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Add independent variable rows to sensitivity toolbox Pynumero-based sensitivity calcs #3655

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Merged
merged 5 commits into from
Jul 24, 2025
Merged

Add independent variable rows to sensitivity toolbox Pynumero-based sensitivity calcs #3655

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705479a
Add parameter rows to dsdp
Jul 8, 2025
86a0623
Merge branch 'main' into pynum_sens
mrmundt Jul 8, 2025
53fa926
Merge branch 'main' of https://github.com/pyomo/pyomo into pynum_sens
Jul 8, 2025
6a15e3c
Run black
Jul 8, 2025
d771a60
Merge branch 'pynum_sens' of https://github.com/eslickj/pyomo into py…
Jul 8, 2025
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11 changes: 10 additions & 1 deletion pyomo/contrib/sensitivity_toolbox/pynumero.py
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Original file line number Diff line number Diff line change
Expand Up @@ -62,7 +62,7 @@ def get_dsdp_dfdp(model, theta):
Returns
-------
scipy.sparse.csc_matrix, csc_matrix, ComponentMap, ComponentMap
ds/dp (ns by np), df/dp (1 by np), row map, column map.
ds/dp (ns + np by np), df/dp (1 by np), row map, column map.
The column map maps Pyomo variables p to columns and the
row map maps Pyomo variables s to rows.
"""
Expand Down Expand Up @@ -98,6 +98,15 @@ def get_dsdp_dfdp(model, theta):
_coo_reorder_cols(dfdx, remap=col_remap)
dfdx = dfdx.tocsc()
dfdp = dfdx[0, :ns] @ dsdp + dfdx[0, ns:]

# Add independent variables to the rows as the end
dsdp = scipy.sparse.vstack(
[dsdp, scipy.sparse.identity(len(column_map), format="csc")]
)
n = len(row_map)
for p, i in column_map.items():
row_map[p] = i + n

# return sensitivity of the outputs to p and component maps
return dsdp, dfdp, row_map, column_map

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6 changes: 5 additions & 1 deletion pyomo/contrib/sensitivity_toolbox/tests/test_pynumero.py
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Expand Up @@ -73,11 +73,15 @@ def test_dsdp_dfdp_pyomo_nlp(self):
dsdp, dfdp, rmap, cmap = pnsens.get_dsdp_dfdp(m2, theta)

# Since x1 = p1
assert dsdp.shape == (2, 2)
assert dsdp.shape == (4, 2)
np.testing.assert_almost_equal(dsdp[rmap[m.x1], cmap[m.p1]], 40.0)
np.testing.assert_almost_equal(dsdp[rmap[m.x1], cmap[m.p2]], 0.0)
np.testing.assert_almost_equal(dsdp[rmap[m.x2], cmap[m.p1]], 0.0)
np.testing.assert_almost_equal(dsdp[rmap[m.x2], cmap[m.p2]], 1.0)
np.testing.assert_almost_equal(dsdp[rmap[m.p1], cmap[m.p2]], 0.0)
np.testing.assert_almost_equal(dsdp[rmap[m.p2], cmap[m.p2]], 1.0)
np.testing.assert_almost_equal(dsdp[rmap[m.p1], cmap[m.p1]], 1.0)
np.testing.assert_almost_equal(dsdp[rmap[m.p2], cmap[m.p1]], 0.0)

# if x1 = 2 * p1 and x2 = p2 then
# df/dp1 = 6 p1**2 + p2 = 45.0
Expand Down