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ModelLike of type Alpine.Optimizer does not support accessing the attribute MathOptInterface.ResultCount() #162

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OliverEvans96 opened this issue Dec 22, 2020 · 6 comments
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ModelLike of type Alpine.Optimizer does not support accessing the attribute MathOptInterface.ResultCount() #162

OliverEvans96 opened this issue Dec 22, 2020 · 6 comments
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@OliverEvans96
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Hello,

I'm having trouble accessing the optimal variable value from an Alpine model. Possibly related to #101.

Minimal Example

using JuMP, Alpine, Ipopt, Cbc

m = Model()
set_optimizer(m, Alpine.Optimizer)
set_optimizer_attributes(m, "mip_solver"=>Cbc.Optimizer)
set_optimizer_attributes(m, "nlp_solver"=>Ipopt.Optimizer)
@variable(m, -1 <= x <= 1)
@NLobjective(m, Max, -x+1)
optimize!(m)

An optimal solution is reached:

Solver Output 
PROBLEM STATISTICS
  Objective sense = Max
  #Variables = 1
  #Bin-Int Variables = 0
  #Constraints = 0
  #NL Constraints = 0
  #Linear Constraints = 0
  #Detected convex constraints = 0
  #Detected nonlinear terms = 0
  #Variables involved in nonlinear terms = 0
  #Potential variables for partitioning = 0
SUB-SOLVERS USED BY ALPINE
  NLP local solver = Ipopt
  MIP solver = Cbc
ALPINE CONFIGURATION
  Maximum iterations =  99
  Relative optimality gap criteria = 0.0100%
  Discretization ratio = 4
  Bound-tightening (OBBT) presolve = true
  OBBT maximum iterations = 10
PRESOLVE 
  Doing local search
This is Ipopt version 3.13.2, running with linear solver mumps.
NOTE: Other linear solvers might be more efficient (see Ipopt documentation).

Number of nonzeros in equality constraint Jacobian...:        0
Number of nonzeros in inequality constraint Jacobian.:        0
Number of nonzeros in Lagrangian Hessian.............:        0

Total number of variables............................:        1
                     variables with only lower bounds:        0
                variables with lower and upper bounds:        1
                     variables with only upper bounds:        0
Total number of equality constraints.................:        0
Total number of inequality constraints...............:        0
        inequality constraints with only lower bounds:        0
   inequality constraints with lower and upper bounds:        0
        inequality constraints with only upper bounds:        0

iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls
   0  1.0000000e+00 0.00e+00 1.00e+00  -1.0 0.00e+00    -  0.00e+00 0.00e+00   0
   1  1.5000000e+00 0.00e+00 3.31e-01  -1.7 5.00e-01    -  6.69e-01 1.00e+00f  1
   2  1.9950000e+00 0.00e+00 8.33e-17  -1.7 7.13e-01    -  1.00e+00 6.94e-01f  1
   3  1.9998086e+00 0.00e+00 5.20e-18  -3.8 4.81e-03    -  1.00e+00 1.00e+00f  1
   4  1.9999982e+00 0.00e+00 1.91e-17  -5.7 1.90e-04    -  1.00e+00 1.00e+00f  1
   5  2.0000000e+00 0.00e+00 8.97e-17  -8.6 1.85e-06    -  1.00e+00 1.00e+00f  1

Number of Iterations....: 5

                                   (scaled)                 (unscaled)
Objective...............:  -2.0000000074923991e+00    2.0000000074923991e+00
Dual infeasibility......:   8.9681201477325150e-17    8.9681201477325150e-17
Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00
Complementarity.........:   2.5076007968172859e-09   -2.5076007968172859e-09
Overall NLP error.......:   2.5076007968172859e-09    8.9681201477325150e-17


Number of objective function evaluations             = 6
Number of objective gradient evaluations             = 6
Number of equality constraint evaluations            = 0
Number of inequality constraint evaluations          = 0
Number of equality constraint Jacobian evaluations   = 0
Number of inequality constraint Jacobian evaluations = 0
Number of Lagrangian Hessian evaluations             = 5
Total CPU secs in IPOPT (w/o function evaluations)   =      0.002
Total CPU secs in NLP function evaluations           =      0.000

EXIT: Optimal Solution Found.
  Local solver returns a feasible point
  Starting bound-tightening
Welcome to the CBC MILP Solver 
Version: 2.10.3 
Build Date: Oct  7 2019 

command line - Cbc_C_Interface -seconds 999999.9961321354 -solve -quit (default strategy 1)
seconds was changed from 1e+100 to 1e+06
Presolve determined that the problem was infeasible with tolerance of 1e-08
Analysis indicates model infeasible or unbounded
0  Obj -1 Primal inf 0.99999991 (1)
Primal infeasible - objective value -1
PrimalInfeasible objective -1 - 0 iterations time 0.002

