# ScipySLSQPV1#

class zfit.minimize.ScipySLSQPV1(tol=None, gradient=None, verbosity=None, maxiter=None, criterion=None, strategy=None, name='SciPy SLSQP V1')[source]#

Bases: `ScipyBaseMinimizerV1`

Local, gradient-based minimizer using tho Sequential Least Squares Programming algorithm.name.

Sequential Least Squares Programming is an iterative method for nonlinear parameter optimization.

​This implenemtation wraps the minimizers in

Parameters:
• tol () – ​Termination value for the convergence/stopping criterion of the algorithm in order to determine if the minimum has been found. Defaults to 1e-3.​

• gradient () –

​Define the method to use for the gradient computation that the minimizer should use. This can be the gradient provided by the loss itself or method from the minimizer. In general, using the zfit provided automatic gradient is more precise and needs less computation time for the evaluation compared to a numerical method, but it may not always be possible. In this case, zfit switches to a generic, numerical gradient which in general performs worse than if the minimizer has its own numerical gradient. The following are possible choices:

If set to `False` or `'zfit'` (or `None`; default), the gradient of the loss (usually the automatic gradient) will be used; the minimizer won’t use an internal algorithm.​​`True` tells the minimizer to use its default internal gradient estimation. This can be specified more clearly using the arguments `'2-point'` and `'3-point'`, which specify the numerical algorithm the minimizer should use in order to estimate the gradient.​

• verbosity () –

​Verbosity of the minimizer. Has to be between 0 and 10. The verbosity has the meaning:

• a value of 0 means quiet and no output

• above 0 up to 5, information that is good to know but without flooding the user, corresponding to a “INFO” level.

• A value above 5 starts printing out considerably more and is used more for debugging purposes.

• Setting the verbosity to 10 will print out every evaluation of the loss function and gradient.

Some minimizers offer additional output which is also distributed as above but may duplicate certain printed values.​

• maxiter () – ​Approximate number of iterations. This corresponds to roughly the maximum number of evaluations of the `value`, ‘gradient`` or `hessian`.​

• criterion (`ConvergenceCriterion` | `None`) – ​Criterion of the minimum. This is an estimated measure for the distance to the minimum and can include the relative or absolute changes of the parameters, function value, gradients and more. If the value of the criterion is smaller than `loss.errordef * tol`, the algorithm stopps and it is assumed that the minimum has been found.​

• strategy (`ZfitStrategy` | `None`) – ​A class of type `ZfitStrategy` that takes no input arguments in the init. Determines the behavior of the minimizer in certain situations, most notably when encountering NaNs. It can also implement a callback function.​

• name (`str`) – ​Human-readable name of the minimizer.​

create_criterion(loss=None, params=None)#

Create a criterion instance for the given loss and parameters.

Parameters:
Return type:

`ConvergenceCriterion`

Returns:

ConvergenceCriterion to check if the function converged.

create_evaluator(loss=None, params=None, numpy_converter=None, strategy=None)#

Make a loss evaluator using the strategy and more from the minimizer.

Convenience factory for the loss evaluator. This wraps the loss to return a numpy array, to catch NaNs, stop on maxiter and evaluate the gradient and hessian without the need to specify the order every time.

Parameters:
Returns:

The evaluator that wraps the Loss ant Strategy with the current parameters.

Return type:

LossEval

minimize(loss, params=None, init=None)#

Fully minimize the `loss` with respect to `params`, optionally using information from `init`.

The minimizer changes the parameter values in order to minimize the loss function until the convergence criterion value is less than the tolerance. This is a stateless function that can take a `FitResult` in order to initialize the minimization.

Parameters:
• loss (`ZfitLoss` | `Callable`) – Loss to be minimized until convergence is reached. Usually a `ZfitLoss`.

• attribute (- If this is a simple callable that takes an array as argument and an attribute errordef. The) –

can be set to any arbitrary function like

```def loss(x):
return - x ** 2

loss.errordef = 0.5  # as an example
minimizer.minimize(loss, [2, 5])
```

If not TensorFlow is used inside the function, make sure to set `zfit.run.set_graph_mode(False)` and `zfit.run.set_autograd_mode(False)`.

• method (- A FitResult can be provided as the only argument to the) – parameters to be minimized are taken from it. This allows to easily chain minimization algorithms.

• the (in which case the loss as well as) – parameters to be minimized are taken from it. This allows to easily chain minimization algorithms.

• params (`Optional`[`Iterable`[`ZfitParameter`]]) –

The parameters with respect to which to minimize the `loss`. If `None`, the parameters will be taken from the `loss`.

In order to fix the parameter values to a specific value (and thereby make them indepented of their current value), a dictionary mapping a parameter to a value can be given.

If `loss` is a callable, `params` can also be (instead of `Parameters`):

• an array of initial values

• for more control, a `dict` with the keys:

• `value` (required): array-like initial values.

• `name`: list of unique names of the parameters.

• `lower`: array-like lower limits of the parameters,

• `upper`: array-like upper limits of the parameters,

• `step_size`: array-like initial step size of the parameters (approximately the expected uncertainty)

This will create internally a single parameter for each value that can be accessed in the `FitResult` via params. Repeated calls can therefore (in the current implement) cause a memory increase. The recommended way is to re-use parameters (just taken from the `FitResult` attribute `params`).

• init (`ZfitResult` | `None`) –

A result of a previous minimization that provides auxiliary information such as the starting point for the parameters, the approximation of the covariance and more. Which information is used can depend on the specific minimizer implementation.

In general, the assumption is that the loss provided is similar enough to the one provided in `init`.

What is assumed to be close:

• the parameters at the minimum of loss will be close to the parameter values at the minimum of init.

• Covariance matrix, or in general the shape, of init to the loss at its minimum.

What is explicitly _not_ assumed to be the same:

• absolute value of the loss function. If init has a function value at minimum x of fmin, it is not assumed that `loss` will have the same/similar value at x.

• parameters that are used in the minimization may differ in order or which are fixed.

Return type:

`FitResult`

Returns:

The fit result containing all information about the minimization.

Examples

Using the ability to restart a minimization with a previous result allows to use a more global search algorithm with a high tolerance and an additional local minimization to polish the found minimum.

```result_approx = minimizer_global.minimize(loss, params)
result = minimizer_local.minimize(result_approx)
```

For a simple usage with a callable only, the parameters can be given as an array of initial values.

```def func(x):
return np.log(np.sum(x ** 2))

func.errordef = 0.5
params = [1.1, 3.5, 8.35]  # initial values
result = minimizer.minimize(func, param)
```