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- Operators in quantum mechanics: '5_operators_QM.md' - Operators in quantum mechanics: '5_operators_QM.md'
- Eigenvectors and eigenvalues: '6_eigenvectors_QM.md' - Eigenvectors and eigenvalues: '6_eigenvectors_QM.md'
- Differential equations 1: '7_differential_equations_1.md' - Differential equations 1: '7_differential_equations_1.md'
- Differential equations 2: '8_differential_equations_2.md'
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---
title: Differential Equations 2
---
# Differential equations 2
The lecture on differential equations consists of three parts, each with their own video:
- [Higher order linear differential equations](#higher-order-linear-differential-equations)
- [Partial differential equations: Separation of variables](#partial-differential-equations-separation-of-variables)
- [Self-adjoint differential operators](#self-adjoint-differential-operators)
**Total video length: hour minutes seconds**
## Higher order linear differential equations
<iframe width="100%" height=315 src="https://www.youtube-nocookie.com/embed/ucvIiLgJ2i0?rel=0" frameborder="0" allow="accelerometer; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
In the previous lecture, we focused on first order linear differential equations
as well as systems of such equations. In this lecture we switch focus to DE's
which involve higher derivatives of the function we would like to solve for. To
f`%acilitate this change we are going to change notation. In the previous lecture
we wrote differential equations for $x(t)$. In this lecture we will write DE's
of $y(x)$, where $y$ is an unknown function and $x$ is the independent variable.
For this purpose we make the following definitions,
$$y' = \frac{dy}{dx}, \ y'' = \frac{d^2 y}{dx^2}, \ \cdots, \ y^{(n)} = \frac{d^n y}{dx^n}.$$
In the new notation, a linear $n$-th order differential equation with constant
coefficients reads
$$y^{(n)} + a_{n-1} y^{(n-1)} + \cdots + a_1 y' + a_0 y = 0. $$
!!! info "Linear combination of solutions are still solutions"
Note that as was the case for first order linear DE's, the propery of
linearity once again means that if $y_{1}(x)$ and $y_{2}(x)$ are both
solutions, and $a$ and $b$ are constants,
$$a y_{1}(x) + b y_{2}(x)$$
then linear combination of the solutions is also a solution.
In order to solve a higher order linear DE we will present a trick that makes it
possible to map the problem of solving a single $n$-th order linear DE into a
related problem of solving a system of $n$ first order linear DE's.
To begin, define:
$$y_{1} = y, \ y_{2} = y', \ \cdots, \ y_{n} = y^{(n-1)}.$$
Then, the differential equation can be re-written as
$$y_1 ' = y_2$$
$$y_2 ' = y_3$$
$$ \vdots $$
$$y_{n-1} ' = y_{n}$$
$$y_{n} ' = - a_{0} y_{1} - a_{1} y_{2} - \cdots - a_{n-1} y_{n}.$$
Notice that together these $n$ equations form a linear first order system, the
first $n-1$ equations of which are trivial. Note that this trick can be used to
reduce any system of $n$-th order linear DE's to a larger system of first order
linear DE's.
Since we have discussed already the method of solution for first order linear
systems, we will outline the general solution to this system. As before, the
general solution will be the linear combination of $n$ linearly independent
solutions $f_{i}(x)$, $i \epsilon \{1, \cdots, n \}$, which make up a basis for
the solution space. That is the general solution has the form
$$y(x) = c_1 f_1 (x) + c_2 f_2 (x) + \cdots + c_n f_{n}(x). $$
To check that the $n$ solutions form a basis, it is sufficient to verify
$$ det \begin{bmatrix}
f_1(x) & \cdots & f_{n}(x) \\
f_1 ' (x) & \cdots & f_{n}'(x) \\
\vdots & \vdots & \vdots \\
f^{(n-1)}_{1} (x) & \cdots & f^{(n-1)}_{n} (x) \\
\end{bmatrix} \neq 0.$$
The determinant in the preceding line is called the *Wronski determinant*. In
particular, to determine solutions, we need to find the eigenvalues of
$$A = \begin{bmatrix}
0 & 1 & 0 & \cdots & 0 \\
0 & 0 & 1 & \cdots & 0 \\
\vdots & \vdots & \vdots & \cdots & \vdots \\
-a_0 & -a_1 & -a_2 & \cdots & -a_{n-1} \\
\end{bmatrix}.$$
It is possible to show that
$$det(A - \lambda I) = -P(\lambda),$$
in which $P(\lambda)$ is the characteristic polynomial of the system matrix $A$,
$$P(\lambda) = \lambda^n + a_{n-1} \lambda^{n-1} + \cdots + a_0.$$
As we demonstrate below, the proof relies on the co-factor expansion technique
for calculating a determinant.
