# Gurobi reports OPTIMAL on a QP having a huge MaxVio

**URL:** <https://discourse.julialang.org/t/gurobi-reports-optimal-on-a-qp-having-a-huge-maxvio/129645>\
**Category:** Optimization (Mathematical)\
**Tags:** jump, gurobi\
**Created:** [June 4, 2025, 2:47pm UTC](https://discourse.julialang.org/t/gurobi-reports-optimal-on-a-qp-having-a-huge-maxvio/129645 "2025-06-04T14:47:28Z")\
**Posts on this page:** 9\
**Page:** 1

<div class="post-metadata">

**Author:** ![WalterMadelim](https://avatars.discourse-cdn.com/v4/letter/w/3e96dc/32.png) [@WalterMadelim](https://discourse.julialang.org/u/WalterMadelim)\
**Post date:** [June 4, 2025, 2:47pm UTC](https://discourse.julialang.org/t/gurobi-reports-optimal-on-a-qp-having-a-huge-maxvio/129645/1 "2025-06-04T14:47:28Z")

</div>

```julia
julia> JuMP.unset_silent(outer)
Set parameter OutputFlag to value 1

julia> JuMP.optimize!(outer)
Gurobi Optimizer version 12.0.2 build v12.0.2rc0 (win64 - Windows 11.0 (26100.2))

CPU model: 11th Gen Intel(R) Core(TM) i5-1135G7 @ 2.40GHz, instruction set [SSE2|AVX|AVX2|AVX512]
Thread count: 4 physical cores, 8 logical processors, using up to 8 threads

Optimize a model with 39 rows, 41 columns and 127 nonzeros
Model fingerprint: 0x6abf7598
Model has 38 quadratic objective terms
Coefficient statistics:
  Matrix range [1e+00, 3e+02]
  Objective range [9e-18, 5e+00]
  QObjective range [2e+00, 2e+00]
  Bounds range [1e+03, 1e+03]
  RHS range [4e+02, 2e+03]
Presolve removed 11 rows and 2 columns
Presolve time: 0.00s
Presolved: 28 rows, 39 columns, 80 nonzeros
Presolved model has 38 quadratic objective terms
Ordering time: 0.00s

Barrier statistics:
 Free vars : 39
 AA' NZ : 6.900e+01
 Factor NZ : 1.060e+02
 Factor Ops : 8.340e+02 (less than 1 second per iteration)
 Threads : 1

                  Objective Residual
Iter Primal Dual Primal Dual Compl Time
   0 1.29618707e+01 1.29610499e+01 4.29e+02 1.02e+01 1.08e+06 0s
   1 7.79452936e+03 -9.29391938e+03 4.59e+01 4.59e-01 8.38e+04 0s
   2 2.34735483e+03 -3.67336088e+03 3.99e+00 4.00e-02 1.54e+04 0s
   3 9.28074635e+01 -8.89553051e+02 5.28e-01 5.42e-03 3.32e+03 0s
   4 2.67970847e+00 -2.69217523e+02 6.93e-02 1.81e-04 4.56e+02 0s
   5 4.93346415e+00 -2.33632458e+02 6.85e-02 1.78e-04 4.42e+02 0s
   6 2.61337406e+00 -2.70695461e+02 3.15e-01 1.50e-04 4.63e+02 0s
   7 1.09725171e+01 -1.34247786e+02 5.05e-01 1.37e-04 3.67e+02 0s
   8 6.15979734e+00 -7.90459574e+01 2.02e-01 6.79e-05 1.87e+02 0s
   9 2.03422156e-02 -8.54163247e+01 1.06e-01 4.22e-05 1.57e+02 0s
  10 6.28528487e-01 -3.31827999e+01 2.99e-02 4.33e-11 3.35e+01 0s
  11 1.33921978e-03 -2.70800877e+01 4.09e-13 2.12e-12 1.23e+00 0s
  12 2.55952604e-04 -3.06308511e+01 1.99e-13 2.79e-12 1.10e-02 0s
  13 3.33498799e-04 -3.42217263e+01 3.09e-13 2.70e-12 1.19e-04 0s
  14 5.57483325e-04 -3.51843270e+01 9.52e-14 1.82e-12 4.34e-07 0s
  15 2.39292335e-04 -3.74834526e+01 2.37e-13 1.82e-12 2.28e-09 0s
  16 2.43578332e-04 -3.89336401e+01 1.87e-13 2.41e-12 1.43e-10 0s
  17 2.11498031e-03 -5.43399078e+01 4.19e-02 3.42e-12 7.46e-11 0s
  18 2.96317192e-04 -5.53941734e+01 1.40e-13 5.61e-12 2.39e-12 0s
  19 4.12094691e-04 -5.67513066e+01 5.10e-14 4.84e-12 3.94e-13 0s
  20 6.75682559e-04 -5.83702637e+01 1.42e-13 1.61e-12 4.12e-16 0s
  21 8.90546712e-04 -5.95038795e+01 2.26e-13 3.58e-12 1.14e-16 0s
  22 9.87057970e-04 -6.26382723e+01 1.59e-13 3.55e-12 2.54e-19 0s

Barrier solved model in 22 iterations and 0.01 seconds (0.00 work units)
Optimal objective 9.87057970e-04

User-callback calls 113, time in user-callback 0.00 sec

julia> JuMP.solution_summary(outer)
solution_summary(; result = 1, verbose = false)
├ solver_name : Gurobi
├ Termination
│ ├ termination_status : OPTIMAL
│ ├ result_count : 1
│ ├ raw_status : Model was solved to optimality (subject to tolerances), and an optimal solution is available.
│ └ objective_bound : -2.70801e+01
├ Solution (result = 1)
│ ├ primal_status : FEASIBLE_POINT
│ ├ dual_status : FEASIBLE_POINT
│ ├ objective_value : 9.87058e-04
│ └ dual_objective_value : -4.97810e+01
└ Work counters
  ├ solve_time (sec) : 7.99990e-03
  ├ simplex_iterations : 0
  ├ barrier_iterations : 22
  └ node_count : 0

julia> vio = JuMP.MOI.get(JuMP.backend(outer), Gurobi.ModelAttribute("MaxVio"))
43.97206084166428

julia> Gurobi.GRBwrite(JuMP.unsafe_backend(outer), "outer.mps")
0

```

This “OPTIMAL” solution is very weird—noticing the MaxVio, and the termination gap. Is this normal? (@odow I remember that there was also a gap displayed here)

The code to reproduce this model `outer` is here. (A lightweight PGlib.jl data package is required.)

