A Second Law for Open Markov Processes

15 November, 2014

guest post by Blake Pollard

What comes to mind when you hear the term ‘random process’? Do you think of Brownian motion? Do you think of particles hopping around? Do you think of a drunkard staggering home?

Today I’m going to tell you about a version of the drunkard’s walk with a few modifications. Firstly, we don’t have just one drunkard: we can have any positive real number of drunkards. Secondly, our drunkards have no memory; where they go next doesn’t depend on where they’ve been. Thirdly, there are special places, such as entrances to bars, where drunkards magically appear and disappear.

The second condition says that our drunkards satisfy the Markov property, making their random walk into a Markov process. The third condition is really what I want to tell you about, because it makes our Markov process into a more general ‘open Markov process’.

There are a collection of places the drunkards can be, for example:

V= \{ \text{bar},\text{sidewalk}, \text{street}, \text{taco truck}, \text{home} \}

We call this set V the set of states. There are certain probabilities associated with traveling between these places. We call these transition rates. For example it is more likely for a drunkard to go from the bar to the taco truck than to go from the bar to home so the transition rate between the bar and the taco truck should be greater than the transition rate from the bar to home. Sometimes you can’t get from one place to another without passing through intermediate places. In reality the drunkard can’t go directly from the bar to the taco truck: he or she has to go from the bar to sidewalk to the taco truck.

This information can all be summarized by drawing a directed graph where the positive numbers labelling the edges are the transition rates:

For simplicity we draw only three states: home, bar, taco truck. Drunkards go from home to the bar and back, but they never go straight from home to the taco truck.

We can keep track of where all of our drunkards are using a vector with 3 entries:

\displaystyle{ p(t) = \left( \begin{array}{c} p_h(t) \\ p_b(t) \\ p_{tt}(t) \end{array} \right) \in \mathbb{R}^3 }

We call this our population distribution. The first entry p_h is the number of drunkards that are at home, the second p_b is how many are at the bar, and the third p_{tt} is how many are at the taco truck.

There is a set of coupled, linear, first-order differential equations we can write down using the information in our graph that tells us how the number of drunkards in each place change with time. This is called the master equation:

\displaystyle{ \frac{d p}{d t} = H p }

where H is a 3×3 matrix which we call the Hamiltonian. The off-diagonal entries are nonnegative:

H_{ij} \geq 0, i \neq j

and the columns sum to zero:

\sum_i H_{ij}=0

We call a matrix satisfying these conditions infinitesimal stochastic. Stochastic matrices have columns that sum to one. If we take the exponential of an infinitesimal stochastic matrix we get one whose columns sum to one, hence the label ‘infinitesimal’.

The Hamiltonian for the graph above is

H = \left( \begin{array}{ccc} -2 & 5 & 10 \\ 2 & -12 & 0 \\ 0 & 7 & -10 \end{array} \right)

John has written a lot about Markov processes and infinitesimal stochastic Hamiltonians in previous posts.

Given two vectors p,q \in \mathbb{R}^3 describing the populations of drunkards which obey the same master equation, we can calculate the relative entropy of p relative to q:

\displaystyle{ S(p,q) = \sum_{ i \in V} p_i \ln \left( \frac{p_i}{q_i} \right) }

This is an example of a ‘divergence’. In statistics, a divergence a way of measuring the distance between probability distributions, which may not be symmetrical and may even not obey the triangle inequality.

The relative entropy is important because it decreases monotonically with time, making it a Lyapunov function for Markov processes. Indeed, it is a well known fact that

\displaystyle{ \frac{dS(p(t),q(t) ) } {dt} \leq 0 }

This is true for any two population distributions which evolve according to the same master equation, though you have to allow infinity as a possible value for the relative entropy and negative infinity for its time derivative.

Why is entropy decreasing? Doesn’t the Second Law of Thermodynamics say entropy increases?

Don’t worry: the reason is that I have not put a minus sign in my definition of relative entropy. Put one in if you like, and then it will increase. Sometimes without the minus sign it’s called the Kullback–Leibler divergence. This decreases with the passage of time, saying that any two population distributions p(t) and q(t) get ‘closer together’ as they get randomized with the passage of time.