$$- det(A - \lambda I) = \begin{bmatrix}
\lambda & -1 & 0 & \cdots & 0 \\
0 & \lambda & -1 & \cdots & 0 \\
\vdots & \vdots & \vdots & \cdots & \vdots \\
a_0 & a_1 & a_2 & \cdots & a_{n-1} + \lambda \\
\end{bmatrix} $$
$$- det(A - \lambda I) = \lambda det \begin{bmatrix}
\lambda & -1 & 0 & \cdots & 0 \\
0 & \lambda & -1 & \cdots & 0 \\
\vdots & \vdots & \vdots & \cdots & \vdots \\
a_1 & a_2 & a_3 & \cdots & a_{n-1} + \lambda \\
\end{bmatrix} + (-1)^{n+1}a_0 det \begin{bmatrix}
-1 & 0 & 0 & \cdots & 0 \\
\lambda & -1 & 0 & cdots & 0 \\
\vdots & \vdots & \vdots & \cdots & \vdots \\
0 & 0 & \cdots & \lambda & -1 \\
\end{bmatrix}$$
$$- det(A - \lambda I) = \lambda det \begin{bmatrix}
\lambda & -1 & 0 & \cdots & 0 \\
0 & \lambda & -1 & \cdots & 0 \\
\vdots & \vdots & \vdots & \cdots & \vdots \\
a_1 & a_2 & a_3 & \cdots & a_{n-1} + \lambda \\
\end{bmatrix} + (-1)^{n+1} a_0 (-1)^{n-1}$$
$$- det(A - \lambda I) = \lambda det \begin{bmatrix}
\lambda & -1 & 0 & \cdots & 0 \\
0 & \lambda & -1 & \cdots & 0 \\
\vdots & \vdots & \vdots & \cdots & \vdots \\
a_1 & a_2 & a_3 & \cdots & a_{n-1} + \lambda \\
\end{bmatrix} + a_0$$
$$- det(A - \lambda I) = \lambda (\lambda (\lambda \cdots + a_2) + a_1) + a_0$$
$$- det(A - \lambda I) = P(\lambda).$$
In the second last line of the proof we indicated that the method of co-factor
expansion demonstrated is repeated an additional $n-2$ times. This completes the
proof.
With the characteristic polynomial, it is possible to write the differential
equation as
$$P(\frac{d}{dx})y(x) = 0.$$
To determine solutions, we need to find $\lambda_i$ such that $P(\lambda_i) = 0$.
By the fundamental theorem of algebra, we know that $P(\lambda)$ can be written
as
$$P(\lambda) = \overset{l}{\underset{k=1}{\Sigma}} (\lambda - \lambda_k)^{m_k}.$$
In the previous equation $\lambda_k$ are the k roots of the equations, and $m_k$
is the multiplicity of each root. Note that the multiplicities satisfy
$\overset{l}{\underset{k=1}{\Sigma}} m_k = n$.
If the multiplicity of each eigenvalue is one, then solutions which form the
basis are then given as:
$$f(x) = e^{\lambda_1 x}, \ e^{\lambda_2 x}, \ \cdots, \ e^{\lambda_n x}.$$
If there are eigenvalues with multiplicity greater than one, the the solutions
which form the basis are given as
$$f(x) = e^{\lambda_1 x}, \ x e^{\lambda_1 x} , \ \cdots, \ x^{m_{1}-1} e^{\lambda_1 x}, \ etc.$$
---ADD PROOF HERE---
!!! check "Example: Second order homogeneous linear DE with constant coefficients"
Consider the equation
$$y'' + Ey = 0.$$
The characteristic polynomial of this equation is
$$P(\lambda) = \lambda^2 + E.$$
There are three cases for the possible solutions, depending upon the value
of E.
