fitbenchmarking.controllers.nlopt_controller module
Implements a controller for the NLOPT software.
- class fitbenchmarking.controllers.nlopt_controller.NLoptController(cost_func)
Bases:
fitbenchmarking.controllers.base_controller.Controller
Controller for NLOPT
- algorithm_check = {'all': ['LN_BOBYQA', 'LN_NEWUOA', 'LN_NEWUOA_BOUND', 'LN_PRAXIS', 'LD_SLSQP', 'LD_VAR2', 'LD_VAR1', 'AUGLAG', 'AUGLAG_EQ', 'LN_NELDERMEAD', 'LN_SBPLX', 'LN_COBYLA', 'LD_CCSAQ', 'LD_MMA', 'LD_TNEWTON_PRECOND_RESTART', 'LD_TNEWTON_PRECOND', 'LD_TNEWTON_RESTART', 'LD_TNEWTON', 'LD_LBFGS', 'GN_DIRECT', 'GN_DIRECT_L', 'GN_DIRECT_L_RAND', 'GNL_DIRECT_NOSCAL', 'GN_DIRECT_L_NOSCAL', 'GN_DIRECT_L_RAND_NOSCAL', 'GN_ORIG_DIRECT', 'GN_ORIG_DIRECT_L', 'GN_CRS2_LM', 'G_MLSL_LDS', 'G_MLSL', 'GD_STOGO', 'GD_STOGO_RAND', 'GN_AGS', 'GN_ISRES'], 'bfgs': ['LD_LBFGS'], 'conjugate_gradient': ['LN_COBYLA'], 'deriv_free': ['LN_BOBYQA', 'LN_NEWUOA', 'LN_NEWUOA_BOUND', 'LN_PRAXIS'], 'gauss_newton': ['LD_TNEWTON_PRECOND_RESTART', 'LD_TNEWTON_PRECOND', 'LD_TNEWTON_RESTART', 'LD_TNEWTON'], 'general': ['LD_SLSQP', 'LD_VAR2', 'LD_VAR1', 'AUGLAG', 'AUGLAG_EQ'], 'global_optimization': ['GN_DIRECT', 'GN_DIRECT_L', 'GN_DIRECT_L_RAND', 'GNL_DIRECT_NOSCAL', 'GN_DIRECT_L_NOSCAL', 'GN_DIRECT_L_RAND_NOSCAL', 'GN_ORIG_DIRECT', 'GN_ORIG_DIRECT_L', 'GN_CRS2_LM', 'G_MLSL_LDS', 'G_MLSL', 'GD_STOGO', 'GD_STOGO_RAND', 'GN_AGS', 'GN_ISRES'], 'levenberg-marquardt': [], 'ls': [], 'simplex': ['LN_NELDERMEAD', 'LN_SBPLX'], 'steepest_descent': [], 'trust_region': ['LN_COBYLA', 'LD_CCSAQ', 'LD_MMA']}
Within the controller class, you must initialize a dictionary,
algorithm_check
, such that the keys are given by:all
- all minimizersls
- least-squares fitting algorithmsderiv_free
- derivative free algorithms (these are algorithms that cannot use information about derivatives – e.g., theSimplex
method inMantid
)general
- minimizers which solve a generic min f(x)simplex
- derivative free simplex based algorithms e.g. Nelder-Meadtrust_region
- algorithms which emply a trust region approachlevenberg-marquardt
- minimizers that use the Levenberg-Marquardt algorithmgauss_newton
- minimizers that use the Gauss Newton algorithmbfgs
- minimizers that use the BFGS algorithmconjugate_gradient
- Conjugate Gradient algorithmssteepest_descent
- Steepest Descent algorithmsglobal_optimization
- Global Optimization algorithms
The values of the dictionary are given as a list of minimizers for that specific controller that fit into each of the above categories. See for example the
GSL
controller.
- cleanup()
Convert the result to a numpy array and populate the variables results will be read from
- fit()
Run problem with NLOPT
- jacobian_enabled_solvers = ['LD_SLSQP', 'LD_VAR2', 'LD_VAR1', 'LD_CCSAQ', 'LD_MMA', 'LD_TNEWTON_PRECOND_RESTART', 'LD_TNEWTON_PRECOND', 'LD_TNEWTON_RESTART', 'LD_TNEWTON', 'GD_STOGO', 'GD_STOGO_RAND']
Within the controller class, you must define the list
jacobian_enabled_solvers
if any of the minimizers for the specific software are able to use jacobian information.jacobian_enabled_solvers
: a list of minimizers in a specific
software that allow Jacobian information to be passed into the fitting algorithm
- objective_master_nlopt(x, grad)
NLOPT objective function
- setup()
Setup problem ready to be run with NLOPT