Optimize action fails for certain optimize methods when tol is given
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Summary
finesse.analysis.actions.optimize fails for certain methods when a tolerance is given. The function calls scipy.optimize.minimize and passes tol as a list, but this should be a float (https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html).
See line 247 of finesse/analysis/actions/optimisation.py
Steps to reproduce
Minimal example:
model = finesse.Model()
model.parse(
"""
l l1 P=1
pd P l1.p1.o
"""
)
sol = model.run(
Minimize(model.P, model.l1.P, method=None, bounds=[-2, 2], tol=1e-8)
)Bug behavior
Crashes for certain methods. In this case
What is the expected correct behavior?
Should not crash.
Relevant logs and/or screenshots
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Cell In[11], line 19
4 base = finesse.Model()
5 base.parse(
6 """
7 l l1 P=1
(...) 16 """
17 )
---> 19 base.run(Maximize(base.P, base.m2_z.DC, method=None, bounds=[-2, 2], tol=1e-10, verbose=True))
20 print(base.m2_z)
21 sol = base.run()
File ~/miniconda3/envs/finessedoc/lib/python3.14/site-packages/finesse/model.py:110, in locked_when_built.<locals>.wrapper(self, *args, **kwargs)
106 if self.is_built:
107 raise Exception(
108 f"Model has been built for a simulation, cannot use {func} here"
109 )
--> 110 return func(self, *args, **kwargs)
File ~/miniconda3/envs/finessedoc/lib/python3.14/site-packages/finesse/model.py:3322, in Model.run(self, analysis, return_state, progress_bar, simulation_type, simulation_options)
3319 else:
3320 raise ValueError(f"Cannot handle analysis input `{analysis}`")
-> 3322 rtn = _analysis._run(
3323 self,
3324 return_state=return_state,
3325 progress_bar=progress_bar,
3326 simulation_type=simulation_type,
3327 simulation_options=simulation_options,
3328 )
3330 self.analysis = curr_analysis
3332 if not return_state:
File ~/miniconda3/envs/finessedoc/lib/python3.14/site-packages/finesse/analysis/actions/base.py:239, in Action._run(self, model, return_state, progress_bar, simulation_type, simulation_options)
236 else:
237 action = self
--> 239 result = state.apply(action)
241 if type(result) is tuple:
242 sol = BaseSolution("root")
File ~/miniconda3/envs/finessedoc/lib/python3.14/site-packages/finesse/analysis/actions/base.py:105, in AnalysisState.apply(self, action)
103 def apply(self, action):
104 start = time.time_ns()
--> 105 sol = action._do(self)
106 if sol is not None:
107 if not isinstance(sol, BaseSolution):
File ~/miniconda3/envs/finessedoc/lib/python3.14/site-packages/finesse/analysis/actions/series.py:65, in Series._do(self, state)
63 for action in pbar:
64 pbar.set_description_str(self.name)
---> 65 next_sol = state.apply(action)
66 if self.flatten and next_sol is not None:
67 first.add(next_sol)
File ~/miniconda3/envs/finessedoc/lib/python3.14/site-packages/finesse/analysis/actions/base.py:105, in AnalysisState.apply(self, action)
103 def apply(self, action):
104 start = time.time_ns()
--> 105 sol = action._do(self)
106 if sol is not None:
107 if not isinstance(sol, BaseSolution):
File ~/miniconda3/envs/finessedoc/lib/python3.14/site-packages/finesse/analysis/actions/optimisation.py:243, in Optimize._do(self, state)
240 else:
241 opfunc = self.opfunc
--> 243 res = minimize(
244 opfunc,
245 np.array([_.value for _ in params]),
246 bounds=np.atleast_2d(self.bounds) if self.bounds else None,
247 tol=[self.tol] if self.tol else None,
248 options={"maxiter": self.max_iterations, **self.kwargs},
249 method=self.method,
250 args=(params, state, dws),
251 )
252 if res.nit == 1:
253 warnings.warn(
254 "Optimisation finished after 1 iteration, tolerances might be too high",
255 OptimizationWarning,
256 stacklevel=1,
257 )
File ~/miniconda3/envs/finessedoc/lib/python3.14/site-packages/scipy/optimize/_minimize.py:784, in minimize(fun, x0, args, method, jac, hess, hessp, bounds, constraints, tol, callback, options)
781 res = _minimize_newtoncg(fun, x0, args, jac, hess, hessp, callback,
782 **options)
783 elif meth == 'l-bfgs-b':
--> 784 res = _minimize_lbfgsb(fun, x0, args, jac, bounds,
785 callback=callback, **options)
786 elif meth == 'tnc':
787 res = _minimize_tnc(fun, x0, args, jac, bounds, callback=callback,
788 **options)
File ~/miniconda3/envs/finessedoc/lib/python3.14/site-packages/scipy/optimize/_lbfgsb_py.py:461, in _minimize_lbfgsb(fun, x0, args, jac, bounds, disp, maxcor, ftol, gtol, eps, maxfun, maxiter, iprint, callback, maxls, finite_diff_rel_step, workers, **unknown_options)
459 g = g.astype(np.float64)
460 # x, f, g, wa, iwa, task, csave, lsave, isave, dsave = \
--> 461 _lbfgsb.setulb(m, x, low_bnd, upper_bnd, nbd, f, g, factr, pgtol, wa,
462 iwa, task, lsave, isave, dsave, maxls, ln_task)
464 if task[0] == 3:
465 # The minimization routine wants f and g at the current x.
466 # Note that interruptions due to maxfun are postponed
467 # until the completion of the current minimization iteration.
468 # Overwrite f and g:
469 f, g = func_and_grad(x)
TypeError: only 0-dimensional arrays can be converted to Python scalarsPossible Fixes
Do not put tol in a list.
Urgency
If the bug is preventing Finesse from being used, for example if it produces seemingly valid but incorrect results, add this label.