# Trying it out¶

This is a basic guide to using the optimizers mainly intended to test whether your installation works. If you are already familiar to using optimizers within a quantum programming framework, you may be better served using the interoperability interfaces, such as the ones to Qiskit and SciPy.

First, you need to have some objective function to optimize. All the optimizers are minimizers and expect to do simple “less than” comparisons on the result. Thus, if instead you need to maximize the result, simply add a minus sign. The objective function is expected to accept an evaluation point in the form of a `numpy` array of floating point values, or a list of such evaluation points to allow evaluation in parallel.

Example of an objective function:

```import numpy as np

# some interesting objective function to minimize
def objective_function(x):
fv = np.inner(x, x)
fv *= 1 + 0.1*np.sin(10*(x+x))
return np.random.normal(fv, 0.01)
```

All optimizers provided require bounds. This is not true for optimizers in general, but is of such great benefit when dealing with noisy objective functions that it is pretty much a requirement. In most cases, the better the bounds, the faster the optimizer will run and the higher the quality of the result. For difficult problems, it may be necessary to refine bounds while switching optimizers to solve. Not all optimizers are equally sensitive to bounds.

```# create a numpy array of bounds, one (low, high) for each parameter
bounds = np.array([[-np.pi, np.pi], [-np.pi, np.pi]], dtype=float)
```

Likewise, consider whether a good initial estimate can be provided, and if yes, it is often worthwhile to spend some (classical) computational resources to obtain a high quality initial estimate. Not every optimizer benefits equally of a good initial estimate, but most do, especially when combined with tight bounds. If no initial estimate is provided, a random point is used within the given bounds.

```# initial values for all parameters
x0 = np.array([0.5, 0.5])
```

The objective function is considered expensive to calculate (running a circuit many times on the QPU). It is therefore important to consider a budget (number of allowed evaluations), rather than to rely solely on convergence criteria, especially since tight tolerances can not alway be met in the case of large noise. The budget is always an upper limit: if convergence happens earlier, the minimizer will stop.

```# budget (number of calls, assuming 1 count per call)
budget = 100
```

Finally, import and run the minimizer. The result object will contain the optimal parameters (`result.optpar`) and optimal value (`result.optval`). The history object contains the full call history.

```from skquant.opt import minimize

# method can be ImFil, SnobFit, NOMAD, Orbit, or Bobyqa (case insensitive)
result, history = \
minimize(objective_function, x0, bounds, budget, method='imfil')
```