**Case 1: $E>0$**
For ease of notation, define $E=k^2$ for some constant $k$. The
characteristic polynomial can then be factored as
$$P(\lambda) = (\lambda+ i k)(\lambda - i k). $$
Following our formulation for the solution, the two basis functions for the
solution space are
$$f_1(x) = e^{i k x}, \ f_2=e^{- i k x}.$$
Alternatively, the trigonometric functions can serve as basis functions,
since they are linear combinations of $f_1$ and $f_2$ which remain linearly
independent,
$$\tilde{f_1}(x)=cos(kx), \tilde{f_2}(x)=sin{kx}.$$
**Case 2: $E<0$**
This time, define $E=-k^2$, for constant $k$. The characteristic polynomial
can then be factored as
$$P(\lambda) = (\lambda+ k)(\lambda - k).$$
The two basis functions for this solution are then
$$f_1(x)=e^{k x}, \ f_2(x) = e^{-k x}.$$
**Case 3: $E=0$**
In this case, there is a repeated eigenvalue (equal to $0$), since the
characteristic polynomial reads
$$P(\lambda) = (\lambda-0)^2.$$
Hence the basis functions for the solution space read
$$f_1(x)=e^{0 x} = 1, \ f_{2}(x) = x e^{0 x} = x. $$
## Partial differential equations: Separation of variables
<iframe width="100%" height=315 src="https://www.youtube-nocookie.com/embed/I4ghpYsFLFY?rel=0" frameborder="0" allow="accelerometer; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
### Definitions and examples
A partial differential equation (PDE) is an equation involving a function of two or
more indepenedent variables and derivatives of said function. These equations
are classified similarly to ordinary differential equations (the subject of
our earlier study). For example, they are called linear if no terms such as
$$\frac{\partial y(x,t)}{\partial x} \cdot \frac{d y(x,t)}{\partial t} \ or $$
$$\frac{\partial^2 y(x,t)}{\partial x^2} y(x,t)$$
occur. A PDE can be classified as $n$-th order accorind to the highest
derivative order of either variable occuring in the equation. For example, the
equation
$$\frac{\partial^3 f(x,y)}{\partial x^3} + \frac{\partial f(x,t)}{\partial t} = 5$$
is a $3$-rd order equation because of the third derivative with respect to x
in the equation.
To begin, we demonstrate that PDE's are of fundamental importance in physics,
especially in quantum physics. In particular, the Schrödinger equation,
which is of central importance in quantum physics is a partial differential
equation with respect to time and space. This equation is very important
because it describes the evolution in time and space of the entire description
of a quantum system $\psi(x,t)$, which is known as the wavefunction.
For a free particle in one dimension, the Schrödinger equation is
$$i \hbar \frac{\partial \psi(x,t)}{\partial t} = - \frac{\hbar^2}{2m} \frac{\partial^2 \psi(x,t)}{\partial x^2}. $$
When we studied ODEs, an initial condition was necessary in order to fully
specify a solution. Similarly, in the study of PDEs an initial condition is
required but now boundary conditions are also required. Going back to the
intuitive discussion from the lecture on ODEs, each of these conditions is
necassary in order to specify an integration constant that occurs in solving
the equation. In partial differential equations at least one such constant will
arise from the time derivative and likewise at least one from the spatial
derivative.
For the Schrödinger equation, we could supply the initial conditions
$$\psi(x,0)= \psi_{0}(x) \ & \ \psi(0,t) = \psi{t, L} = 0.$$
This particular set of boundary conditions corresponds to a particle in a box,
a situation which is used as the base model for many derivations in quantum
physics.
Another example of a partial differential equation common in physics is the
Laplace equation
$$\frac{\partial^2 \phi(x,y)}{\partial x^2}+\frac{\partial^2 \phi(x,y)}{\partial y^2}=0.$$
In quantum physics Laplace's equation is important for the study of the hydrogen
atom. In three dimensions and using spherical coordinates, the solutions to
Laplace's equation are special functions called spherical harmonics. In the
context of the hydrogen atom, these functions describe the wave function of the
system and a unique spherical harmonic function corresponds to each distinct set
of quantum numbers.
In the study of PDEs there is not a comprehensive overall treatment to the same
extent as there is for ODEs. There are several techniques which can be applied
to solving these equations, but the choice of technique must be tailored to the
equation at hand. Hence we focus on some specific examples that are common in
physics.
### Separation of variables
Let us focus on the one dimensional Schrödinger equation of a free particle
$$i \hbar \frac{\partial \psi(x,t)}{\partial t} = - \frac{\hbar^2}{2m} \frac{\partial^2 \psi(x,t)}{\partial x^2}. $$
To attempt a solution, we will make a *separation ansatz*,
$$\psi(x,t)=\phi(x) f(t).$$
!!! info "Separation ansatz"
The separation ansatz is a restrictive ansatz, not a fully general one. In
general, for such a treatment to be valid an equation and the boundary
conditions given with it have to fulfill certain properties. In this course
however you will only be asked to use this technique when it is suitable.
