTO to 1.
Knitro changing mip_lpalg from AUTO to 3.
Knitro changing mip_branchrule from AUTO to 2.
Knitro changing mip_selectrule from AUTO to 2.
Knitro changing mip_mir from AUTO to 1.
Knitro changing mip_rounding from AUTO to 3.
Knitro changing mip_heuristic_strategy from AUTO to 1.
Knitro changing mip_heuristic_feaspump from AUTO to 1.
Knitro changing mip_heuristic_mpec from AUTO to 1.
Knitro changing mip_heuristic_diving from AUTO to 0.
Knitro changing mip_heuristic_lns from AUTO to 0.
Knitro changing mip_pseudoinit from AUTO to 1.

Problem Characteristics
-----------------------
Objective goal:  Minimize
Objective type:  quadratic
Number of variables:                                304
    bounded below only:                               0
    bounded above only:                               0
    bounded below and above:                        279
    fixed:                                            1
    free:                                            24
Number of binary variables:                          38
Number of integer variables:                          0
Number of constraints:                              353
    linear equalities:                               48
    quadratic equalities:                             1
    gen. nonlinear equalities:                      152
    linear one-sided inequalities:                    0
    quadratic one-sided inequalities:               152
    gen. nonlinear one-sided inequalities:            0
    linear two-sided inequalities:                    0
    quadratic two-sided inequalities:                 0
    gen. nonlinear two-sided inequalities:            0
Number of nonzeros in Jacobian:                    1588
Number of nonzeros in Hessian:                      572

Knitro detected 0 GUB constraints
Knitro derived 0 knapsack covers after examining 0 constraints
Knitro using Branch and Bound method with 1 thread.

       Nodes        Best solution   Best bound      Gap       Time 
   Expl  |  Unexpl      value         value                  (secs)
   ---------------  -------------   ----------      ---      ------
      1       0                        73133.8                0.079
      1       0      76747.8   FP      73133.8      4.71%     0.419
     48      26      75892.1 FCRD      75763.2      0.17%    21.038
     52      26      75762.0 LEAF      75769.0     -0.01%    21.284

EXIT: Optimal solution found (assuming convexity).

HINT: The problem may be a non-convex mixed-integer problem.  Set
      mip_multistart=1 to enable a mixed-integer multistart heuristic,
      which may improve the chances of finding the global solution.

Final Statistics for MIP
------------------------
Final objective value               =  7.57620054342590e+04
Final bound value                   =  7.57690388360681e+04
Final optimality gap (abs / rel)    =  -7.03e+00 / -9.28e-05 (-0.01%)
# of nodes processed                =  52 (6.501s)
# of strong branching evaluations   =  33 (14.176s)
# of function evaluations           =  39322 (2.381s)
# of gradient evaluations           =  25722 (0.359s)
# of hessian evaluations            =  26025 (4.023s)
# of hessian-vector evaluations     =  0
# of subproblems processed          =  125 (21.244s)
Total program time (secs)           =  21.28451 (21.284 CPU time)
Time spent in evaluations (secs)    =  0.00017

Cuts statistics (computed / added)
----------------------------------
Knapsack cuts                       =  0 / 0
Mixed-Integer Rounding cuts         =  0 / 0

Heuristics statistics (calls / successes / time)
------------------------------------------------
Feasibility pump                    =  1 / 1 / 0.340s
Rounding heuristic                  =  1 / 1 / 0.018s
MPEC heuristic                      =  1 / 0 / 0.226s

===========================================================================

Dict{String, Any} with 8 entries:
  "solve_time"         => 21.284
  "optimizer"          => "Knitro"
  "termination_status" => LOCALLY_SOLVED
  "dual_status"        => FEASIBLE_POINT
  "primal_status"      => FEASIBLE_POINT
  "objective"          => 75762.0
  "solution"           => Dict{String, Any}("baseMVA"=>100.0, "branch"=>Dict{String, Any}("24"=>Dict{String, Any}("qf"=>-0.414671, "qt"=>0.379171, "br_status"=…
  "objective_lb"       => 75769.0

[odow]

What about:

using JuMP, KNITRO, PowerModels
minlp_solver = JuMP.optimizer_with_attributes(KNITRO.Optimizer, "maxtime_real" => 15.0)

We should probably contact Artelys support about this.

