Skip to content

getTermsQuadractic correctly returns linear terms #1132

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

Open
Joao-Dionisio wants to merge 5 commits into
base: master
Choose a base branch
from
+123 −7
Open

getTermsQuadractic correctly returns linear terms #1132

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
5 commits
Select commit Hold shift + click to select a range
2a8dc58
fix getTermsQuadratic
Joao-Dionisio Dec 17, 2025
4a7def6
test and changelog
Joao-Dionisio Dec 17, 2025
34666bf
add getVarFarkasCoef (no test)
Joao-Dionisio Dec 17, 2025
92da4ab
add back deleted changelog
Joao-Dionisio Dec 17, 2025
6ab6c2c
copilot suggestions
Joao-Dionisio Dec 17, 2025
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
2 changes: 2 additions & 0 deletions CHANGELOG.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -2,8 +2,10 @@

## Unreleased
### Added
- Added getVarFarkasCoef() (untested)
### Fixed
- all fundamental callbacks now raise an error if not implemented
- getTermsQuadratic() now correctly returns all linear terms
### Changed
### Removed

Expand Down
1 change: 1 addition & 0 deletions src/pyscipopt/scip.pxd
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -1001,6 +1001,7 @@ cdef extern from "scip/scip.h":
SCIP_Real SCIPgetDualboundRoot(SCIP* scip)
SCIP_Real SCIPgetVarRedcost(SCIP* scip, SCIP_VAR* var)
SCIP_RETCODE SCIPgetDualSolVal(SCIP* scip, SCIP_CONS* cons, SCIP_Real* dualsolval, SCIP_Bool* boundconstraint)
SCIP_Real SCIPgetVarFarkasCoef(SCIP* scip, SCIP_VAR* var)

# Reader plugin
SCIP_RETCODE SCIPincludeReader(SCIP* scip,
Expand Down
81 changes: 74 additions & 7 deletions src/pyscipopt/scip.pxi
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -316,7 +316,7 @@
if rc == SCIP_OKAY:
pass
elif rc == SCIP_ERROR:
raise Exception('SCIP: unspecified error!')

Check failure on line 319 in src/pyscipopt/scip.pxi

View workflow job for this annotation

GitHub Actions / test-coverage (3.11) 

SCIP: unspecified error!
elif rc == SCIP_NOMEMORY:
raise MemoryError('SCIP: insufficient memory error!')
elif rc == SCIP_READERROR:
Expand All @@ -335,7 +335,7 @@
raise Exception('SCIP: method cannot be called at this time'
+ ' in solution process!')
elif rc == SCIP_INVALIDDATA:
raise Exception('SCIP: error in input data!')

Check failure on line 338 in src/pyscipopt/scip.pxi

View workflow job for this annotation

GitHub Actions / test-coverage (3.11) 

SCIP: error in input data!
elif rc == SCIP_INVALIDRESULT:
raise Exception('SCIP: method returned an invalid result code!')
elif rc == SCIP_PLUGINNOTFOUND:
Expand Down Expand Up @@ -5730,6 +5730,13 @@

PyCons = Constraint.create(scip_cons)

# Store the original polynomial expression on the constraint so that
# helpers such as getTermsQuadratic can reconstruct full linear terms
# even if SCIP's internal quadratic representation does not expose
# all linear coefficients explicitly.
if PyCons.data is None:
PyCons.data = quadcons.expr
Copy link
Member

@mmghannam mmghannam Dec 18, 2025

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

We cannot get the data from SCIP instead of this added complexity?

Copy link
Member Author

@Joao-Dionisio Joao-Dionisio Jan 13, 2026

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

I couldn't find a way, Mo :/ It does seem that the SCIP method has this limitation


return PyCons

def _createConsNonlinear(self, cons, **kwargs):
Expand Down Expand Up @@ -6064,6 +6071,11 @@

PY_SCIP_CALL(SCIPaddCons(self._scip, scip_cons))
pycons = Constraint.create(scip_cons)
# propagate any problem data (such as the original Expr for
# expression-based constraints) from the temporary constraint
# created in createConsFromExpr to the final Constraint object
# that is returned to the user
pycons.data = (<Constraint>pycons_initial).data
PY_SCIP_CALL(SCIPreleaseCons(self._scip, &scip_cons))

return pycons
Expand Down Expand Up @@ -8085,8 +8097,17 @@
Returns
-------
bilinterms : list of tuple
Triples ``(var1, var2, coef)`` for terms of the form
``coef * var1 * var2`` with ``var1 != var2``.
quadterms : list of tuple
Triples ``(var, sqrcoef, lincoef)`` corresponding to diagonal
quadratic terms of the form ``sqrcoef * var**2`` and the linear
coefficient ``lincoef`` associated with the same variable when it
also appears linearly in the quadratic part.
linterms : list of tuple
Pairs ``(var, coef)`` for all variables with a nonzero linear
coefficient in the constraint, including variables that also
appear in quadratic or bilinear terms.
Comment on lines +8108 to +8110
Copy link
Contributor

@DominikKamp DominikKamp Jan 28, 2026

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Why would linterms contain terms, which are already contained in quadterms? Only purely linear terms should appear here. A variable in non-linear terms is not linear in this expression.