Substituting the separation ansatz into the PDE,
$$i \hbar \frac{\partial \phi(x)f(t)}{\partial t} = - \frac{\hbar^2}{2m} \frac{\partial^2 \phi(x)f(t)}{\partial x^2} $$
$$i \hbar \dot{f}(t) \phi(x) = - \frac{\hbar^2}{2m} \phi''(x)f(t). $$
Notice that in the above equation the derivatives on $f$ and $\phi$ can each be
written as ordinary derivatives, $\dot{f}=\frac{df(t)}{dt}$,
$\phi''(x)=\frac{d^2 \phi}{dx^2}$. This is so because each is only a function of
one variable.
Next, divide both sides of the equation through by $\psi(x,t)=\phi(x) f(t)$,
$$i \hbar \frac{\dot{f}(t)}{f(t)} = - \frac{\hbar^2}{2m} \frac{\phi''(x)}{\phi(x)} = constant := \lambda. $$
In the previous line we concluded that each part of the equation must be equal
to a constant, which we defined as $\lambda$. This follows because the left hand
side of the equation only has a dependence on the spatial coordinate $x$, whereas
the right hand side only has dependence on the time coordinate $t$. If we have
two functions $a(x)$ and $b(t)$ such that
$a(x)=b(t) \ \forall x, \ t \ \epsilon \mathbb{R}$, then $a(x)=b(t)=const$.
The constant we defined, $lambda$, is called a *separation constant*. With it, we
can break the spatial and time dependent parts of the equation into two separate
equations,
$$i \hbar \dot{f}(t) = \lambda f(t)$$
$$-\frac{\hbar^2}{2m} \phi''(x) = \lambda \phi(x) .$$
To summarize, this process has broken one partial differential equation into two
ordinary differential equations of different variables. In order to do this, we
needed to introduce a separation constant, which remains to be determined.
### Boundary and eigenvalue problems
Continuing on with the Schrödinger equation example from the previous
section, let us focus on
$$-\frac{\hbar^2}{2m} \phi''(x) = \lambda \phi(x),$$
$$\phi(0)=\phi(L)=0.$$
This has the form of an eigenvalue equation, in which $\lambda$ is the
eigenvalue, $- \frac{\hbar^2}{2m} \frac{d^2}{dx^2}[\cdot]$ is the linear
operator and $\phi(x)$ is the eigenfunction.
Notice that when stating the ordinary differential equation, it is specified
along with it's boundary conditions. Note that in contrast to an initial value
problem, a boundary value problem does not always have a solution. For example,
in the figure below, regardless of the initial slope, the curves never reach $0$
when $x=L$.
![image](figures/DE2_1.png)
For boundary value problems like this, there are only solutions for particular
eigenvalues $\lambda$. Coming back to the example, it turns out that solutions
only exist for $\lambda>0$ --this can be shown quickly, feel free to try it!
Define for simplicity $k^2:= \frac{2m \lambda}{\hbar^2}$. The equation then
reads
$$\phi''(x)+k^2 \phi(x)=0.$$
Two linearly independent solutions to this equation are
$$\phi_{1}(x)=sin(k x), \ \phi_{2}(x) = cos(k x).$$
The solution to this homogeneous equation is then
$$\phi(x)=c_1 \phi_1(x)+c_2 \phi_2(x).$$
The eigenvalue, $\lambda$ as well as one of the constant coefficients can be
determined using the boundary conditions.
$$\phi(0)=0 \ \Rightarrow \ \phi(x)=c_1 sin(k x), \ c_2=0.$$
$$\phi(L)=0 \ \Rightarrow \ 0=c_1 sin(k L) .$$
In turn, using the properties of the $sin(\cdot)$ function, it is now possible
to find the allowed values of $k$ and hence also $\lambda$. The previous
equation implies,
$$k L = n \pi, \ n \ \epsilon \ \mathbb{N}$$
$$\lambda_n = \big{(}\frac{n \pi \hbar}{L} \big{)}^2.$$
The values $\lambda_n$ are the eigenvalues. Now that we have determined
$\lambda$, it enters into the time equation, $i \hbar \dot{f}(t) = \lambda f(t)$
only as a constant. We can hence simply solve,
$$\dot{f}(t) = -i \frac{\lambda}{\hbar} f(t)$$
$$f(t) = A e^{\frac{-i \lambda t}{\hbar}}.$$
In the previous equation, the coefficient $A$ can be determined if the original
PDE was supplied with an initial condition.