> **code**
>
> ```julia
> import SparseArrays, PGLib; RawDict = PGLib.pglib("pglib_opf_case118_ieee.m");
> import Random
> function rectify_dir!(bft)
> for r in 1:size(bft, 1)
> if bft[r, 1] > bft[r, 2]
> bft[r, 1], bft[r, 2] = bft[r, 2], bft[r, 1]
> elseif bft[r, 1] == bft[r, 2]
> error("self-branch")
> end
> end
> end; function assert_is_connected(bft)
> B = size(bft, 1)
> pb = Vector(1:B) # primal bs
> sn = [1] # subnet
> for ite in 1:B # only du a full iteration as a conservative choice
> progress = false
> for (i, b) in enumerate(pb) # take out branch b
> if bft[b, 1] in sn
> on = bft[b, 2] # the other node
> on ∉ sn && push!(sn, on)
> elseif bft[b, 2] in sn
> on = bft[b, 1] # the other node
> on ∉ sn && push!(sn, on)
> else
> continue
> end
> popat!(pb, i); progress = true; break # fathom branch b
> end
> if progress == false
> error("ite = $ite. All the rest branches are not connected to the subnet being investigated. Check the current subnet")
> end
> 1:maximum(bft) ⊆ sn && return # The graph is proved to be connected
> end
> error("here shouldn't be reached")
> end; function bft_2_A(bft)
> B, N = size(bft, 1), maximum(bft)
> return SparseArrays.sparse([Vector(1:B); Vector(1:B)], vec(bft), [-ones(Int, B); ones(Int, B)], B, N)
> end; function adjnode(n)
> nv = Int[]
> for ci in findall(x -> x == n, bft)
> b, c = ci.I # row(= branch), col
> on = bft[b, 3 - c]
> on ∉ nv && push!(nv, on)
> end
> sort(nv)
> end; function get_b(n, m) # use this after `rectify_dir!(bft)` had been done
> n > m && ((n, m) = (m, n))
> for r in 1:size(bft, 1)
> (bft[r, 1] == n && bft[r, 2] == m) && return r
> end
> error("no such branch exist")
> end;
> G, G2N, Gcapinit, Gptcost = let
> G = length(RawDict["gen"])
> G2N = [RawDict["gen"]["$g"]["gen_bus"] for g in 1:G]
> cost = [RawDict["gen"]["$g"]["cost"] for g in 1:G]
> pmax = [RawDict["gen"]["$g"]["pmax"] for g in 1:G]
> gi = findall(x -> x > 1e-5, pmax)
> G2N, cost, pmax, G = G2N[gi], cost[gi], pmax[gi], length(gi)
> cost = [.001i[1] for i in cost]
> f = x -> round(x; digits = 6)
> G, G2N, pmax, f.(cost)
> end;
> D, D2N, Dref = let
> D = length(RawDict["load"])
> D2N = [RawDict["load"]["$d"]["load_bus"] for d in 1:D]
> Dref = [RawDict["load"]["$d"]["pd"] for d in 1:D]
> D, D2N, Dref
> end;
> T, Dura, Dmat = let
> Random.seed!(1); Dlarge = rand(1.1:7e-13:1.7, D) .* Dref
> Random.seed!(6); Dmiddle = rand(.7:7e-13:1.7, D) .* Dref
> Dmat = [Dref Dmiddle Dlarge]
> Dura = [6, 3, 1]
> T = size(Dmat, 2)
> T, Dura, Dmat
> end;
> S, prob_vec, avai_vec, g_hyd, Ginvcost = let
> g_hyd = 17; Gptcost[g_hyd] = 0 # 💧 pg[g = 17] is a hydro generator
> Ginvcost = 2.0Gptcost;
> Ginvcost[g_hyd] = maximum(Gptcost);
> avai_vec = [.3, .79] # availability factor for 💧 generator
> prob_vec = [.5, .5] # The associated probability vector
> S = length(prob_vec)
> S, prob_vec, avai_vec, g_hyd, Ginvcost
> end;
> 
> # feasible cost = 959.7425433749277
> # SP solution cost = 904.3816383661987
> # NA-relaxed cost = 884.6469843243801
> 
> B, N = length.([RawDict["branch"], RawDict["bus"]]) # (186, 118), fixed for this network
> bft = let # load a rectified bft
> bft = [RawDict["branch"]["$r"][c == 1 ? "f_bus" : "t_bus"] for r in 1:B, c in 1:2]; # raw bft
> assert_is_connected(bft);
> rectify_dir!(bft);
> bft
> end; A = bft_2_A(bft);
> 
> import JuMP, Gurobi; GRB_ENV = Gurobi.Env();
> 
> function bpf(b, Dvec, pgvec, pfvec) # dispatch function of power flow on branch b
> b == 9 && return -pgvec[findfirst(x -> x == 10, G2N)::Int] # b8[9] = -3
> b == 7 && return bpf(9, Dvec, pgvec, pfvec) # b8[7] = -1
> b == 94 && return bpf(93, Dvec, pgvec, pfvec) # b8[94] = -1
> b == 127 && return -bpf(126, Dvec, pgvec, pfvec) # b8[127] = -1
> b == 134 && return -pgvec[findfirst(x -> x == 87, G2N)::Int] # b8[134] = -3
> b == 176 && return -pgvec[findfirst(x -> x == 111, G2N)::Int] # b8[176] = -3
> b == 184 && return Dvec[findfirst(x -> x == 117, D2N)::Int] # b8[184] = -2
> dup_bvec = [67, 76, 99, 86, 124, 139, 142]
> b ∈ dup_bvec && return false # b8[dup_bvec] .= 0; b8[dup_bvec .- 1] .= 2
> return pfvec[(f = findfirst(x -> x == b, F2B);)] # [Fallback] the corresponding JuMP.@variable
> end
> n8 = let # elements being
> # [1]: a bus with load only, and whose degree is larger than 2
> # [2]: a bus with gen only, and whose degree is larger than 2
> # [3]: a bus with both power injection and load
> # [-1]: a bus with load only, and whose degree is 1, we won't add KCL constr here
> # [-3]: a bus with gen only, and whose degree is 1, we won't add KCL constr here
> # [-2]: a bare bus whose degree is 2, we won't add KCL constr here
> # [0]: a bare joint bus
> n8 = zeros(Int8, N); n8[D2N] .+= 1; n8[G2N] .+= 2
> n8[117] = -1
> n8[[10, 87, 111]] .= -3
> n8[[9, 63, 81]] .= -2
> n8
> end; b8 = let # Note this procedure is specific to case118, therefore executing once is sufficient
> # elements being
> # [2]: this line represents a 2-parallel branch, works as a normal line
> # [1]: a normal line