That itself is a nice result, but I want to tell you what happens when you allow drunkards to appear and disappear at certain states. Drunkards appear at the bar once they’ve had enough to drink and once they are home for long enough they can disappear. The set of places where drunkards can appear or disappear B is called the set of boundary states.  So for the above process

B = \{ \text{home},\text{bar} \}

is the set of boundary states. This changes the way in which the population of drunkards changes with time!

The drunkards at the taco truck obey the master equation. For them,

\displaystyle{ \frac{dp_{tt}}{dt} = 7p_b -10 p_{tt} }

still holds. But because the populations can appear or disappear at the boundary states the master equation no longer holds at those states! Instead it is useful to define the flow of drunkards into the i^{th} state by

\displaystyle{ \frac{Dp_i}{Dt} = \frac{dp_i}{dt}-\sum_j H_{ij} p_j}

This quantity describes by how much the rate of change of the populations at the boundary states differ from that given by the master equation.

The reason why we are interested in open Markov processes is because you can take two open Markov processes and glue them together along some subset of their boundary states to get a new open Markov process! This allows us to build up or break down complicated Markov processes using open Markov processes as the building blocks.

For example we can draw the graph corresponding to the drunkards’ walk again, only now we will distinguish boundary states from internal states by coloring internal states blue and having boundary states be white:

Consider another open Markov process with states

V=\{ \text{home},\text{work},\text{bar} \}

where

B=\{ \text{home}, \text{bar}\}

are the boundary states, leaving

I=\{\text{work}\}

as an internal state:

Since the boundary states of this process overlap with the boundary states of the first process we can compose the two to form a new Markov process:

Notice the boundary states are now internal states. I hope any Markov process that could approximately model your behavior has more interesting nodes! There is a nice way to figure out the Hamiltonian of the composite from the Hamiltonians of the pieces, but we will leave that for another time.

We can ask ourselves, how does relative entropy change with time in open Markov processes? You can read my paper for the details, but here is the punchline:

\displaystyle{ \frac{dS(p(t),q(t) ) }{dt} \leq \sum_{i \in B} \frac{Dp_i}{Dt}\frac{\partial S}{\partial p_i} + \frac{Dq_i}{Dt}\frac{\partial S}{\partial q_i} }

This is a version of the Second Law of Thermodynamics for open Markov processes.

It is important to notice that the sum is only over the boundary states! This inequality tells us that relative entropy still decreases inside our process, but depending on the flow of populations through the boundary states the relative entropy of the whole process could either increase or decrease! This inequality will be important when we study how the relative entropy changes in different parts of a bigger more complicated process.

That is all for now, but I leave it as an exercise for you to imagine a Markov process that describes your life. How many states does it have? What are the relative transition rates? Are there states you would like to spend more or less time in? Are there states somewhere you would like to visit?

Here is my paper, which proves the above inequality:

• Blake Pollard, A Second Law for open Markov processes.

If you have comments or corrections, let me know!


Network Theory Seminar (Part 4)

5 November, 2014

 

Since I was in Banff, my student Franciscus Rebro took over this week and explained more about cospan categories. These are a tool for constructing categories where the morphisms are networks such as electrical circuit diagrams, signal flow diagrams, Markov processes and the like. For some more details see:

• John Baez and Brendan Fong, A compositional framework for passive linear networks.

Cospan categories are really best thought of as bicategories, and Franciscus gets into this aspect too.


Network Theory (Part 33)

4 November, 2014

Last time I came close to describing the ‘black box functor’, which takes an electrical circuit made of resistors

and sends it to its behavior as viewed from outside. From outside, all you can see is the relation between currents and potentials at the ‘terminals’—the little bits of wire that poke out of the black box:

I came close to defining the black box functor, but I didn’t quite make it! This time let’s finish the job.

The categories in question

The black box functor

\blacksquare : \mathrm{ResCirc} \to \mathrm{LinRel}

goes from the category \mathrm{ResCirc}, where morphisms are circuits made of resistors, to the category \mathrm{LinRel}, where morphisms are linear relations. Let me remind you how these categories work, and introduce a bit of new notation.

Here is the category \mathrm{ResCirc}:

• an object is a finite set;

• a morphism from X to Y is an isomorphism class of cospans

in the category of graphs with edges labelled by resistances: numbers in (0,\infty). Here we think of the finite sets X and Y as graphs with no edges. We call X the set of inputs and Y the set of outputs.

