Here's an attempt at a distributional treatment. The ideas were easier than the write-up seems to indicate. Informal reasoning usuall works well, but sometimes it leads ones astray. See
https://www.physicsforums.com/showthread.php?p=998502". Here's Roger Penrose on the question of mathematical rigour in quantum theory: "Quantum mechanics is full of irritating issues of this kind. As the state of the art stands, one either be decidedly sloppy about such mathematical niceties and even pretend that position states and momentum sates are actually states, or else spend the whole time insisting on getting the mathematics right, in which case there is a contrasting danger of getting trapped in 'rigour mortis'. ... I am not at all sure what the correct answer is for making progress in the subject!''
A distribution is a continuous linear mapping (functional) from \mathcal{T} to \mathbb{C}, where \mathcal{T} is the space of test (i.e., sufficiently nice) functions. Any locally integrable function g naturally defines a distribution G:
G\left[ f\right] =\int_{-\infty }^{\infty }g\left( x\right) f\left( x\right) dx
for all test functions f. Not all distributions arise in such a fashion.
Even though the Dirac delta function, defined by \delta \left[ f\right] =f\left( 0\right), is an example of a distribution that doesn't arise in the above manner, it is convenient (and useful!) notationally to pretend that it does, i.e., that \delta is a function such that
f\left( 0\right) =\int_{-\infty }^{\infty }\delta \left( x\right) f\left( x\right) dx.
If g is any function such that
\int_{-\infty }^{\infty }g\left( x\right) dx=1,
then the family of functions g_{\varepsilon }\left( x\right) :=g\left( x/\varepsilon \right) /\varepsilon defines a family of distributions (as above) G_{\varepsilon }, with G_{\varepsilon }\rightarrow \delta (in the distributional or weak sense) as \varepsilon \rightarrow 0, i.e.,
f\left( 0\right) =\lim_{\varepsilon \rightarrow 0}\int_{-\infty }^{\infty }g_{\varepsilon }\left( x\right) f\left( x\right) dx.
The definitions of differentiation of distributions and multiplication of distributions by somewhat nice functions are both motivated by functions considered as distributions. Let g be a function that has locally integrable derivative g^{\prime }, and use g^{\prime } to define the distribution G^{\prime } in the usual way:
<br />
\begin{equation*}<br />
\begin{split}<br />
G^{\prime }\left[ f\right] &= \int_{-\infty }^{\infty }g^{\prime }\left( x\right) f\left( x\right) dx \\<br />
&= \left[ g\left( x\right) f\left( x\right) \right] _{-\infty }^{\infty }-\int_{-\infty }^{\infty }g\left( x\right) f^{\prime }\left( x\right) dx.<br />
\end{split}<br />
\end{equation*}<br />
Test functions die at \pm \infty, so the first term in the last line is zero, giving G^{\prime }\left[ f\right] =-G\left[ f^{\prime }\right]. This definition works for all distributions (including the Dirac delta function), not just those that correspond to functions.
Suppose v\left( x\right) =u\left( x\right) g\left( x\right). Then,
V\left[ f\right] =\int_{-\infty }^{\infty }v\left( x\right) f\left( x\right) dx=\int_{-\infty }^{\infty }g\left( x\right) \left[ u\left( x\right) f\left( x\right) \right] dx=G\left[ uf\right].
This motivates the definition of a distribution \left( uG\right) \left[ f\right] :=G\left[ uf\right] for G an arbitrary distribution and u a function.
Now to the problem at hand. Consider the standard textbook form of the Schrodinger equation with delta function potential,
-\frac{\hbar ^{2}}{2m}\frac{d^{2}\psi }{dx^{2}}-\alpha \delta \left( x\right) \psi =E\psi .
The second term on the left is a distribution, so all terms in the equation need to be distributions, i.e., try to find a function \psi that has corresponding distribution \Psi (not to be confused with the time-dependent wavefunction), and that satisfies the distributional Schrodinger equation
-\frac{\hbar ^{2}}{2m}\Psi ^{\prime \prime }\left[ f\right] -\left( \alpha \psi \delta \right) \left[ f\right] =E\Psi \left[ f\right]
for all test functions f.
The term on the right is a distribution that corresponds to a function, and therefore, if the first term also corresponds to a function, then (by rearrangement and linearity of distributions), then so does the delta distribution. Since the delta distribution doesn't correspond to a function, the first term on the left can't be a distribution that corresponds to a function. In other words, \psi is twice differentiable as a distribution, but not as a function. Clearly, the only place is a problem is at x=0, so provisionally, assume \psi is continuous everywhere and piecewise smooth on both sides of zero.
The two different notions of differentiation give two options: 1) turn \psi into the distribution \Psi and use distributional differentiation (defined above) to produce the distributions \Psi ^{\prime } and \Psi ^{\prime \prime }; 2) apply piecewise differentiation of functions to \psi (except at x=0) to produce functions \phi ^{\prime } and \phi ^{\prime \prime }, and use these function to define distributions \Phi ^{\prime } and \Phi ^{\prime \prime }. In order to distinguish between these two options, I've introduced somewhat awkward notation.