> # [0]: this line was banned (pf ≡ 0) due to parallel redundancy, we won't allocate pf variable here
> # [-1]: power flow of this line hinges on one of its adjacent line, we won't allocate pf variable here
> # [-2]: power flow of this line hinges on the corresponding pure load, we won't allocate pf variable here
> # [-3]: power flow of this line hinges on the corresponding pure gen, we won't allocate pf variable here
> b8 = ones(Int8, size(bft, 1))
> b8[9] = -3
> b8[7] = -1
> b8[94] = -1
> b8[127] = -1
> b8[134] = -3
> b8[176] = -3
> b8[184] = -2
> dup_bvec = [67, 76, 99, 86, 124, 139, 142]
> b8[dup_bvec] .= 0; b8[dup_bvec .- 1] .= 2
> b8
> end; 
> F, F2B, fra = let
> F2B = findall(s -> s ∈ 1:2, b8); F = length(F2B);
> fra = [RawDict["branch"]["$b"]["rate_a"] for b in F2B]; # f_rate_a is a used part of b_rate_a, under Dref, if fra *= .59, then INFEASIBLE
> F, F2B, fra
> end;
> 
> min_s = JuMP.Model(() -> Gurobi.Optimizer(GRB_ENV)); JuMP.set_silent(min_s);
> JuMP.@variable(min_s, 0 ≤ invg_s[1:G] ≤ 300); # ⚠️ set an artificial ub here
> JuMP.@variable(min_s, 0 ≤ pg_s[1:G, 1:T]);
> JuMP.@expression(min_s, power_max, invg_s + Gcapinit)
> JuMP.@constraint(min_s, [g = 1:G, t = 1:T], pg_s[g, t] ≤ power_max[g]);
> JuMP.@variable(min_s, -fra[f] ≤ pf_s[f = 1:F, t = 1:T] ≤ fra[f]);
> for t in 1:T, nState in 0:3, n in findall(ø -> ø == nState, n8) # real-time everynode KCL
> p_gen = (nState < 2 ? false : pg_s[findfirst(x -> x == n, G2N)::Int, t])
> p_demand = (nState == 1 || nState == 3 ? Dmat[findfirst(x -> x == n, D2N)::Int, t] : false)
> bv, sgv = SparseArrays.findnz(view(A, :, n))
> f = b -> bpf(b, view(Dmat, :, t), view(pg_s, :, t), view(pf_s, :, t))
> JuMP.@constraint(min_s, sgv'f.(bv) + p_gen == p_demand)
> end;
> JuMP.@expression(min_s, hyd_energy, Dura'view(pg_s, g_hyd, :))
> JuMP.@expression(min_s, hyd_energy_max, power_max[g_hyd]sum(Dura))
> JuMP.@expression(min_s, cost_vec_s, [Ginvcost'invg_s, (Gptcost'pg_s)Dura])
> constr_s = JuMP.@constraint(min_s, 0 == 1); # initial dumb
> function solve_min_s(min_s, constr_s, β_s, avai_vec_s, prob_vec_s)
> JuMP.delete(min_s, constr_s)
> constr_s = JuMP.@constraint(min_s, min_s[:hyd_energy] ≤ min_s[:hyd_energy_max]avai_vec_s)
> invg_s, cost_vec_s = min_s[:invg_s], min_s[:cost_vec_s]
> bias_s = JuMP.@expression(min_s, sum(cost_vec_s)prob_vec_s)
> JuMP.@objective(min_s, Min, β_s'invg_s + bias_s) # ✅ θs-β cut
> JuMP.optimize!(min_s); JuMP.assert_is_solved_and_feasible(min_s; allow_local = false)
> slope_s, bias_s = JuMP.value.(invg_s), JuMP.value(bias_s)
> return constr_s, JuMP.objective_value(min_s), slope_s, bias_s
> end
> 
> outer = JuMP.Model(() -> Gurobi.Optimizer(GRB_ENV)); JuMP.set_silent(outer); # Linear Programming
> JuMP.@variable(outer, outer_β[1:G, 1:S]); JuMP.@constraint(outer, sum(outer_β; dims = 2) .== 0);
> JuMP.@variable(outer, outer_O ≤ 1e3); # ⚠️ this ub needs to be valid
> JuMP.@variable(outer, outer_θ[1:S]);
> JuMP.@constraint(outer, outer_O == sum(outer_θ))
> good_objc = JuMP.@constraint(outer, 0 ≤ 1) # initial dumb
> function solve_outer_1(outer, good_objc)
> JuMP.delete(outer, good_objc)
> JuMP.@objective(outer, Max, outer[:outer_O])
> JuMP.optimize!(outer); JuMP.assert_is_solved_and_feasible(outer; allow_local = false)
> return dual_upper_bound = JuMP.objective_bound(outer)
> end;
> function solve_outer_2(outer, β, ub, lb)
> good_objc = JuMP.@constraint(outer, .7ub + .3lb ≤ outer[:outer_O])
> outer_β = outer[:outer_β]
> Δ = JuMP.@expression(outer, outer_β - β)
> JuMP.@objective(outer, Min, sum(x -> (x)x, Δ))
> JuMP.optimize!(outer); JuMP.assert_is_solved_and_feasible(outer; allow_local = false)
> vio = JuMP.MOI.get(JuMP.backend(outer), Gurobi.ModelAttribute("MaxVio"))
> vio > 9e-7 && @error "outer_QP: vio = $vio"
> return good_objc, JuMP.value.(outer_β)
> end;
> 
> β = zeros(G, S); # start from zero
> lbv = similar(prob_vec);
> for ite in 1:8
> global good_objc
> for s in 1:S
> global constr_s
> constr_s, lbv[s], slope_s, bias_s = solve_min_s(min_s, constr_s, view(β, :, s), avai_vec[s], prob_vec[s])
> JuMP.@constraint(outer, outer_θ[s] ≤ bias_s + slope_s'view(outer_β, :, s))
> end; lb = sum(lbv)
> ub = solve_outer_1(outer, good_objc)
> @info "ite = $ite, (P) lb = $lb | $ub = ub (D)"
> good_objc, β = solve_outer_2(outer, β, ub, lb)
> end
> 
> global good_objc # ite == 9
> for s in 1:S
> global constr_s
> constr_s, lbv[s], slope_s, bias_s = solve_min_s(min_s, constr_s, view(β, :, s), avai_vec[s], prob_vec[s])
> JuMP.@constraint(outer, outer_θ[s] ≤ bias_s + slope_s'view(outer_β, :, s))
> end; lb = sum(lbv)
> ub = solve_outer_1(outer, good_objc)
> @info "(P) lb = $lb | $ub = ub (D)"
> good_objc = JuMP.@constraint(outer, .7ub + .3lb ≤ outer[:outer_O])
> outer_β = outer[:outer_β]
> Δ = JuMP.@expression(outer, outer_β - β)
> JuMP.@objective(outer, Min, sum(x -> (x)x, Δ))
> JuMP.unset_silent(outer)
> JuMP.optimize!(outer)
> JuMP.solution_summary(outer)
> vio = JuMP.MOI.get(JuMP.backend(outer), Gurobi.ModelAttribute("MaxVio"))
> Gurobi.GRBwrite(JuMP.unsafe_backend(outer), "outer.mps")
> 
> ```