• we compose morphisms in \mathrm{ResCirc} by composing isomorphism classes of cospans.

And here is the category \mathrm{LinRel}:

• an object is a finite-dimensional real vector space;

• a morphism from U to V is a linear relation R : U \leadsto V, meaning a linear subspace R \subseteq U \times V;

• we compose a linear relation R \subseteq U \times V and a linear relation S \subseteq V \times W in the usual way we compose relations, getting:

SR = \{(u,w) \in U \times W : \; \exists v \in V \; (u,v) \in R \mathrm{\; and \;} (v,w) \in S \}

In case you’re wondering: I’ve just introduced the wiggly arrow notation

R : U \leadsto V

for a linear relation from U to V, because it suggests that a relation is a bit like a function but more general. Indeed, a function is a special case of a relation, and composing functions is a special case of composing relations.

The black box functor

Now, how do we define the black box functor?

Defining it on objects is easy. An object of \mathrm{ResCirc} is a finite set S, and we define

\blacksquare{S} = \mathbb{R}^S \times \mathbb{R}^S

The idea is that S could be a set of inputs or outputs, and then

(\phi, I) \in \mathbb{R}^S \times \mathbb{R}^S

is a list of numbers: the potentials and currents at those inputs or outputs.

So, the interesting part is defining the black box functor on morphisms!

For this we start with a morphism in \mathrm{ResCirc}:

The labelled graph \Gamma consists of:

• a set N of nodes,

• a set E of edges,

• maps s, t : E \to N sending each edge to its source and target,

• a map r : E \to (0,\infty) sending each edge to its resistance.

The cospan gives maps

i: X \to N, \qquad o: Y \to N

These say how the inputs and outputs are interpreted as nodes in the circuit. We’ll call the nodes that come from inputs or outputs ‘terminals’. So, mathematically,

T = \mathrm{im}(i) \cup \mathrm{im}(o) \subseteq N

is the set of terminals: the union of the images of i and o.

In the simplest case, the maps i and o are one-to-one, with disjoint ranges. Then each terminal either comes from a single input, or a single output, but not both! This is a good picture to keep in mind. But some subtleties arise when we leave this simplest case and consider other cases.

Now, the black box functor is supposed to send our circuit to a linear relation. I’ll call the circuit \Gamma for short, though it’s really the whole cospan

So, our black box functor is supposed to send this circuit to a linear relation

\blacksquare(\Gamma) : \mathbb{R}^X \times \mathbb{R}^X \leadsto \mathbb{R}^Y \times \mathbb{R}^Y

This is a relation between the potentials and currents at the input terminals and the potentials and currents at the output terminals! How is it defined?

I’ll start by outlining how this works.

First, our circuit picks out a subspace

dQ \subseteq \mathbb{R}^T \times \mathbb{R}^T

This is the subspace of allowed potentials and currents on the terminals. I’ll explain this and why it’s called dQ a bit later. Briefly, it comes from the principle of minimum power, described last time.

Then, the map

i: X \to T

gives a linear relation

S(i) : \mathbb{R}^X \times \mathbb{R}^X \leadsto \mathbb{R}^T \times \mathbb{R}^T

This says how the potentials and currents at the inputs are related to those at the terminals. Similarly, the map

o: Y \to T

gives a linear relation

S(o) : \mathbb{R}^Y \times \mathbb{R}^Y \leadsto \mathbb{R}^T \times \mathbb{R}^T

This says how the potentials and currents at the outputs are related to those at the terminals.

Next, we can ‘turn around’ any linear relation

R : \mathbb{R}^Y \times \mathbb{R}^Y \leadsto \mathbb{R}^T \times \mathbb{R}^T

to get a relation

R^\dagger : \mathbb{R}^T \times \mathbb{R}^T  \leadsto \mathbb{R}^Y \times \mathbb{R}^Y

defined by

R^\dagger = \{(\phi',-I',\phi,-I) : (\phi, I, \phi', I') \in R \}

Here we are just switching the input and output potentials, but when we switch the currents we also throw in a minus sign. The reason is that we care about the current flowing in to an input, but out of an output.