Result - Linear relaxation infeasible

Enumerated nodes:           0
Total iterations:           0
Time (CPU seconds):         0.00
Time (Wallclock Seconds):   0.00

Total time (CPU seconds):       0.00   (Wallclock seconds):       0.00

!Welcome to the CBC MILP Solver 
Version: 2.10.3 
Build Date: Oct  7 2019 

command line - Cbc_C_Interface -seconds 999999.9936430454 -solve -quit (default strategy 1)
seconds was changed from 1e+100 to 1e+06
Presolve determined that the problem was infeasible with tolerance of 1e-08
Analysis indicates model infeasible or unbounded
0  Obj -1 Primal inf 0.99999991 (1) Dual inf 0.9999999 (1)
1  Obj -2 Primal inf 0.99999991 (1)
Primal infeasible - objective value -2
PrimalInfeasible objective -2.000000007 - 1 iterations time 0.002

Result - Linear relaxation infeasible

Enumerated nodes:           0
Total iterations:           0
Time (CPU seconds):         0.00
Time (Wallclock Seconds):   0.00

Total time (CPU seconds):       0.00   (Wallclock seconds):       0.00

!  Completed presolve in 0.01s (1 iterations)
UPPER-BOUNDING ITERATIONS
====================================================================================================
| Iter   | Incumbent       | Best Incumbent      | Upper Bound        | Gap (%)         | Time      
Welcome to the CBC MILP Solver 
Version: 2.10.3 
Build Date: Oct  7 2019 

command line - Cbc_C_Interface -seconds 999999.9922780991 -solve -quit (default strategy 1)
seconds was changed from 1e+100 to 1e+06
Empty problem - 0 rows, 1 columns and 0 elements
Optimal - objective value 1
Optimal objective 1 - 0 iterations time 0.002
Total time (CPU seconds):       0.00   (Wallclock seconds):       0.00

| finish | 2.0             | 2.0                 | 2.0                | 0.0             | 0.01s            
====================================================================================================

*** Alpine ended with status OPTIMAL ***

Error Message

But when I try try to access the value, of x, I get the following error:

julia> value(x)
ERROR: ArgumentError: ModelLike of type Alpine.Optimizer does not support accessing the attribute MathOptInterface.ResultCount()
Stacktrace:
 [1] get_fallback(::Alpine.Optimizer, ::MathOptInterface.ResultCount) at /home/oliver/.julia/packages/MathOptInterface/ZJFKw/src/attributes.jl:274
 [2] get(::Alpine.Optimizer, ::MathOptInterface.ResultCount) at /home/oliver/.julia/packages/MathOptInterface/ZJFKw/src/attributes.jl:272
 [3] check_result_index_bounds(::Alpine.Optimizer, ::MathOptInterface.VariablePrimal) at /home/oliver/.julia/packages/MathOptInterface/ZJFKw/src/attributes.jl:131
 [4] get(::Alpine.Optimizer, ::MathOptInterface.VariablePrimal, ::MathOptInterface.VariableIndex) at /home/oliver/.julia/packages/Alpine/Z4PGO/src/solver.jl:583
 [5] get(::MathOptInterface.Bridges.LazyBridgeOptimizer{Alpine.Optimizer}, ::MathOptInterface.VariablePrimal, ::MathOptInterface.VariableIndex) at /home/oliver/.julia/packages/MathOptInterface/ZJFKw/src/Bridges/bridge_optimizer.jl:808
 [6] get(::MathOptInterface.Utilities.CachingOptimizer{MathOptInterface.AbstractOptimizer,MathOptInterface.Utilities.UniversalFallback{MathOptInterface.Utilities.Model{Float64}}}, ::MathOptInterface.VariablePrimal, ::MathOptInterface.VariableIndex) at /home/oliver/.julia/packages/MathOptInterface/ZJFKw/src/Utilities/cachingoptimizer.jl:605
 [7] _moi_get_result(::MathOptInterface.Utilities.CachingOptimizer{MathOptInterface.AbstractOptimizer,MathOptInterface.Utilities.UniversalFallback{MathOptInterface.Utilities.Model{Float64}}}, ::MathOptInterface.VariablePrimal, ::Vararg{Any,N} where N) at /home/oliver/.julia/packages/JuMP/qhoVb/src/JuMP.jl:848
 [8] get(::Model, ::MathOptInterface.VariablePrimal, ::VariableRef) at /home/oliver/.julia/packages/JuMP/qhoVb/src/JuMP.jl:878
 [9] value(::VariableRef; result::Int64) at /home/oliver/.julia/packages/JuMP/qhoVb/src/variables.jl:767
 [10] value(::VariableRef) at /home/oliver/.julia/packages/JuMP/qhoVb/src/variables.jl:767
 [11] top-level scope at REPL[76]:1