[odow]

Can you check that any options work?

julia> using JuMP, KNITRO, PowerModels

julia> minlp_solver = JuMP.optimizer_with_attributes(KNITRO.Optimizer, "outlev" => 0)
MathOptInterface.OptimizerWithAttributes(KNITRO.Optimizer, Pair{MathOptInterface.AbstractOptimizerAttribute, Any}[MathOptInterface.RawOptimizerAttribute("outlev") => 0])

julia> solve_ots("pglib_opf_case14_ieee__api.m", ACPPowerModel, minlp_solver)
[info | PowerModels]: removing 3 cost terms from generator 4: Float64[]
[info | PowerModels]: removing 1 cost terms from generator 1: [792.0951, 0.0]
[info | PowerModels]: removing 3 cost terms from generator 5: Float64[]
[info | PowerModels]: removing 1 cost terms from generator 2: [2326.9494, 0.0]
[info | PowerModels]: removing 3 cost terms from generator 3: Float64[]
##### This license is only intended for use by JuMP dev. #####
##### License is valid until Dec 31, 2024 #####
##### This license is only intended for use by JuMP dev. #####
##### License is valid until Dec 31, 2024 #####
Dict{String, Any} with 8 entries:
  "solve_time"         => 0.000871
  "optimizer"          => "Knitro"
  "termination_status" => INTERRUPTED
  "dual_status"        => UNKNOWN_RESULT_STATUS
  "primal_status"      => UNKNOWN_RESULT_STATUS
  "objective"          => 0.0
  "solution"           => Dict{String, Any}("baseMVA"=>100.0, "branch"=>Dict{String, Any}("4"=>Dict{…
  "objective_lb"       => -1.79769e308

[chengg04]

The outlev=>0 option works.

For maxtime_real, the log shows that this option is taken. However, the solver still does not stop at the time limit. I guess maxtime_cpu is needed to impose the time limit, and KNITRO.jl has trouble just for this one option...Here is the code I use:

using JuMP, KNITRO, PowerModels

minlp_solver = JuMP.optimizer_with_attributes(KNITRO.Optimizer, "maxtime_real" => 5.0)

solve_ots("pglib_opf_case24_ieee_rts__sad.m", ACPPowerModel, minlp_solver)

And here is the log. The maxtime_real option is shown in line 3 of the log:

datacheck:               0
hessian_no_f:            1
maxtime_real:            5
numthreads:              1
mip_numthreads:          1
Knitro changing mip_method from AUTO to 1.
Knitro changing mip_rootalg from AUTO to 1.
Knitro changing mip_lpalg from AUTO to 3.
Knitro changing mip_branchrule from AUTO to 2.
Knitro changing mip_selectrule from AUTO to 2.
Knitro changing mip_mir from AUTO to 1.
Knitro changing mip_rounding from AUTO to 3.
Knitro changing mip_heuristic_strategy from AUTO to 1.
Knitro changing mip_heuristic_feaspump from AUTO to 1.
Knitro changing mip_heuristic_mpec from AUTO to 1.
Knitro changing mip_heuristic_diving from AUTO to 0.
Knitro changing mip_heuristic_lns from AUTO to 0.
Knitro changing mip_pseudoinit from AUTO to 1.

Problem Characteristics
-----------------------
Objective goal:  Minimize
Objective type:  quadratic
Number of variables:                                304
    bounded below only:                               0
    bounded above only:                               0
    bounded below and above:                        279
    fixed:                                            1
    free:                                            24
Number of binary variables:                          38
Number of integer variables:                          0
Number of constraints:                              353
    linear equalities:                               48
    quadratic equalities:                             1
    gen. nonlinear equalities:                      152
    linear one-sided inequalities:                    0
    quadratic one-sided inequalities:               152
    gen. nonlinear one-sided inequalities:            0
    linear two-sided inequalities:                    0
    quadratic two-sided inequalities:                 0
    gen. nonlinear two-sided inequalities:            0
Number of nonzeros in Jacobian:                    1588
Number of nonzeros in Hessian:                      572

Knitro detected 0 GUB constraints
Knitro derived 0 knapsack covers after examining 0 constraints
Knitro using Branch and Bound method with 1 thread.

       Nodes        Best solution   Best bound      Gap       Time 
   Expl  |  Unexpl      value         value                  (secs)
   ---------------  -------------   ----------      ---      ------
      1       0                        73133.8                0.079
      1       0      76747.8   FP      73133.8      4.71%     0.421
     48      26      75892.1 FCRD      75763.2      0.17%    21.045
     52      26      75762.0 LEAF      75769.0     -0.01%    21.291

EXIT: Optimal solution found (assuming convexity).