"""
cdef SCIP_EXPR* expr
Expand Down Expand Up @@ -8117,15 +8138,36 @@
assert self.checkQuadraticNonlinear(cons), "constraint is not quadratic"

expr = SCIPgetExprNonlinear(cons.scip_cons)
SCIPexprGetQuadraticData(expr, NULL, &nlinvars, &linexprs, &lincoefs, &nquadterms, &nbilinterms, NULL, NULL)
SCIPexprGetQuadraticData(expr, NULL, &nlinvars, &linexprs, &lincoefs,
&nquadterms, &nbilinterms, NULL, NULL)

linterms = []
bilinterms = []
quadterms = []

for termidx in range(nlinvars):
var = Variable.create(SCIPgetVarExprVar(linexprs[termidx]))
linterms.append((var, lincoefs[termidx]))
# First try to recover all linear coefficients from the original
# polynomial expression, if it has been stored on the Constraint.
if isinstance(cons.data, Expr):
lindict = {}
for term, coef in cons.data.terms.items():
if coef == 0.0:
continue
if len(term) == 1:
var = term[0]
key = var.ptr()
if key in lindict:
_, oldcoef = lindict[key]
lindict[key] = (var, oldcoef + coef)
else:
lindict[key] = (var, coef)
for _, (var, coef) in lindict.items():
linterms.append((var, coef))
else:
# use only the purely linear part as exposed by SCIP's quadratic representation
for termidx in range(nlinvars):
scipvar1 = SCIPgetVarExprVar(linexprs[termidx])
var = Variable.create(scipvar1)
linterms.append((var, lincoefs[termidx]))

for termidx in range(nbilinterms):
SCIPexprGetQuadraticBilinTerm(expr, termidx, &bilinterm1, &bilinterm2, &bilincoef, NULL, NULL)
Expand All @@ -8137,13 +8179,13 @@
bilinterms.append((var1,var2,bilincoef))
else:
quadterms.append((var1,bilincoef,0.0))

for termidx in range(nquadterms):
SCIPexprGetQuadraticQuadTerm(expr, termidx, NULL, &lincoef, &sqrcoef, NULL, NULL, &sqrexpr)
if sqrexpr == NULL:
continue
Comment on lines 8184 to 8185
Copy link
Contributor

@DominikKamp DominikKamp Jan 28, 2026 •
edited
Loading

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Suggested change
if sqrexpr == NULL:
continue

Use expr instead (third pointer argument).

var = Variable.create(SCIPgetVarExprVar(sqrexpr))
quadterms.append((var,sqrcoef,lincoef))
scipvar1 = SCIPgetVarExprVar(sqrexpr)
var = Variable.create(scipvar1)
quadterms.append((var, sqrcoef, lincoef))

return (bilinterms, quadterms, linterms)

Expand Down Expand Up @@ -8488,6 +8530,31 @@

return _dualsol

def getVarFarkasCoef(self, Variable var):
"""
Returns the Farkas coefficient of the variable in the current node's LP relaxation; the current node has to have an infeasible LP.

Parameters
----------
var : Variable
variable to get the farkas coefficient of

Returns
-------
float

"""
assert SCIPgetStatus(self._scip) == SCIP_STATUS_INFEASIBLE, "Method can only be called with an infeasible model."

farkas_coef = None
try:
farkas_coef = SCIPgetVarFarkasCoef(self._scip, var.scip_var)
if self.getObjectiveSense() == "maximize":
farkas_coef = -farkas_coef
except Exception:
raise Warning("no farkas coefficient available for variable " + var.name)
return farkas_coef

def optimize(self):
"""Optimize the problem."""
PY_SCIP_CALL(SCIPsolve(self._scip))
Expand Down
46 changes: 46 additions & 0 deletions tests/test_nonlinear.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -288,6 +288,52 @@ def test_quad_coeffs():
assert linterms[0][0].name == z.name
assert linterms[0][1] == 4


def test_quad_coeffs_mixed_linear_and_quadratic():

scip = Model()

var1 = scip.addVar(name="var1", vtype='C', lb=None)
var2 = scip.addVar(name="var2", vtype='C')
var3 = scip.addVar(name="var3", vtype='B')
var4 = scip.addVar(name="var4", vtype='B')

cons = scip.addCons(
8 * var4
+ 4 * var3
- 5 * var2
+ 6 * var3 ** 2
- 3 * var1 ** 2
+ 2 * var2 * var1
+ 7 * var1 * var3
== -2
)

bilinterms, quadterms, linterms = scip.getTermsQuadratic(cons)

# Linear part: getTermsQuadratic must report all linear coefficients,
# including those of variables that also appear quadratically or
# bilinearly.
lin_only = {v.name: c for (v, c) in linterms}
assert lin_only["var4"] == 8
assert lin_only["var3"] == 4
assert lin_only["var2"] == -5
# var1 has no linear component and must not appear in linterms
assert "var1" not in lin_only or lin_only.get("var1", 0.0) == 0.0

# For completeness, checking if the coefficients from reconstructing the full linear
# coefficients from both linterms and quadterms match
full_lin = {}
for v, c in linterms:
full_lin[v.name] = full_lin.get(v.name, 0.0) + c
for v, _, lincoef in quadterms:
if lincoef != 0.0:
full_lin[v.name] = full_lin.get(v.name, 0.0) + lincoef

assert full_lin["var4"] == 8
assert full_lin["var3"] == 4
assert full_lin["var2"] == -5

def test_addExprNonLinear():
m = Model()
x = m.addVar("x", lb=0, ub=1, obj=10)
Expand Down