---

<div class="post-metadata">

**Author:** ![odow](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/odow/32/28685_2.png) [@odow](https://discourse.julialang.org/u/odow)\
**Post date:** [June 4, 2025, 9:02pm UTC](https://discourse.julialang.org/t/gurobi-reports-optimal-on-a-qp-having-a-huge-maxvio/129645/2 "2025-06-04T21:02:58Z")

</div>

When I run your code I get:

```julia
julia> JuMP.optimize!(outer)

julia> JuMP.solution_summary(outer)
solution_summary(; result = 1, verbose = false)
├ solver_name : Gurobi
├ Termination
│ ├ termination_status : NUMERICAL_ERROR
│ ├ result_count : 0
│ ├ raw_status : Optimization was terminated due to unrecoverable numerical difficulties.
│ └ objective_bound : -2.51388e+01
├ Solution (result = 1)
│ ├ primal_status : NO_SOLUTION
│ └ dual_status : NO_SOLUTION
└ Work counters
  ├ solve_time (sec) : 1.10602e-03
  ├ simplex_iterations : 0
  ├ barrier_iterations : 48
  └ node_count : 0

```

Can you turn logging on and provide the output?

Can you also provide the MPS file using these instructions: [GitHub - jump-dev/Gurobi.jl: A Julia interface to the Gurobi Optimizer](https://github.com/jump-dev/Gurobi.jl?tab=readme-ov-file#mps-files-for-support)

---

<div class="post-metadata">

**Author:** ![WalterMadelim](https://avatars.discourse-cdn.com/v4/letter/w/3e96dc/32.png) [@WalterMadelim](https://discourse.julialang.org/u/WalterMadelim)\
**Post date:** [June 4, 2025, 11:59pm UTC](https://discourse.julialang.org/t/gurobi-reports-optimal-on-a-qp-having-a-huge-maxvio/129645/3 "2025-06-04T23:59:17Z")

</div>

I’ve revised my post.  
And if the MPS file is a text one, I copy-paste its content here.