HINT: The problem may be a non-convex mixed-integer problem.  Set
      mip_multistart=1 to enable a mixed-integer multistart heuristic,
      which may improve the chances of finding the global solution.

Final Statistics for MIP
------------------------
Final objective value               =  7.57620054342590e+04
Final bound value                   =  7.57690388360681e+04
Final optimality gap (abs / rel)    =  -7.03e+00 / -9.28e-05 (-0.01%)
# of nodes processed                =  52 (6.518s)
# of strong branching evaluations   =  33 (14.166s)
# of function evaluations           =  39322 (2.340s)
# of gradient evaluations           =  25722 (0.356s)
# of hessian evaluations            =  26025 (4.085s)
# of hessian-vector evaluations     =  0
# of subproblems processed          =  125 (21.250s)
Total program time (secs)           =  21.29098 (21.292 CPU time)
Time spent in evaluations (secs)    =  0.00015

Cuts statistics (computed / added)
----------------------------------
Knapsack cuts                       =  0 / 0
Mixed-Integer Rounding cuts         =  0 / 0

Heuristics statistics (calls / successes / time)
------------------------------------------------
Feasibility pump                    =  1 / 1 / 0.341s
Rounding heuristic                  =  1 / 1 / 0.018s
MPEC heuristic                      =  1 / 0 / 0.225s

===========================================================================

Dict{String, Any} with 8 entries:
  "solve_time"         => 21.292
  "optimizer"          => "Knitro"
  "termination_status" => LOCALLY_SOLVED
  "dual_status"        => FEASIBLE_POINT
  "primal_status"      => FEASIBLE_POINT
  "objective"          => 75762.0
  "solution"           => Dict{String, Any}("baseMVA"=>100.0, "branch"=>Dict{String, Any}("24"=>Dict{String, Any}("qf"=>-0.414671, "qt"=>0.379171, "br_status"=…
  "objective_lb"       => 75769.0

[odow]

KNITRO.jl has trouble just for this one option

To clarify: this appears to be a problem with the upstream solver and not with KNITRO.jl. We don't do anything other than pass options straight through.

[Bougron]

I'm a member of Knitro development team, do not hesitate to contact us at when you encounter an issue with Knitro itself. If you are trying to solve a MINLP problem, you should use mip_maxtime_cpu and mip_maxtime_real (all options are described in online documentation). Let me know how it goes.

[chengg04]

Thank you very much for the follow up.

I tried both options, and for both of them, I encountered the error MathOptInterface.UnsupportedAttribute. More specifically, if I run the following:

using JuMP, KNITRO, PowerModels

minlp_solver = JuMP.optimizer_with_attributes(KNITRO.Optimizer, "mip_maxtime_cpu" => 5.0)

solve_ots("pglib_opf_case24_ieee_rts__sad.m", ACPPowerModel, minlp_solver)

then I will get the error:

