SlsqpOptimizer
class SlsqpOptimizer(iter=100, acc=1e-06, iprint=0, trials=1, clip=100.0, full_output=False)
Bases: qiskit.optimization.algorithms.multistart_optimizer.MultiStartOptimizer
The SciPy SLSQP optimizer wrapped as an Qiskit OptimizationAlgorithm.
This class provides a wrapper for scipy.optimize.fmin_slsqp (https://docs.scipy.org/doc/scipy-0.13.0/reference/generated/scipy.optimize.fmin_slsqp.html) to be used within the optimization module. The arguments for fmin_slsqp are passed via the constructor.
Examples
>>> from qiskit.optimization.problems import QuadraticProgram
>>> from qiskit.optimization.algorithms import SlsqpOptimizer
>>> problem = QuadraticProgram()
>>> # specify problem here
>>> x = problem.continuous_var(name="x")
>>> y = problem.continuous_var(name="y")
>>> problem.maximize(linear=[2, 0], quadratic=[[-1, 2], [0, -2]])
>>> optimizer = SlsqpOptimizer()
>>> result = optimizer.solve(problem)Initializes the SlsqpOptimizer.
This initializer takes the algorithmic parameters of SLSQP and stores them for later use of fmin_slsqp when solve() is invoked. This optimizer can be applied to find a (local) optimum for problems consisting of only continuous variables.
Parameters
-
iter (
int) – The maximum number of iterations. -
acc (
float) – Requested accuracy. -
iprint (
int) –The verbosity of fmin_slsqp :
- iprint <= 0 : Silent operation
- iprint == 1 : Print summary upon completion (default)
- iprint >= 2 : Print status of each iterate and summary
-
trials (
int) – The number of trials for multi-start method. The first trial is solved with the initial guess of zero. If more than one trial is specified then initial guesses are uniformly drawn from[lowerbound, upperbound]with potential clipping. -
clip (
float) – Clipping parameter for the initial guesses in the multi-start method. If a variable is unbounded then the lower bound and/or upper bound are replaced with the-cliporclipvalues correspondingly for the initial guesses. -
full_output (
bool) – IfFalse, return only the minimizer of func (default). Otherwise, output final objective function and summary information.
Methods
get_compatibility_msg
SlsqpOptimizer.get_compatibility_msg(problem)
Checks whether a given problem can be solved with this optimizer.
Checks whether the given problem is compatible, i.e., whether the problem contains only continuous variables, and otherwise, returns a message explaining the incompatibility.
Parameters
problem (QuadraticProgram) – The optimization problem to check compatibility.
Return type
str
Returns
Returns a string describing the incompatibility.
is_compatible
SlsqpOptimizer.is_compatible(problem)
Checks whether a given problem can be solved with the optimizer implementing this method.
Parameters
problem (QuadraticProgram) – The optimization problem to check compatibility.
Return type
bool
Returns
Returns True if the problem is compatible, False otherwise.
multi_start_solve
SlsqpOptimizer.multi_start_solve(minimize, problem)
Applies a multi start method given a local optimizer.
Parameters
- minimize (
Callable[[ndarray],Tuple[ndarray,Any]]) – A callable object that minimizes the problem specified - problem (
QuadraticProgram) – A problem to solve
Return type
OptimizationResult
Returns
The result of the multi start algorithm applied to the problem.
solve
SlsqpOptimizer.solve(problem)
Tries to solves the given problem using the optimizer.
Runs the optimizer to try to solve the optimization problem.
Parameters
problem (QuadraticProgram) – The problem to be solved.
Return type
OptimizationResult
Returns
The result of the optimizer applied to the problem.
Raises
QiskitOptimizationError – If the problem is incompatible with the optimizer.
Attributes
clip
Returns the clip value for this optimizer.
Return type
float
Returns
The clip value.
trials
Returns the number of trials for this optimizer.
Return type
int
Returns
The number of trials.