> **outer.mps**
>
> ```julia
> * Signature: 0xd23e3c025a2e18cb
> NAME 
> ROWS
> N OBJ
> E R0      
> E R1      
> E R2      
> E R3      
> E R4      
> E R5      
> E R6      
> E R7      
> E R8      
> E R9      
> E R10     
> E R11     
> E R12     
> E R13     
> E R14     
> E R15     
> E R16     
> E R17     
> E R18     
> E R19     
> L R20     
> L R21     
> L R23     
> L R24     
> L R26     
> L R27     
> L R29     
> L R30     
> L R32     
> L R33     
> L R35     
> L R36     
> L R38     
> L R39     
> L R41     
> L R42     
> L R44     
> L R45     
> L R46     
> COLUMNS
> outer_β[1,1] OBJ -8.6003572601445053e-18
> outer_β[1,1] R0 1
> outer_β[1,1] R20 -1.2487815708109435e+00
> outer_β[1,1] R23 -1.2487815708109435e+00
> outer_β[1,1] R26 -1.2487815708109435e+00
> outer_β[1,1] R29 -1.2487815708109435e+00
> outer_β[1,1] R32 -1.2487815708109435e+00
> outer_β[1,1] R35 -1.2487815708109435e+00
> outer_β[1,1] R38 -1.2487815708109435e+00
> outer_β[1,1] R41 -1.2487815708109435e+00
> outer_β[1,1] R44 -1.2487815708109435e+00
> outer_β[2,1] R1 1
> outer_β[3,1] R2 1
> outer_β[4,1] R3 1
> outer_β[5,1] R4 1
> outer_β[6,1] R5 1
> outer_β[7,1] OBJ -1.6144551946487720e-01
> outer_β[7,1] R6 1
> outer_β[7,1] R20 -6.2192242822179207e+00
> outer_β[7,1] R23 -5.9600000000000009e+00
> outer_β[7,1] R26 -5.9600000000000009e+00
> outer_β[7,1] R29 -5.9599999999999991e+00
> outer_β[7,1] R32 -5.9600000000000009e+00
> outer_β[7,1] R35 -6.1800000000000015e+00
> outer_β[7,1] R38 -6.1800000000000015e+00
> outer_β[7,1] R41 -6.1800000000000015e+00
> outer_β[7,1] R44 -6.1800000000000015e+00
> outer_β[8,1] R7 1
> outer_β[9,1] R8 1
> outer_β[10,1] OBJ -2.0963800747519235e-16
> outer_β[10,1] R9 1
> outer_β[10,1] R20 -1.4379999999999999e+01
> outer_β[10,1] R23 -1.4379999999999999e+01
> outer_β[10,1] R26 -1.4379999999999999e+01
> outer_β[10,1] R29 -1.4379999999999999e+01
> outer_β[10,1] R32 -1.4379999999999999e+01
> outer_β[10,1] R35 -1.4379999999999999e+01
> outer_β[10,1] R38 -1.4379999999999999e+01
> outer_β[10,1] R41 -1.4379999999999999e+01
> outer_β[10,1] R44 -1.4379999999999999e+01
> outer_β[11,1] R10 1
> outer_β[12,1] R11 1
> outer_β[13,1] R12 1
> outer_β[14,1] R13 1
> outer_β[15,1] R14 1
> outer_β[16,1] R15 1
> outer_β[17,1] OBJ 5.0888147531855576e+00
> outer_β[17,1] R16 1
> outer_β[17,1] R23 -300
> outer_β[17,1] R26 -300
> outer_β[17,1] R29 -3.1633971389895873e+01
> outer_β[17,1] R32 -3.1633971389895873e+01
> outer_β[17,1] R35 -3.1193971389895875e+01
> outer_β[17,1] R38 -3.1193971389895875e+01
> outer_β[17,1] R41 -3.1193971389895875e+01
> outer_β[17,1] R44 -3.1193971389895875e+01
> outer_β[18,1] R17 1
> outer_β[19,1] R18 1
> outer_β[1,2] OBJ 8.6003587510138856e-18
> outer_β[1,2] R0 1
> outer_β[1,2] R21 -1.2487815708109435e+00
> outer_β[1,2] R24 -1.2487815708109435e+00
> outer_β[1,2] R27 -1.2487815708109435e+00
> outer_β[1,2] R30 -1.2487815708109435e+00
> outer_β[1,2] R33 -1.2487815708109435e+00
> outer_β[1,2] R36 -1.2487815708109435e+00
> outer_β[1,2] R39 -1.2487815708109435e+00
> outer_β[1,2] R42 -1.2487815708109435e+00
> outer_β[1,2] R45 -1.2487815708109435e+00
> outer_β[2,2] R1 1
> outer_β[3,2] R2 1
> outer_β[4,2] R3 1
> outer_β[5,2] R4 1
> outer_β[6,2] R5 1
> outer_β[7,2] OBJ 1.6144551946487720e-01
> outer_β[7,2] R6 1
> outer_β[7,2] R21 -5.9600000000000009e+00
> outer_β[7,2] R24 -8.4713702725457214e+00
> outer_β[7,2] R27 -8.4713702725457214e+00
> outer_β[7,2] R30 -8.47137027254572
> outer_β[7,2] R33 -8.27137027254572
> outer_β[7,2] R36 -8.27137027254572
> outer_β[7,2] R39 -6.1800000000000015e+00
> outer_β[7,2] R42 -6.1800000000000015e+00
> outer_β[7,2] R45 -6.1800000000000015e+00
> outer_β[8,2] R7 1
> outer_β[9,2] R8 1
> outer_β[10,2] OBJ 2.0963797633358350e-16
> outer_β[10,2] R9 1
> outer_β[10,2] R21 -1.4379999999999999e+01
> outer_β[10,2] R24 -1.4379999999999999e+01
> outer_β[10,2] R27 -1.4379999999999999e+01
> outer_β[10,2] R30 -1.4379999999999999e+01
> outer_β[10,2] R33 -1.4379999999999999e+01