Relevant Package Versions

Using Julia v1.5.3 (2020-11-09)

  [07493b3f] Alpine v0.2.1
  [9961bab8] Cbc v0.6.7
  [b6b21f68] Ipopt v0.6.5
  [4076af6c] JuMP v0.21.5

Is there currently any way to get the variable value?

Thanks!
Oliver
@harshangrjn
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@OliverEvans96 Are you getting this issue with other MILP solvers like CPLEX/Gurobi?

@blegat
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blegat commented Dec 23, 2020 •
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Looking at the code, value is indeed not going to work, A getter is needed for ResultCount here:

Alpine.jl/src/solver.jl

Line 583 in 383bdbc

MOI.check_result_index_bounds(model, attr)

A simple implementation is

MOI.get(model::Optimizer, ::MOI.ResultCount) = model.alpine_status == MOI.OPTIMIZE_NOT_CALLED ? 0 : 1

#152 should help catching these kind of issues

@OliverEvans96
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OliverEvans96 commented Dec 23, 2020 •
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@harshangrjn I don't have CPLEX/Gurobi.

@blegat when I define your MOI.get method before calling value(x), the first error is resolved, but I now get a new error:

julia> value(x)
ERROR: The index MathOptInterface.VariableIndex(1) is invalid. Note that an index becomes invalid after it has been deleted.
Stacktrace:
 [1] throw_if_not_valid(::Alpine.Optimizer, ::MathOptInterface.VariableIndex) at /home/oliver/.julia/packages/MathOptInterface/ZJFKw/src/indextypes.jl:75
 [2] get(::Alpine.Optimizer, ::MathOptInterface.VariablePrimal, ::MathOptInterface.VariableIndex) at /home/oliver/.julia/packages/Alpine/Z4PGO/src/solver.jl:584
 [3] get(::MathOptInterface.Bridges.LazyBridgeOptimizer{Alpine.Optimizer}, ::MathOptInterface.VariablePrimal, ::MathOptInterface.VariableIndex) at /home/oliver/.julia/packages/MathOptInterface/ZJFKw/src/Bridges/bridge_optimizer.jl:808
 [4] get(::MathOptInterface.Utilities.CachingOptimizer{MathOptInterface.AbstractOptimizer,MathOptInterface.Utilities.UniversalFallback{MathOptInterface.Utilities.Model{Float64}}}, ::MathOptInterface.VariablePrimal, ::MathOptInterface.VariableIndex) at /home/oliver/.julia/packages/MathOptInterface/ZJFKw/src/Utilities/cachingoptimizer.jl:605
 [5] _moi_get_result(::MathOptInterface.Utilities.CachingOptimizer{MathOptInterface.AbstractOptimizer,MathOptInterface.Utilities.UniversalFallback{MathOptInterface.Utilities.Model{Float64}}}, ::MathOptInterface.VariablePrimal, ::Vararg{Any,N} where N) at /home/oliver/.julia/packages/JuMP/qhoVb/src/JuMP.jl:848
 [6] get(::Model, ::MathOptInterface.VariablePrimal, ::VariableRef) at /home/oliver/.julia/packages/JuMP/qhoVb/src/JuMP.jl:878
 [7] value(::VariableRef; result::Int64) at /home/oliver/.julia/packages/JuMP/qhoVb/src/variables.jl:767
 [8] value(::VariableRef) at /home/oliver/.julia/packages/JuMP/qhoVb/src/variables.jl:767
 [9] top-level scope at REPL[11]:1

@blegat
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blegat commented Dec 23, 2020 •
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You should also add

MOI.is_valid(model::Optimizer, vi::MOI.VariableIndex) = 1 <= vi.value <= model.num_var_orig

@OliverEvans96
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@blegat - that worked!

julia> value(x)
-1.0

Thanks and happy holidays!
Oliver

@harshangrjn harshangrjn mentioned this issue Mar 30, 2021
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@harshangrjn
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This issue has been fixed in v0.2.2.

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@blegat @harshangrjn @OliverEvans96