Setting Options for the Dynamics Solvers¶

Occasionally it is necessary to change the built in parameters of the dynamics solvers used by for example the qutip.mesolve and qutip.mcsolve functions. The options for all dynamics solvers may be changed by using the Options class qutip.solver.Options.

options = Options()

the properties and default values of this class can be view via the print function:

print(options)

Output:

Options:
-----------
atol:              1e-08
rtol:              1e-06
method:            adams
order:             12
nsteps:            1000
first_step:        0
min_step:          0
max_step:          0
tidy:              True
num_cpus:          2
norm_tol:          0.001
norm_steps:        5
rhs_filename:      None
rhs_reuse:         False
seeds:             0
rhs_with_state:    False
average_expect:    True
average_states:    False
ntraj:             500
store_states:      False
store_final_state: False

These properties are detailed in the following table. Assuming options = Options():

Property	Default setting	Description
options.atol	1e-8	Absolute tolerance
options.rtol	1e-6	Relative tolerance
options.method	‘adams’	Solver method. Can be ‘adams’ (non-stiff) or ‘bdf’ (stiff)
options.order	12	Order of solver. Must be <=12 for ‘adams’ and <=5 for ‘bdf’
options.nsteps	1000	Max. number of steps to take for each interval
options.first_step	0	Size of initial step. 0 = determined automatically by solver.
options.min_step	0	Minimum step size. 0 = determined automatically by solver.
options.max_step	0	Maximum step size. 0 = determined automatically by solver.
options.tidy	True	Whether to run tidyup function on time-independent Hamiltonian.
options.store_final_state	False	Whether or not to store the final state of the evolution.
options.store_states	False	Whether or not to store the state vectors or density matrices.
options.rhs_filename	None	RHS filename when using compiled time-dependent Hamiltonians.
options.rhs_reuse	False	Reuse compiled RHS function. Useful for repetitive tasks.
options.rhs_with_state	False	Whether or not to include the state in the Hamiltonian function callback signature.
options.num_cpus	installed num of processors	Integer number of cpus used by mcsolve.
options.seeds	None	Array containing random number seeds for mcsolver.
options.norm_tol	1e-6	Tolerance used when finding wavefunction norm in mcsolve.
options.norm_steps	5	Max. number of steps used to find wavefunction’s norm to within norm_tol in mcsolve.
options.steady_state_average	False	Include an estimation of the steady state in mcsolve.
options.ntraj	500	Number of trajectories in stochastic solvers.
options.average_expect	True	Average expectation values over trajectories.
options.average_states	False	Average of the states over trajectories.
options.openmp_threads	installed num of processors	Number of OPENMP threads to use.
options.use_openmp	None	Use OPENMP for sparse matrix vector multiplication.

As an example, let us consider changing the number of processors used, turn the GUI off, and strengthen the absolute tolerance. There are two equivalent ways to do this using the Options class. First way,

options = Options()
options.num_cpus = 3
options.atol = 1e-10

or one can use an inline method,

options = Options(num_cpus=4, atol=1e-10)

Note that the order in which you input the options does not matter. Using either method, the resulting options variable is now:

print(options)

Output:

Options:
-----------
atol:              1e-10
rtol:              1e-06
method:            adams
order:             12
nsteps:            1000
first_step:        0
min_step:          0
max_step:          0
tidy:              True
num_cpus:          4
norm_tol:          0.001
norm_steps:        5
rhs_filename:      None
rhs_reuse:         False
seeds:             0
rhs_with_state:    False
average_expect:    True
average_states:    False
ntraj:             500
store_states:      False
store_final_state: False

To use these new settings we can use the keyword argument options in either the func:qutip.mesolve and qutip.mcsolve function. We can modify the last example as:

>>> mesolve(H0, psi0, tlist, c_op_list, [sigmaz()], options=options)
>>> mesolve(hamiltonian_t, psi0, tlist, c_op_list, [sigmaz()], H_args, options=options)

or:

>>> mcsolve(H0, psi0, tlist, ntraj,c_op_list, [sigmaz()], options=options)
>>> mcsolve(hamiltonian_t, psi0, tlist, ntraj, c_op_list, [sigmaz()], H_args, options=options)