> outer_β[10,2] R36 -1.4379999999999999e+01
> outer_β[10,2] R39 -1.4379999999999999e+01
> outer_β[10,2] R42 -1.4379999999999999e+01
> outer_β[10,2] R45 -1.4379999999999999e+01
> outer_β[11,2] R10 1
> outer_β[12,2] R11 1
> outer_β[13,2] R12 1
> outer_β[14,2] R13 1
> outer_β[15,2] R14 1
> outer_β[16,2] R15 1
> outer_β[17,2] OBJ -5.0888147531855576e+00
> outer_β[17,2] R16 1
> outer_β[17,2] R21 -7.9626473632515964e+00
> outer_β[17,2] R36 -7.7955587556566606e+00
> outer_β[17,2] R39 -7.7955587556566606e+00
> outer_β[17,2] R42 -7.7955587556566606e+00
> outer_β[17,2] R45 -7.7955587556566606e+00
> outer_β[18,2] R17 1
> outer_β[19,2] R18 1
> outer_O R19 1
> outer_O R46 -1
> outer_θ[1] R19 -1
> outer_θ[1] R20 1
> outer_θ[1] R23 1
> outer_θ[1] R26 1
> outer_θ[1] R29 1
> outer_θ[1] R32 1
> outer_θ[1] R35 1
> outer_θ[1] R38 1
> outer_θ[1] R41 1
> outer_θ[1] R44 1
> outer_θ[2] R19 -1
> outer_θ[2] R21 1
> outer_θ[2] R24 1
> outer_θ[2] R27 1
> outer_θ[2] R30 1
> outer_θ[2] R33 1
> outer_θ[2] R36 1
> outer_θ[2] R39 1
> outer_θ[2] R42 1
> outer_θ[2] R45 1
> RHS
> RHS1 OBJ -1.2961050123997136e+01
> RHS1 R20 4.7570906242305813e+02
> RHS1 R21 4.0893792190132206e+02
> RHS1 R23 2.2280613703891331e+03
> RHS1 R24 4.3645454531102763e+02
> RHS1 R26 2.2280613703891331e+03
> RHS1 R27 4.3645454531102763e+02
> RHS1 R29 5.5638844562656345e+02
> RHS1 R30 4.3645454531102757e+02
> RHS1 R32 5.5638844562656345e+02
> RHS1 R33 4.3642879691102763e+02
> RHS1 R35 5.5476612987656347e+02
> RHS1 R36 4.0918669064075419e+02
> RHS1 R38 5.5476612987656347e+02
> RHS1 R39 4.0901559250170180e+02
> RHS1 R41 5.5476612987656347e+02
> RHS1 R42 4.0901559250170180e+02
> RHS1 R44 5.5476612987656347e+02
> RHS1 R45 4.0901559250170180e+02
> RHS1 R46 -9.0441271353580225e+02
> BOUNDS
> FR BND1 outer_β[1,1]
> FR BND1 outer_β[2,1]
> FR BND1 outer_β[3,1]
> FR BND1 outer_β[4,1]
> FR BND1 outer_β[5,1]
> FR BND1 outer_β[6,1]
> FR BND1 outer_β[7,1]
> FR BND1 outer_β[8,1]
> FR BND1 outer_β[9,1]
> FR BND1 outer_β[10,1]
> FR BND1 outer_β[11,1]
> FR BND1 outer_β[12,1]
> FR BND1 outer_β[13,1]
> FR BND1 outer_β[14,1]
> FR BND1 outer_β[15,1]
> FR BND1 outer_β[16,1]
> FR BND1 outer_β[17,1]
> FR BND1 outer_β[18,1]
> FR BND1 outer_β[19,1]
> FR BND1 outer_β[1,2]
> FR BND1 outer_β[2,2]
> FR BND1 outer_β[3,2]
> FR BND1 outer_β[4,2]
> FR BND1 outer_β[5,2]
> FR BND1 outer_β[6,2]
> FR BND1 outer_β[7,2]
> FR BND1 outer_β[8,2]
> FR BND1 outer_β[9,2]
> FR BND1 outer_β[10,2]
> FR BND1 outer_β[11,2]
> FR BND1 outer_β[12,2]
> FR BND1 outer_β[13,2]
> FR BND1 outer_β[14,2]
> FR BND1 outer_β[15,2]
> FR BND1 outer_β[16,2]
> FR BND1 outer_β[17,2]
> FR BND1 outer_β[18,2]
> FR BND1 outer_β[19,2]
> MI BND1 outer_O 
> UP BND1 outer_O 1000
> FR BND1 outer_θ[1]
> FR BND1 outer_θ[2]
> QUADOBJ
> outer_β[1,1] outer_β[1,1] 2
> outer_β[2,1] outer_β[2,1] 2
> outer_β[3,1] outer_β[3,1] 2
> outer_β[4,1] outer_β[4,1] 2
> outer_β[5,1] outer_β[5,1] 2
> outer_β[6,1] outer_β[6,1] 2
> outer_β[7,1] outer_β[7,1] 2
> outer_β[8,1] outer_β[8,1] 2
> outer_β[9,1] outer_β[9,1] 2
> outer_β[10,1] outer_β[10,1] 2
> outer_β[11,1] outer_β[11,1] 2
> outer_β[12,1] outer_β[12,1] 2
> outer_β[13,1] outer_β[13,1] 2
> outer_β[14,1] outer_β[14,1] 2
> outer_β[15,1] outer_β[15,1] 2
> outer_β[16,1] outer_β[16,1] 2
> outer_β[17,1] outer_β[17,1] 2
> outer_β[18,1] outer_β[18,1] 2
> outer_β[19,1] outer_β[19,1] 2
> outer_β[1,2] outer_β[1,2] 2
> outer_β[2,2] outer_β[2,2] 2
> outer_β[3,2] outer_β[3,2] 2
> outer_β[4,2] outer_β[4,2] 2
> outer_β[5,2] outer_β[5,2] 2
> outer_β[6,2] outer_β[6,2] 2
> outer_β[7,2] outer_β[7,2] 2
> outer_β[8,2] outer_β[8,2] 2
> outer_β[9,2] outer_β[9,2] 2
> outer_β[10,2] outer_β[10,2] 2
> outer_β[11,2] outer_β[11,2] 2
> outer_β[12,2] outer_β[12,2] 2
> outer_β[13,2] outer_β[13,2] 2
> outer_β[14,2] outer_β[14,2] 2
> outer_β[15,2] outer_β[15,2] 2
> outer_β[16,2] outer_β[16,2] 2
> outer_β[17,2] outer_β[17,2] 2
> outer_β[18,2] outer_β[18,2] 2
> outer_β[19,2] outer_β[19,2] 2
> ENDATA
> 
> ```

