If you choose the $L^2$ norm, computing
$$\Phi(a,b)=\int_a^b \Big[\alpha+\beta\, x -e^x\Big]^2\,dx$$ and minimizing, we have
$$\beta(a,b) =\frac{6}{(b-a)^3}\Big[(b-a +2)\,e^a+(b-a-2)\,e^b\Big]$$
$$\alpha(a,b)=\frac{e^b-e^a}{b-a}-\frac{1}{2} (a+b) \, \beta $$
$$2(a-b)^3\, \Phi_{\text{min}}(a,b)=
\left(-e^a (a-b-2)-e^b (a-b+2)\right)\times $$ $$ \left(e^a \left(a^2-2 a (b+3)+b
(b+6)+12\right)-e^b \left(a^2-2 (a+3) b+6 a+b^2+12\right)\right)$$
So, for the next point $c$, if we want to keep the same $\Phi_{\text{min}}=k$, we need to find it such that
$\Phi_{\text{min}}(b,c)=k$. This does not make any problem using Newton method
$$\Phi'_{\text{min}}(b,c)=\frac{\left(e^c \left(b^2-2 (b+2) c+4 b+c^2+6\right)+2 e^b (b-c-3)\right)^2}{(b-c)^4}$$
For the first $c$, we can use $c_0=b+\theta(b-a)$, the first $\theta$ being empirically set equal to $0.5$ ad the next would be updated readjusting $\theta$ from the previous iteration
Suppose that we start with $a=1$ and $b=3$; this gives $k=e^2 \left(e^2-7\right)$ and the iterates are
$$\left(
\begin{array}{cc}
n & c_n \\
0 & 4.000 \\
1 & 4.133 \\
2 & 4.104 \\
3 & 4.101
\end{array}
\right)$$
This allows a recursive definitions of the next points to be used.