Consider the
ordinary differential equation \frac{\mathrm{d} y}{\mathrm{d} t} = f(t,y) with initial value y(t_0) = y_0. Here the function f and the initial data t_0 and y_0 are known; the function y depends on the real variable t and is unknown. A numerical method produces a sequence y_0, y_1, y_2, \ldots such that y_k approximates y(t_0+kh) , where h is called the step size. The backward Euler method computes the approximations using y_{k+1} = y_k + h f(t_{k+1}, y_{k+1}). This differs from the (forward) Euler method in that the forward method uses f(t_k, y_k) in place of f(t_{k+1}, y_{k+1}). The backward Euler method is an implicit method: the new approximation y_{k+1} appears on both sides of the equation, and thus the method needs to solve an algebraic equation for the unknown y_{k+1} . For non-
stiff problems, this can be done with
fixed-point iteration: y_{k+1}^{[0]} = y_k, \quad y_{k+1}^{[i+1]} = y_k + h f(t_{k+1}, y_{k+1}^{[i]}). If this sequence converges (within a given tolerance), then the method takes its limit as the new approximation y_{k+1} . Alternatively, one can use (some modification of) the
Newton–Raphson method to solve the algebraic equation. For a linear differential equation, the update step can be written explicitly as y_{k+1} = \frac{y_k + h q(t_{k+1})}{1 - h p(t_{k+1})}. == Derivation ==