ERROR: LoadError: MathOptInterface.UnsupportedAttribute{MathOptInterface.RawOptimizerAttribute}: Attribute MathOptInterface.RawOptimizerAttribute("mip_maxtime_cpu") is not supported by the model.
Stacktrace:
  [1] set(model::KNITRO.Optimizer, attr::MathOptInterface.RawOptimizerAttribute, value::Float64)
    @ KNITRO ~/.julia/packages/KNITRO/FWSi0/src/MOI_wrapper.jl:316
  [2] _instantiate_and_check(optimizer_constructor::MathOptInterface.OptimizerWithAttributes)
    @ MathOptInterface ~/.julia/packages/MathOptInterface/aJZbq/src/instantiate.jl:120
  [3] instantiate(optimizer_constructor::Any; with_bridge_type::Type{Float64}, with_cache_type::Nothing)
    @ MathOptInterface ~/.julia/packages/MathOptInterface/aJZbq/src/instantiate.jl:175
  [4] set_optimizer(model::Model, optimizer_constructor::Any; add_bridges::Bool)
    @ JuMP ~/.julia/packages/JuMP/7rBNn/src/optimizer_interface.jl:493
  [5] set_optimizer(model::Model, optimizer_constructor::Any)
    @ JuMP ~/.julia/packages/JuMP/7rBNn/src/optimizer_interface.jl:491
  [6] optimize_model!(aim::ACPPowerModel; relax_integrality::Bool, optimizer::MathOptInterface.OptimizerWithAttributes, solution_processors::Vector{Any})
    @ InfrastructureModels ~/.julia/packages/InfrastructureModels/C2xBM/src/core/base.jl:387
  [7] solve_model(data::Dict{String, Any}, model_type::Type, optimizer::MathOptInterface.OptimizerWithAttributes, build_method::typeof(build_ots); ref_extensions::Vector{typeof(ref_add_on_off_va_bounds!)}, solution_processors::Vector{Any}, relax_integrality::Bool, multinetwork::Bool, kwargs::Base.Iterators.Pairs{Union{}, Union{}, Tuple{}, NamedTuple{(), Tuple{}}})
    @ PowerModels ~/.julia/packages/PowerModels/Dhfhc/src/core/base.jl:37
  [8] solve_model(file::String, model_type::Type, optimizer::MathOptInterface.OptimizerWithAttributes, build_method::Function; kwargs::Base.Iterators.Pairs{Symbol, Vector{typeof(ref_add_on_off_va_bounds!)}, Tuple{Symbol}, NamedTuple{(:ref_extensions,), Tuple{Vector{typeof(ref_add_on_off_va_bounds!)}}}})
    @ PowerModels ~/.julia/packages/PowerModels/Dhfhc/src/core/base.jl:18
  [9] #solve_ots#1043
    @ ~/.julia/packages/PowerModels/Dhfhc/src/prob/ots.jl:9 [inlined]
 [10] solve_ots(file::String, model_type::Type, optimizer::MathOptInterface.OptimizerWithAttributes)
    @ PowerModels ~/.julia/packages/PowerModels/Dhfhc/src/prob/ots.jl:9

And the same for mip_maxtime_real.

[odow]

@Bougron, there does seem to be an issue with the C API:

When getting the param ID, it requires mip_maxtimecpu, and when setting, it requires mip_maxtime_cpu:

julia> using KNITRO

julia> model = KNITRO.Optimizer();
##### This license is only intended for use by JuMP dev. #####
##### License is valid until Dec 31, 2024 #####

julia> kc = model.inner;

julia> param_id = Ref{Cint}(0);

julia> KN_get_param_id(kc, "mip_maxtime_cpu", param_id)
-521

julia> param_id
Base.RefValue{Int32}(0)

julia> KN_get_param_id(kc, "mip_maxtimecpu", param_id)
0

julia> param_id
Base.RefValue{Int32}(2006)

julia> KN_set_double_param_by_name(kc, "mip_maxtime_cpu", 3.0)
0

julia> KN_set_double_param_by_name(kc, "mip_maxtimecpu", 3.0)
-516

297 works around this by using only the id version, so once merged, "mip_maxtimecpu" should work.

[odow]

Using #297, I get:

julia> using JuMP, KNITRO, PowerModels

julia> minlp_solver = JuMP.optimizer_with_attributes(KNITRO.Optimizer, "mip_maxtimereal" => 0.0)
MathOptInterface.OptimizerWithAttributes(KNITRO.Optimizer, Pair{MathOptInterface.AbstractOptimizerAttribute, Any}[MathOptInterface.RawOptimizerAttribute("mip_maxtimereal") => 0.0])

julia> solve_ots("pglib_opf_case14_ieee__api.m", ACPPowerModel, minlp_solver)
[info | PowerModels]: removing 3 cost terms from generator 4: Float64[]
[info | PowerModels]: removing 1 cost terms from generator 1: [792.0951, 0.0]
[info | PowerModels]: removing 3 cost terms from generator 5: Float64[]
[info | PowerModels]: removing 1 cost terms from generator 2: [2326.9494, 0.0]
[info | PowerModels]: removing 3 cost terms from generator 3: Float64[]
##### This license is only intended for use by JuMP dev. #####
##### License is valid until Dec 31, 2024 #####
##### This license is only intended for use by JuMP dev. #####
##### License is valid until Dec 31, 2024 #####

=======================================
            Student License
       (NOT FOR COMMERCIAL USE)
         Artelys Knitro 13.1.0
=======================================

WARNING: Problem appears to have nonlinear equalities and be non-convex.
         The Knitro mixed integer solver is designed for convex problems.
         For non-convex problems it is only a heuristic, and the reported
         bounds and optimality claims cannot be verified.