Interesting question: Does \Psi ^{\prime }=\Phi ^{\prime } and \Psi ^{\prime \prime }=\Phi ^{\prime \prime } ?
<br />
\begin{equation*}<br />
\begin{split}<br />
\Psi ^{\prime }\left[ f\right] &= -\Psi \left[ f^{\prime }\right] \\<br />
&= -\int_{-\infty }^{\infty }\psi \left( x\right) f^{\prime }\left( x\right) dx\\<br />
&= -\left( \int_{-\infty }^{0^{-}}\psi \left( x\right) f^{\prime }\left( x\right) dx+\int_{0^{+}}^{\infty }\psi \left( x\right) f^{\prime }\left( x\right) dx\right)\\<br />
&= -\left( \left[ \psi \left( x\right) f\left( x\right) \right] _{-\infty }^{0^{-}}-\int_{-\infty }^{0^{-}}\psi ^{\prime }\left( x\right) f\left( x\right) dx+\left[ \psi \left( x\right) f\left( x\right) \right] _{0^{+}}^{\infty }-\int_{0^{+}}^{\infty }\psi ^{\prime }\left( x\right) f\left( x\right) dx\right)\\<br />
&= -\psi \left( 0^{-}\right) f\left( 0^{-}\right) +\psi \left( 0^{+}\right) f\left( 0^{+}\right) +\int_{-\infty }^{0^{-}}\phi ^{\prime }\left( x\right) f\left( x\right) dx+\int_{0^{+}}^{\infty }\phi ^{\prime }\left( x\right) f\left( x\right) dx<br />
\end{split}<br />
\end{equation*}<br />
Since both \psi and f are continuous,
<br />
-\psi \left( 0^{-}\right) f\left( 0^{-}\right) +\psi \left( 0^{+}\right) f\left( 0^{+}\right) =0,<br />
and hence \Psi ^{\prime }\left[ f\right] =\Phi ^{\prime }\left[ f\right].
<br />
\begin{equation*}<br />
\begin{split}<br />
\Psi ^{\prime \prime }\left[ f\right] &= -\Psi ^{\prime }\left[ f^{\prime }\right]\\<br />
&= -\Phi ^{\prime }\left[ f^{\prime }\right]\\<br />
&= -\left( \int_{-\infty }^{0^{-}}\phi ^{\prime }\left( x\right) f^{\prime }\left( x\right) dx+\int_{0^{+}}^{\infty }\phi ^{\prime }\left( x\right) f^{\prime }\left( x\right) dx\right)\\<br />
&= -\psi ^{\prime }\left( 0^{-}\right) f\left( 0^{-}\right) +\psi ^{\prime }\left( 0^{+}\right) f\left( 0^{+}\right) +\int_{-\infty }^{0^{-}}\phi ^{\prime \prime }\left( x\right) f\left( x\right) dx+\int_{0^{+}}^{\infty }\phi ^{\prime \prime }\left( x\right) f\left( x\right) dx\\<br />
&= \left( \psi ^{\prime }\left( 0^{+}\right) -\psi ^{\prime }\left( 0^{-}\right) \right) f\left( 0\right) +\int_{-\infty }^{\infty }\phi ^{\prime \prime }\left( x\right) f\left( x\right) dx<br />
\end{split}<br />
\end{equation*}<br />
Consequently,
<br />
\Psi ^{\prime \prime }\left[ f\right] =\left( \psi ^{\prime }\left( 0^{+}\right) -\psi ^{\prime }\left( 0^{-}\right) \right) \delta \left[ f\right] +\Phi ^{\prime \prime }\left[ f\right]<br />
Use this in the distributional Schrodinger equation:
<br />
\begin{equation*}<br />
\begin{split}<br />
-\frac{\hbar ^{2}}{2m}\left( \left( \psi ^{\prime }\left( 0^{+}\right) -\psi ^{\prime }\left( 0^{-}\right) \right) \delta \left[ f\right] +\Phi ^{\prime \prime }\left[ f\right] \right) -\left( \alpha \psi \delta \right) \left[ f\right] &= E\Psi \left[ f\right]\\<br />
-\left( \frac{\hbar^{2}}{2m}\left( \psi ^{\prime }\left( 0^{+}\right) -\psi ^{\prime }\left( 0^{-}\right) \right) +\alpha \psi \left( 0\right) \right) \delta \left[ f\right] &= \left( E\Psi +\frac{\hbar ^{2}}{2m}\Phi ^{\prime \prime }\right) \left[ f\right]<br />
\end{split}<br />
\end{equation*}<br />
The distribution on the left side of this equation is a distribution that can't be produced by a function, while the distribution on the right is produced by a function. This is only possible if both sides equal zero. Hence, the left side gives the jump discontinuity in \psi ^{\prime }, while the right gives the standard differential equation satisfied by \psi (since f is an arbitrary test function) for x\neq 0.