---

<div class="post-metadata">

**Author:** ![odow](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/odow/32/28685_2.png) [@odow](https://discourse.julialang.org/u/odow)\
**Post date:** [June 5, 2025, 12:06am UTC](https://discourse.julialang.org/t/gurobi-reports-optimal-on-a-qp-having-a-huge-maxvio/129645/4 "2025-06-05T00:06:13Z")

</div>

Something is really going wrong with your solve. Can you try this?

```julia
julia> import Gurobi

julia> env = Gurobi.Env()
Set parameter LicenseID to value 890341
Gurobi.Env(Ptr{Nothing} @0x00007fed1f8c9a00, false, 0)

julia> pModel = Ref{Ptr{Cvoid}}(C_NULL)
Base.RefValue{Ptr{Nothing}}(Ptr{Nothing} @0x0000000000000000)

julia> Gurobi.GRBreadmodel(env, "/tmp/bug.mps", pModel)
Read MPS format model from file /tmp/bug.mps
Reading time = 0.00 seconds
: 39 rows, 41 columns, 127 nonzeros
0

julia> Gurobi.GRBoptimize(pModel[])
Gurobi Optimizer version 12.0.2 build v12.0.2rc0 (mac64[x86] - Darwin 24.1.0 24B83)

CPU model: Intel(R) Core(TM) i5-8259U CPU @ 2.30GHz
Thread count: 4 physical cores, 8 logical processors, using up to 8 threads

Optimize a model with 39 rows, 41 columns and 127 nonzeros
Model fingerprint: 0x6abf7598
Model has 38 quadratic objective terms
Coefficient statistics:
  Matrix range [1e+00, 3e+02]
  Objective range [9e-18, 5e+00]
  QObjective range [2e+00, 2e+00]
  Bounds range [1e+03, 1e+03]
  RHS range [4e+02, 2e+03]
Presolve removed 11 rows and 2 columns
Presolve time: 0.00s
Presolved: 28 rows, 39 columns, 80 nonzeros
Presolved model has 38 quadratic objective terms
Ordering time: 0.00s

Barrier statistics:
 Free vars : 39
 AA' NZ : 6.900e+01
 Factor NZ : 1.060e+02
 Factor Ops : 8.340e+02 (less than 1 second per iteration)
 Threads : 1

                  Objective Residual
Iter Primal Dual Primal Dual Compl Time
   0 1.29618707e+01 1.29610499e+01 4.29e+02 1.02e+01 1.08e+06 0s
   1 7.79452936e+03 -9.29391938e+03 4.59e+01 4.59e-01 8.38e+04 0s
   2 2.34734323e+03 -3.67334728e+03 3.99e+00 4.00e-02 1.54e+04 0s
   3 9.12137558e+01 -8.83286873e+02 5.15e-01 5.25e-03 3.29e+03 0s
   4 2.66252969e+00 -3.68221392e+02 7.01e-02 5.97e-04 6.12e+02 0s
   5 6.33755441e+00 -1.41663694e+02 2.80e-01 3.33e-04 3.77e+02 0s
   6 1.04371742e+01 -7.60311350e+01 2.47e+00 2.22e-04 1.82e+02 0s
   7 1.19817416e+00 -1.80998379e+01 4.38e-01 3.88e-05 3.69e+01 0s
   8 1.11611446e-01 -1.91537672e+00 6.22e-15 1.84e-07 3.71e+00 0s
   9 8.92532211e-03 -4.57382751e-02 1.74e-15 1.56e-09 9.74e-02 0s
  10 7.97637457e-04 -6.28498269e-03 8.50e-17 5.34e-14 1.26e-02 0s
  11 2.13483370e-04 -6.80122803e-05 5.38e-18 1.78e-15 5.00e-04 0s
  12 1.34514789e-04 8.17619151e-05 1.83e-19 1.78e-15 9.38e-05 0s
  13 1.02313735e-04 9.58889782e-05 1.01e-20 1.78e-15 1.14e-05 0s
  14 1.04812007e-04 9.91673318e-05 1.93e-21 1.78e-15 1.00e-05 0s
  15 1.00771461e-04 1.00764000e-04 3.37e-21 1.78e-15 1.33e-08 0s
  16 1.00766770e-04 1.00766763e-04 1.99e-21 1.78e-15 1.33e-11 0s

Barrier solved model in 16 iterations and 0.00 seconds (0.00 work units)
Optimal objective 1.00766770e-04

0

```

---

<div class="post-metadata">

**Author:** ![WalterMadelim](https://avatars.discourse-cdn.com/v4/letter/w/3e96dc/32.png) [@WalterMadelim](https://discourse.julialang.org/u/WalterMadelim)\
**Post date:** [June 5, 2025, 12:16am UTC](https://discourse.julialang.org/t/gurobi-reports-optimal-on-a-qp-having-a-huge-maxvio/129645/5 "2025-06-05T00:16:15Z")