Putting the solutions to the two ODEs together and redefining
$\tilde{A}=A \cdot c_1$, we arrive at the solutions for theb PDE,
$\psi_n(x,t) = \tilde{A}_n e^{-i \frac{\lambda_n t}{\hbar}} sin(\frac{n \pi x}{L}).$
Notice that there is one solution $\psi_{n}(x,t)$ for each natural number $n$.
These are still very special solutions. We will begin discussing next how to
obtain the general solution in our example.
## Self-adjoint differential operators
<iframe width="100%" height=315 src="https://www.youtube-nocookie.com/embed/p4MHW0yMMvY?rel=0" frameborder="0" allow="accelerometer; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
As we hinted was possible earlier, let us re-write the previous equation by
defining a linear operator, $L$, acting on the space of functions which satisfy
$\phi(0)=\phi(L)=0$:
$$L[\cdot]:= \frac{- \hbar^2}{2m} \frac{d^2}{dx^2}[\cdot]. $$
Then, the ODE can be writted as
$$L[\phi]=\lambda \phi.$$
This equation looks exactly like, and turns out to be, an eigenvalue equation!
!!! info "Connecting function spaces to Hilbert spaces"
Recall that a space of functions can be transformed into a Hilbert space by
equipping it with a inner product,
$$\langle f, g \rangle = \int^{L}_{0} dx f*(x) g(x) $$
Use of this inner product also has utility in demonstrating that particular
operators are *Hermitian*. The term hermitian is precisely defined below.
Of considerable interest is that hermition operators have a set of nice
properties including all real eigenvalues and orthonormal eigenfunctions.
The nicest type of operators for many practical purposes are hermitian
operators. In quantum physics for example, any physical operator must be
hermitian. Denote a hilbert space $\mathcal{H}$. An opertor
$H: \mathcal{H} \mapsto \mathcal{H}$ is said to be hermitian if it satisfies
$$\langle f, H g \rangle = \langle H f, g \rangle \ \forall \ f, \ g \ \epsilon \ \mathcal{H}.$$
Now, we would like to investigate whether the operator we have been working with,
$L$ satisfies the criterion of being hermitian over the function space
$\phi(0)=\phi(L)=0$ equipped with the above defined inner product (i.e. it is a
Hilbert space). Denote this Hilbert space $\mathcal{H}_{0}$ and consider let
$f, \ g \ \epsilon \ \mathcal{H}_0$ denote two functions from the Hilbert space.
Then, we can investigate
$$\langle f, L g \rangle = \frac{- \hbar^2}{2m} \int^{L}_{0} dx f*(x) \frac{d^2}{dx^2}g(x).$$
As a first step, it is possible to do integration by parts in the integral,
$$\langle f, L g \rangle = \frac{+ \hbar^2}{2m} ( \int^{L}_{0} dx \frac{d f*}{dx} \frac{d g}{dx} - [f*(x)\frac{d g}{dx}] \big{|}^{L}_{0} )$$
The boundary term vansishes, due to the boundary conditions $f(0)=f(L)=0$,
which directly imply $f*(0)=f*(L)=0$. Now, intergrate by parts a second time
$$\langle f, L g \rangle = \frac{- \hbar^2}{2m} (\int^{L}_{0} dx \frac{d^2 f*}{dx^2} g(x) - [\frac{d f*}{dx} g(x)] \big{|}^{L}_{0} ).$$
As before, the boundary term vanishes, due to the boundary conditions
$g(0)=g(L)=0$. Upon cancelling the boundary term however, the expression on
the right hand side, contained in the integral is simply
$\langle L f, g \rangle$. Therefore,
$$\langle f, L g \rangle=\langle L f, g \rangle. $$
We have demonstrated that $L$ is a hermitian operator on the space
$\mathcal{H}_0$. As a hermitian operator, $L$ has the property that it's
eigenfunctions form an orthonormal basis for the space $\mathcal{H}_0$. Hence it
is possible to expand any function $f \ \epsilon \ \mathcal{H}_0$ in terms of
the eigenfunctions of $L$.
