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Fix tests with scipy 1.16 #40285

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Fix tests with scipy 1.16 #40285

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af982ea
Drop depecated iprint argument from optimize.fmin_l_bfgs_b
antonio-rojas Jun 22, 2025
094ca44
Adapt tests to new COBYLA implementation
antonio-rojas Jun 22, 2025
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10 changes: 5 additions & 5 deletions src/sage/numerical/optimize.py
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Original file line number Diff line number Diff line change
Expand Up @@ -519,9 +519,9 @@ def minimize_constrained(func, cons, x0, gradient=None, algorithm='default', **a
sage: x, y = var('x y')
sage: f(x,y) = (100 - x) + (1000 - y)
sage: c(x,y) = x + y - 479 # > 0
sage: minimize_constrained(f, [c], [100, 300])
sage: minimize_constrained(f, [c], [100, 300]) # random
(805.985..., 1005.985...)
sage: minimize_constrained(f, c, [100, 300])
sage: minimize_constrained(f, c, [100, 300]) # random
(805.985..., 1005.985...)

If ``func`` is symbolic, its minimizer should be in the same order
Expand All @@ -532,7 +532,7 @@ def minimize_constrained(func, cons, x0, gradient=None, algorithm='default', **a
sage: f(y,x) = x - y
sage: c1(y,x) = x
sage: c2(y,x) = 1-y
sage: minimize_constrained(f, [c1, c2], (0,0))
sage: minimize_constrained(f, [c1, c2], (0,0)) # abs tol 1e-04
(1.0, 0.0)
"""
from sage.structure.element import Expression
Expand Down Expand Up @@ -567,12 +567,12 @@ def minimize_constrained(func, cons, x0, gradient=None, algorithm='default', **a
if isinstance(cons[0], (tuple, list)) or cons[0] is None:
if gradient is not None:
if algorithm == 'l-bfgs-b':
min = optimize.fmin_l_bfgs_b(f, x0, gradient, bounds=cons, iprint=-1, **args)[0]
min = optimize.fmin_l_bfgs_b(f, x0, gradient, bounds=cons, **args)[0]
else:
min = optimize.fmin_tnc(f, x0, gradient, bounds=cons, messages=0, **args)[0]
else:
if algorithm == 'l-bfgs-b':
min = optimize.fmin_l_bfgs_b(f, x0, approx_grad=True, bounds=cons, iprint=-1, **args)[0]
min = optimize.fmin_l_bfgs_b(f, x0, approx_grad=True, bounds=cons, **args)[0]
else:
min = optimize.fmin_tnc(f, x0, approx_grad=True, bounds=cons, messages=0, **args)[0]
elif isinstance(cons[0], (function_type, Expression)):
Expand Down
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