</div>

```julia
julia> env = Gurobi.Env()
Set parameter Username
Set parameter LicenseID to value 2663413
Academic license - for non-commercial use only - expires 2026-05-11
Gurobi.Env(Ptr{Nothing} @0x0000019a16b17ff0, false, 0)

julia> pModel = Ref{Ptr{Cvoid}}(C_NULL)
Base.RefValue{Ptr{Nothing}}(Ptr{Nothing} @0x0000000000000000)

julia> Gurobi.GRBreadmodel(env, "data/outer.mps", pModel)
Read MPS format model from file data/outer.mps
Reading time = 0.04 seconds
: 39 rows, 41 columns, 127 nonzeros
0

julia> Gurobi.GRBoptimize(pModel[])
Gurobi Optimizer version 12.0.2 build v12.0.2rc0 (win64 - Windows 11.0 (26100.2))

CPU model: 11th Gen Intel(R) Core(TM) i5-1135G7 @ 2.40GHz, instruction set [SSE2|AVX|AVX2|AVX512]
Thread count: 4 physical cores, 8 logical processors, using up to 8 threads

Optimize a model with 39 rows, 41 columns and 127 nonzeros
Model fingerprint: 0x6abf7598
Model has 38 quadratic objective terms
Coefficient statistics:
  Matrix range [1e+00, 3e+02]
  Objective range [9e-18, 5e+00]
  QObjective range [2e+00, 2e+00]
  Bounds range [1e+03, 1e+03]
  RHS range [4e+02, 2e+03]
Presolve removed 11 rows and 2 columns
Presolve time: 0.00s
Presolved: 28 rows, 39 columns, 80 nonzeros
Presolved model has 38 quadratic objective terms
Ordering time: 0.00s

Barrier statistics:
 Free vars : 39
 AA' NZ : 6.900e+01
 Factor NZ : 1.060e+02
 Factor Ops : 8.340e+02 (less than 1 second per iteration)
 Threads : 1

                  Objective Residual
Iter Primal Dual Primal Dual Compl Time
   0 1.29618707e+01 1.29610499e+01 4.29e+02 1.02e+01 1.08e+06 0s
   1 7.79452936e+03 -9.29391938e+03 4.59e+01 4.59e-01 8.38e+04 0s
   2 2.34735483e+03 -3.67336088e+03 3.99e+00 4.00e-02 1.54e+04 0s
   3 9.28074635e+01 -8.89553051e+02 5.28e-01 5.42e-03 3.32e+03 0s
   4 2.67970847e+00 -2.69217523e+02 6.93e-02 1.81e-04 4.56e+02 0s
   5 4.93346415e+00 -2.33632458e+02 6.85e-02 1.78e-04 4.42e+02 0s
   6 2.61337406e+00 -2.70695461e+02 3.15e-01 1.50e-04 4.63e+02 0s
   7 1.09725171e+01 -1.34247786e+02 5.05e-01 1.37e-04 3.67e+02 0s
   8 6.15979734e+00 -7.90459574e+01 2.02e-01 6.79e-05 1.87e+02 0s
   9 2.03422156e-02 -8.54163247e+01 1.06e-01 4.22e-05 1.57e+02 0s
  10 6.28528487e-01 -3.31827999e+01 2.99e-02 4.33e-11 3.35e+01 0s
  11 1.33921978e-03 -2.70800877e+01 4.09e-13 2.12e-12 1.23e+00 0s
  12 2.55952604e-04 -3.06308511e+01 1.99e-13 2.79e-12 1.10e-02 0s
  13 3.33498799e-04 -3.42217263e+01 3.09e-13 2.70e-12 1.19e-04 0s
  14 5.57483325e-04 -3.51843270e+01 9.52e-14 1.82e-12 4.34e-07 0s
  15 2.39292335e-04 -3.74834526e+01 2.37e-13 1.82e-12 2.28e-09 0s
  16 2.43578332e-04 -3.89336401e+01 1.87e-13 2.41e-12 1.43e-10 0s
  17 2.11498031e-03 -5.43399078e+01 4.19e-02 3.42e-12 7.46e-11 0s
  18 2.96317192e-04 -5.53941734e+01 1.40e-13 5.61e-12 2.39e-12 0s
  19 4.12094691e-04 -5.67513066e+01 5.10e-14 4.84e-12 3.94e-13 0s
  20 6.75682559e-04 -5.83702637e+01 1.42e-13 1.61e-12 4.12e-16 0s
  21 8.90546712e-04 -5.95038795e+01 2.26e-13 3.58e-12 1.14e-16 0s
  22 9.87057970e-04 -6.26382723e+01 1.59e-13 3.55e-12 2.54e-19 0s

Barrier solved model in 22 iterations and 0.01 seconds (0.00 work units)
Optimal objective 9.87057970e-04

0

julia> 

```

---

<div class="post-metadata">

**Author:** ![odow](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/odow/32/28685_2.png) [@odow](https://discourse.julialang.org/u/odow)\
**Post date:** [June 5, 2025, 12:34am UTC](https://discourse.julialang.org/t/gurobi-reports-optimal-on-a-qp-having-a-huge-maxvio/129645/6 "2025-06-05T00:34:02Z")

</div>

This looks like a bug. I’ll raise it with their support

---

<div class="post-metadata">

**Author:** ![WalterMadelim](https://avatars.discourse-cdn.com/v4/letter/w/3e96dc/32.png) [@WalterMadelim](https://discourse.julialang.org/u/WalterMadelim)\
**Post date:** [June 5, 2025, 12:47am UTC](https://discourse.julialang.org/t/gurobi-reports-optimal-on-a-qp-having-a-huge-maxvio/129645/7 "2025-06-05T00:47:47Z")

</div>

Thanks.

so the conclusion is: the model is itself numeric difficult,. In mac os gurobi gives an honest termination, whereas in win11 gurobi gives a dishonest OPTIMAL?

---

<div class="post-metadata">

**Author:** ![odow](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/odow/32/28685_2.png) [@odow](https://discourse.julialang.org/u/odow)\
**Post date:** [June 5, 2025, 1:03am UTC](https://discourse.julialang.org/t/gurobi-reports-optimal-on-a-qp-having-a-huge-maxvio/129645/8 "2025-06-05T01:03:21Z")

</div>

It’s a likely bug. I don’t have Windows, so I can’t investigate more. I’ve opened a support ticket and they are investigating.

---

<div class="post-metadata">

**Author:** ![odow](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/odow/32/28685_2.png) [@odow](https://discourse.julialang.org/u/odow)\
**Post date:** [July 4, 2025, 1:08am UTC](https://discourse.julialang.org/t/gurobi-reports-optimal-on-a-qp-having-a-huge-maxvio/129645/9 "2025-07-04T01:08:16Z")

</div>

Closing the loop: the incorrect status will be fixed in the next release of Gurobi. The underlying numerical issues have not been fixed.