!!! info "Connection to quantum states"
Recall that q quantum state $\ket{\phi}$ can be written in an orthonormal
basis $\{ \ket{u_n} \}$ as
$$\ket{\phi} = \underset{n}{\Sigma} \bra{u_n} \ket{\phi} \ket{u_n}.$$
In terms of hermitian operators and their eigenfunctions, the eigenfunctions
play the role of the orthonormal basis. In reference to our running example,
the 1D Schrödinger equation of a free particle, the eigenfunctions
$sin(\frac{n \pi x}{L})$ play the role of the basis functions $\ket{u_n}$.
To close our running example, consider the initial condition
$\psi(x,o) = \psi_{0}(x)$. Since the eigenfunctions $sin(\frac{n \pi x}{L})$
form a basis, we can now write the general solution to the problem as
$$\psi(x,t) = \overset{\infinity}{\underset{n}{\Sigma}} c_n e^{-i \frac{\lambda_n t}{\hbar}} sin(\frac{n \pi x}{L}),$$
where in the above we have defined the coefficients using the Fourier
coefficient,
$$c_n:= \int^{L}_{0} dx sin(\frac{n \pi x}{L}) \psi_{0}(x). $$
### General recipie for seperable PDEs
1. Make the separation ansatz to obtain separate ordinary differential
equations.
2. Choose which euation to treat as the eigenvalue equation. This will depend
upon the boundary conditions. Additionally, verify that the linear
differential operator $L$ in the eigenvalue equation is hermitian.
3.Solve the eigenvalue equation. Substitute the eigenvalues into the other
equations and solve those too.
4. Use the orthonormal basis functions to write down the solution corresponding
to the specified initial and boundary conditions.
One natural question is what if the operator $L$ from setp 2 is not hermitian?
It is possible to try and make it hermitian by working on a Hilbert space
equipped with a different inner product. This means one can consider
modifications to the definition of $\langle \cdot, \cdot \rangle$ such that $L$
is hermitian with respect to the modified inner product. This type of technique
falls under the umbrella of *Sturm-Liouville Theory*, which forms the foundation
for a lot of the analysis that can be done analytically on PDEs.
Another question is of course what if the equation is not separable? One
possible approach is to try working in a different coordinate system. There are
a few more analytic techniques available, however in many situations it becomes
necessary to work with numerical methods of solution.
### Problems
1. [:grinning:] Which of the following equations for $y(x)$ is linear?
(a) y''' - y'' + x cos(x) y' + y - 1 = 0
(b) y''' + 4 x y' - cos(x) y = 0
(c) y'' + y y' = 0
(d) y'' + e^x y' - x y = 0
2. [:grinning:] Find the general solution to the equation
$$y'' - 4 y' + 4 y = 0. $$
Show explicitly by computing the Wronski determinant that the
basis for the solution space is actually linearly independent.
3. [:grinning:] Find the general solution to the equation
$$y''' - y'' + y' - y = 0.$$
Then find the solution to the initial conditions $y''(0) =0$, $y'(0)=1$, $y(0)=0$.
4. [:smirk:] Take the Laplace equation in 2D:
$$\frac{\partial^2 \phi(x,y)}{\partial x^2} + \frac{\partial^2 \phi(x,y)}{\partial y^2} = 0.$$
(a) Make a separation ansatz $\phi(x,y) = f(x)g(y)$ and write
down the resulting ordinary differential equations.
(b) Now assume that the boundary conditions $\phi(0,y) = \phi(L,y) =0$ for
all y, i.e. f(0)=f(L)=0. Find all solutions $f(x)$ and the
corresponding eigenvalues.
(c) Finally, for each eigenvalue, find the general solution $g(y)$ for
this eigenvalue. Combine this with all solutions $f(x)$ to write down
the general solution (we know from the lecture that the operator
$\frac{d^2}{dx^2}$ is Hermitian - you can thus directly assume that
the solutions form an orthogonal basis).
5. [:smirk:] Take the partial differential equation
$$\frac{\partial h(x,y)}{\partial x} + x \frac{\partial h(x,y)}{\partial y} = 0. $$
Try to make a separation ansatz $h(x,y)=f(x)g(y)$. What do you observe?
6. [:sweat:] We consider the Hilbert space of functions $f(x)$ defined
for $x \ \epsilon \ [0,L]$ with $f(0)=f(L)=0$.
Which of the following operators on this space is hermitian?
(a) Lf = A(x) \frac{d^2 f}{dx^2}
(b) Lf = \frac{d}{dx} \big{()} A(x) \frac{df}{dx} \big{)}
src/figures/DE2_1.png

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