Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

Add farkas dual for variable bounds #363

Merged
merged 9 commits into from
Nov 5, 2020
Merged

Add farkas dual for variable bounds #363

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
9 commits
Select commit Hold shift + click to select a range
cbd0243
Add farkas dual for variable bounds
odow Oct 28, 2020
b7b0e8f
Switch to using GRBgetvars
odow Oct 28, 2020
9db94d8
Add tests for min and max
odow Oct 28, 2020
f5ecf02
Update signs of FarkasDual
odow Oct 29, 2020
8d364b2
Inner product instead of sum
odow Oct 29, 2020
694dcc8
More farkas tests
odow Nov 2, 2020
0b5ee02
Add tests as broken
odow Nov 3, 2020
0250343
Add more tests for EqualTo and Interval constraints
odow Nov 3, 2020
8739ad0
Update MOI_wrapper.jl
odow Nov 5, 2020
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
86 changes: 62 additions & 24 deletions src/MOI_wrapper.jl
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -2512,17 +2512,47 @@ function _dual_multiplier(model::Optimizer)
return MOI.get(model, MOI.ObjectiveSense()) == MOI.MIN_SENSE ? 1.0 : -1.0
end

"""
_farkas_variable_dual(model::Optimizer, col::Cint)

Return a Farkas dual associated with the variable bounds of `col`.

Gurobi computes the Farkas dual as:

ā * x = λ' * A * x <= λ' * b = -β + sum(āᵢ * Uᵢ | āᵢ < 0) + sum(āᵢ * Lᵢ | āᵢ > 0)

The Farkas dual of the variable is ā, and it applies to the upper bound if ā < 0,
and it applies to the lower bound if ā > 0.
"""
function _farkas_variable_dual(model::Optimizer, col::Cint)
numnzP = Ref{Cint}()
ret = GRBgetvars(model, numnzP, C_NULL, C_NULL, C_NULL, col, 1)
_check_ret(model, ret)
vbeg = Vector{Cint}(undef, 2)
vind = Vector{Cint}(undef, numnzP[])
vval = Vector{Cdouble}(undef, numnzP[])
ret = GRBgetvars(model, numnzP, vbeg, vind, vval, col, 1)
_check_ret(model, ret)
λ = Vector{Cdouble}(undef, numnzP[])
ret = GRBgetdblattrlist(model, "FarkasDual", length(vind), vind, λ)
_check_ret(model, ret)
return λ' * vval
end

function MOI.get(
model::Optimizer,
attr::MOI.ConstraintDual,
c::MOI.ConstraintIndex{MOI.SingleVariable, MOI.LessThan{Float64}},
)
_throw_if_optimize_in_progress(model, attr)
MOI.check_result_index_bounds(model, attr)
col = Cint(column(model, c) - 1)
if model.has_infeasibility_cert
dual = _farkas_variable_dual(model, col)
return min(dual, 0.0)
end
reduced_cost = Ref{Cdouble}()
ret = GRBgetdblattrelement(
model, "RC", Cint(column(model, c) - 1), reduced_cost
)
ret = GRBgetdblattrelement(model, "RC", col, reduced_cost)
_check_ret(model, ret)
sense = MOI.get(model, MOI.ObjectiveSense())
# The following is a heuristic for determining whether the reduced cost
Expand Down Expand Up @@ -2550,10 +2580,13 @@ function MOI.get(
)
_throw_if_optimize_in_progress(model, attr)
MOI.check_result_index_bounds(model, attr)
col = Cint(column(model, c) - 1)
if model.has_infeasibility_cert
dual = _farkas_variable_dual(model, col)
return max(dual, 0.0)
end
reduced_cost = Ref{Cdouble}()
ret = GRBgetdblattrelement(
model, "RC", Cint(column(model, c) - 1), reduced_cost
)
ret = GRBgetdblattrelement(model, "RC", col, reduced_cost)
_check_ret(model, ret)
sense = MOI.get(model, MOI.ObjectiveSense())
# The following is a heuristic for determining whether the reduced cost
Expand Down Expand Up @@ -2581,11 +2614,12 @@ function MOI.get(
)
_throw_if_optimize_in_progress(model, attr)
MOI.check_result_index_bounds(model, attr)
col = Cint(column(model, c) - 1)
if model.has_infeasibility_cert
return _farkas_variable_dual(model, col)
end
reduced_cost = Ref{Cdouble}()
reduced_cost = Ref{Cdouble}()
ret = GRBgetdblattrelement(
model, "RC", Cint(column(model, c) - 1), reduced_cost
)
ret = GRBgetdblattrelement(model, "RC", col, reduced_cost)
_check_ret(model, ret)
return _dual_multiplier(model) * reduced_cost[]
end
Expand All @@ -2597,41 +2631,45 @@ function MOI.get(
)
_throw_if_optimize_in_progress(model, attr)
MOI.check_result_index_bounds(model, attr)
col = Cint(column(model, c) - 1)
if model.has_infeasibility_cert
return _farkas_variable_dual(model, col)
end
reduced_cost = Ref{Cdouble}()
ret = GRBgetdblattrelement(
model, "RC", Cint(column(model, c) - 1), reduced_cost
)
ret = GRBgetdblattrelement(model, "RC", col, reduced_cost)
_check_ret(model, ret)
return _dual_multiplier(model) * reduced_cost[]
end

function MOI.get(
model::Optimizer, attr::MOI.ConstraintDual,
c::MOI.ConstraintIndex{MOI.ScalarAffineFunction{Float64}, <:Any}
model::Optimizer,
attr::MOI.ConstraintDual,
c::MOI.ConstraintIndex{MOI.ScalarAffineFunction{Float64}, <:Any},
)
_throw_if_optimize_in_progress(model, attr)
MOI.check_result_index_bounds(model, attr)
valueP = Ref{Cdouble}()
row = Cint(_info(model, c).row - 1)
if model.has_infeasibility_cert
ret = GRBgetdblattrelement(
model, "FarkasDual", Cint(_info(model, c).row - 1), valueP
)
ret = GRBgetdblattrelement(model, "FarkasDual", row, valueP)
_check_ret(model, ret)
return -_dual_multiplier(model) * valueP[]
return -valueP[]
end
ret = GRBgetdblattrelement(
model, "Pi", Cint(_info(model, c).row - 1), valueP
)
ret = GRBgetdblattrelement(model, "Pi", row, valueP)
_check_ret(model, ret)
return _dual_multiplier(model) * valueP[]
end

function MOI.get(
model::Optimizer, attr::MOI.ConstraintDual,
c::MOI.ConstraintIndex{MOI.ScalarQuadraticFunction{Float64}, <:Any}
model::Optimizer,
attr::MOI.ConstraintDual,
c::MOI.ConstraintIndex{MOI.ScalarQuadraticFunction{Float64}, <:Any},
)
_throw_if_optimize_in_progress(model, attr)
MOI.check_result_index_bounds(model, attr)
if model.has_infeasibility_cert
error("Infeasibility certificate not available for $(c).")
end
valueP = Ref{Cdouble}()
ret = GRBgetdblattrelement(
model, "QCPi", Cint(_info(model, c).row - 1), valueP
Expand Down
Loading