datacheck:               0
hessian_no_f:            1
numthreads:              1
mip_maxtime_real:        0
mip_numthreads:          1
Knitro changing mip_method from AUTO to 1.
Knitro changing mip_rootalg from AUTO to 1.
Knitro changing mip_lpalg from AUTO to 3.
Knitro changing mip_branchrule from AUTO to 2.
Knitro changing mip_selectrule from AUTO to 2.
Knitro changing mip_mir from AUTO to 1.
Knitro changing mip_rounding from AUTO to 3.
Knitro changing mip_heuristic_strategy from AUTO to 1.
Knitro changing mip_heuristic_feaspump from AUTO to 1.
Knitro changing mip_heuristic_misqp from AUTO to 0.
Knitro changing mip_heuristic_mpec from AUTO to 1.
Knitro changing mip_heuristic_diving from AUTO to 0.
Knitro changing mip_heuristic_lns from AUTO to 0.
Knitro changing mip_pseudoinit from AUTO to 1.

Problem Characteristics
-----------------------
Objective goal:  Minimize
Objective type:  linear
Number of variables:                                138
    bounded below only:                               0
    bounded above only:                               0
    bounded below and above:                        121
    fixed:                                            3
    free:                                            14
Number of binary variables:                          20
Number of integer variables:                          0
Number of constraints:                              189
    linear equalities:                               28
    quadratic equalities:                             1
    gen. nonlinear equalities:                       80
    linear one-sided inequalities:                    0
    quadratic one-sided inequalities:                80
    gen. nonlinear one-sided inequalities:            0
    linear two-sided inequalities:                    0
    quadratic two-sided inequalities:                 0
    gen. nonlinear two-sided inequalities:            0
Number of nonzeros in Jacobian:                     812
Number of nonzeros in Hessian:                      302

Knitro detected 0 GUB constraints
Knitro derived 0 knapsack covers after examining 0 constraints
Knitro using Branch and Bound method with 1 thread.

       Nodes        Best solution   Best bound      Gap       Time 
   Expl  |  Unexpl      value         value                  (secs)
   ---------------  -------------   ----------      ---      ------

EXIT: Time limit reached. No integer feasible point found.

HINT: The problem may be a non-convex mixed-integer problem.  Set
      mip_multistart=1 to enable a mixed-integer multistart heuristic,
      which may improve the chances of finding the global solution.

Final Statistics for MIP
------------------------
Final objective value               =
Final bound value                   =  0.00000000000000e+00
Final optimality gap (abs / rel)    =  Infinity
# of nodes processed                =  0 (0.000s)
# of strong branching evaluations   =  0 (0.000s)
# of function evaluations           =  2 (0.000s)
# of gradient evaluations           =  1 (0.000s)
# of hessian evaluations            =  0 (0.000s)
# of hessian-vector evaluations     =  0
# of subproblems processed          =  0 (0.000s)
Total program time (secs)           =  0.00067 (0.001 CPU time)
Time spent in evaluations (secs)    =  0.00016

Cuts statistics (computed / added)
----------------------------------
Knapsack cuts                       =  0 / 0
Mixed-Integer Rounding cuts         =  0 / 0

Heuristics statistics (calls / successes / time)
------------------------------------------------
Feasibility pump                    =  0 / 0 / 0.000s
Rounding heuristic                  =  0 / 0 / 0.000s
MPEC heuristic                      =  0 / 0 / 0.000s

===========================================================================

Dict{String, Any} with 8 entries:
  "solve_time"         => 0.00072
  "optimizer"          => "Knitro"
  "termination_status" => TIME_LIMIT
  "dual_status"        => INFEASIBLE_POINT
  "primal_status"      => INFEASIBLE_POINT
  "objective"          => 0.0
  "solution"           => Dict{String, Any}("baseMVA"=>100.0, "branch"=>Dict{String, Any}("4"=>Dict{…
  "objective_lb"       => 0.0

so it is working.

@Bougron, as a secondary request then, shouldn't "maxtime_real" set the time limit regardless of which method is used to solve? The docs do not make it clear that this doesn't apply to MIP solves.

[Bougron]

Yes this is expected (even though I understand it is confusing), the documentation mentions it in the description: When solving a mip, the maxtime_real is solved to limit time on subproblems.

[Bougron]

Regarding the naming, we are checking it out (mip_maxtimecpu vs mip_maxtime_cpu).

[odow]

Ah, then perhaps all that is needed is that for the documentation of maxtime_real it clarifies that this applies to subproblems, and to use the various xxx_maxtime_real depending on the algorithm used to solve.

I see that other options have this wording, but it is missing from the maxtime_real and maxtime_cpu options.

[odow]

This will be fixed when I release #301