# ForwardEulerSolver

`qiskit.algorithms.time_evolvers.variational.ForwardEulerSolver(function, t0, y0, t_bound, vectorized=False, support_complex=False, num_t_steps=15)`

Bases: `OdeSolver`

Forward Euler ODE solver.

Forward Euler ODE solver that implements an interface from SciPy.

**Parameters**

**function**(*Callable*(opens in a new tab)) – Right-hand side of the system. The calling signature is`fun(t, y)`

. Here`t`

is a scalar, and there are two options for the ndarray`y`

: It can either have shape (n,); then`fun`

must return array_like with shape (n,). Alternatively it can have shape (n, k); then`fun`

must return an array_like with shape (n, k), i.e., each column corresponds to a single column in`y`

. The choice between the two options is determined by vectorized argument (see below). The vectorized implementation allows a faster approximation of the Jacobian by finite differences (required for this solver).**t0**(*float*(opens in a new tab)) – Initial time.**y0**(*Sequence*(opens in a new tab)) – Initial state.**t_bound**(*float*(opens in a new tab)) – Boundary time - the integration won’t continue beyond it. It also determines the direction of the integration.**vectorized**(*bool*(opens in a new tab)) – Whether`fun`

is implemented in a vectorized fashion. Default is False.**support_complex**(*bool*(opens in a new tab)) – Whether integration in a complex domain should be supported. Generally determined by a derived solver class capabilities. Default is False.**num_t_steps**(*int*(opens in a new tab)) – Number of time steps for the forward Euler method.

## Attributes

### TOO_SMALL_STEP

`= 'Required step size is less than spacing between numbers.'`

### step_size

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