# ProblemΒΆ

There exist a couple of different ways for defining an optimization problem in *pymoo*. In contrast to other optimization frameworks in Python, the preferred way is to define an **object**. However, a problem can also be defined by functions as shown here. Most algorithms in *pymoo* are population-based, which implies in each generation, not a single but multiple solutions are evaluated. Thus, the problem implementation retrieves the set of solutions to provide the
most flexibility to the end-user. This flexibility allows you to implement a custom parallelization and thus to use your hardware most efficiently. Three different ways of defining a problem are shown below:

Overview

Problem: Object-oriented definition

`Problem`

which implements a method evaluating a**set**of solutions.ElementwiseProblem: Object-oriented definition

`ElementwiseProblem`

which implements a function evaluating a**single**solution at a time.FunctionalProblem: Define a problem

`FunctionalProblem`

by using a**function**for each objective and constraint.

Next, we define an **unconstrained** optimization problem with two variables and two objectives. Because the lower and upper bounds are identical for both variables, only a float value is passed to the `Problem`

constructor. Assuming the `Algorithm`

has a population size N, the input variable `x`

is a two-dimensional matrix with the dimensions (N,2). The input has two columns because the optimization problem has `n_var=2`

. Thus, to evaluated the problem makes use of the vectorized
calculations `[:, 0]`

and `[:, 1]`

to select the first and second variables for each row in the input matrix `x`

.

```
[1]:
```

```
import numpy as np
from pymoo.core.problem import Problem
class MyProblem(Problem):
def __init__(self):
super().__init__(n_var=2,
n_obj=2,
xl=-2.0,
xu=2.0)
def _evaluate(self, x, out, *args, **kwargs):
f1 = 100 * (x[:, 0]**2 + x[:, 1]**2)
f2 = (x[:, 0]-1)**2 + x[:, 1]**2
out["F"] = np.column_stack([f1, f2])
```

Below we define a **constrained** optimization problem with two variables and two objectives. Here, the problem is defined **element-wise**. The lower and upper bounds, `xl`

and `xu`

, are defined using a vector with a length equal to the number of variables. The input `x`

is a **one-dimensional** array of length two and is called N times in each iteration for the algorithm discussed above.

```
[2]:
```

```
import numpy as np
from pymoo.core.problem import ElementwiseProblem
class MyProblem(ElementwiseProblem):
def __init__(self):
super().__init__(n_var=2,
n_obj=2,
n_ieq_constr=2,
xl=np.array([-2,-2]),
xu=np.array([2,2]))
def _evaluate(self, x, out, *args, **kwargs):
f1 = 100 * (x[0]**2 + x[1]**2)
f2 = (x[0]-1)**2 + x[1]**2
g1 = 2*(x[0]-0.1) * (x[0]-0.9) / 0.18
g2 = - 20*(x[0]-0.4) * (x[0]-0.6) / 4.8
out["F"] = [f1, f2]
out["G"] = [g1, g2]
problem = MyProblem()
```