icecube.lilliput.scipymin module

class icecube.lilliput.scipymin.SciPyMinimizer(name, method='L-BFGS-B', tolerance=10000000.0, max_iterations=100, options=None)

Bases: I3Minimizer

Multi-dimensional minimizers in SciPy

Parameters:

  • name (str) – Name for minimizer service

  • method (str) – Name of minimization method

  • tolerance (float) – Tolerance for minimization termination

  • max_iterations (int) – Maximum number of iterations

  • options (dict) – Options that are passed to the minimizer; see scipy.optimize.

GetName()
Minimize((I3Minimizer)arg1, (I3GulliverBase)arg2, (I3FitParameterInitSpecsSeries)arg3) → I3MinimizerResult :
C++ signature :

I3MinimizerResult Minimize(I3MinimizerBase {lvalue},I3GulliverBase {lvalue},std::__1::vector<I3FitParameterInitSpecs, std::__1::allocator<I3FitParameterInitSpecs>>)

Minimize( (I3Minimizer)arg1, (I3GulliverBase)arg2, (I3FitParameterInitSpecsSeries)arg3) -> None :

C++ signature :

void Minimize(I3MinimizerWrapper {lvalue},I3GulliverBase {lvalue},std::__1::vector<I3FitParameterInitSpecs, std::__1::allocator<I3FitParameterInitSpecs>>)

UsesGradient((I3Minimizer)arg1) → bool :
C++ signature :

bool UsesGradient(I3MinimizerBase {lvalue})

methods = {'Anneal': (False, False), 'BFGS': (True, False), 'CG': (True, False), 'COBYLA': (False, False), 'L-BFGS-B': (True, False), 'Nelder-Mead': (False, False), 'Newton-CG': (True, True), 'Powell': (False, False), 'SLSQP': (True, False), 'TNC': (True, False), 'dogleg': (True, True), 'trust-ncg': (True, True)}