Finally, one more trick: given a linear subspace

L \subseteq V

of a vector space V we get a linear relation

1|_L : V \leadsto V

called the identity restricted to L, defined like this:

1|_L = \{ (v, v) :\; v \in L \} \subseteq V \times V

If L is all of V this relation is actually the identity function on V. Otherwise it’s a partially defined function that’s defined only on L, and is the identity there. (A partially defined function is an example of a relation.) My notation 1|_L is probably bad, but I don’t know a better one, so bear with me.

Let’s use all these ideas to define

\blacksquare(\Gamma) : \mathbb{R}^X \times \mathbb{R}^X \leadsto \mathbb{R}^Y \times \mathbb{R}^Y

To do this, we compose three linear relations:

1) We start with

S(i) : \mathbb{R}^X \times \mathbb{R}^X \leadsto \mathbb{R}^T \times \mathbb{R}^T

2) We compose this with

1|_{dQ} : \mathbb{R}^T \times \mathbb{R}^T \leadsto \mathbb{R}^T \times \mathbb{R}^T

3) Then we compose this with

S(o)^\dagger : \mathbb{R}^T \times \mathbb{R}^T \leadsto \mathbb{R}^Y \times \mathbb{R}^Y

Note that:

1) says how the potentials and currents at the inputs are related to those at the terminals,

2) picks out which potentials and currents at the terminals are actually allowed, and

3) says how the potentials and currents at the terminals are related to those at the outputs.

So, I hope all makes sense, at least in some rough way. In brief, here’s the formula:

\blacksquare(\Gamma) = S(o)^\dagger \; 1|_{dQ} \; S(i)

Now I just need to fill in some details. First, how do we define S(i) and S(o)? They work exactly the same way, by ‘copying potentials and adding currents’, so I’ll just talk about one. Second, how do we define the subspace dQ? This uses the principle of minimum power.

Duplicating potentials and adding currents

Any function between finite sets

i: X \to T

gives a linear map

i^* : \mathbb{R}^T \to \mathbb{R}^X

Mathematicians call this linear map the pullback along i, and for any \phi \in \mathbb{R}^T it’s defined by

i^*(\phi)(x) = \phi(i(x))

In our application, we think of \phi as a list of potentials at terminals. The function i could map a bunch of inputs to the same terminal, and the above formula says the potential at this terminal gives the potential at all those inputs. So, we are copying potentials.

We also get a linear map going the other way:

i_* : \mathbb{R}^X \to \mathbb{R}^T

Mathematicians call this the pushforward along i, and for any I \in \mathbb{R}^X it’s defined by

\displaystyle{ i_*(I)(t) = \sum_{x \; : \; i(x) = t } I(x) }

In our application, we think of I as a list of currents entering at some inputs. The function i could map a bunch of inputs to the same terminal, and the above formula says the current at this terminal is the sum of the currents at all those inputs. So, we are adding currents.

Putting these together, our map

i : X \to T

gives a linear relation

S(i) : \mathbb{R}^X \times \mathbb{R}^X \leadsto \mathbb{R}^T \times \mathbb{R}^T

where the pair (\phi, I) \in \mathbb{R}^X \times \mathbb{R}^X is related to the pair (\phi', I') \in \mathbb{R}^T \times \mathbb{R}^T iff

\phi = i^*(\phi')

and

I' = i_*(I)

So, here’s the rule of thumb when attaching the points of X to the input terminals of our circuit: copy potentials, but add up currents. More formally:

\begin{array}{ccl} S(i) &=& \{ (\phi, I, \phi', I') : \; \phi = i^*(\phi') , \; I' = i_*(I) \}  \\ \\  &\subseteq& \mathbb{R}^X \times \mathbb{R}^X \times \mathbb{R}^T \times \mathbb{R}^T \end{array}

The principle of minimum power

Finally, how does our circuit define a subspace

dQ \subseteq \mathbb{R}^T \times \mathbb{R}^T

of allowed potential-current pairs at the terminals? The trick is to use the ideas we discussed last time. If we know the potential at all nodes of our circuit, say \phi \in \mathbb{R}^N, we know the power used by the circuit:

P(\phi) = \displaystyle{ \sum_{e \in E} \frac{1}{r_e} \big(\phi(s(e)) - \phi(t(e))\big)^2 }

We saw last time that if we fix the potentials at the terminals, the circuit will choose potentials at the other nodes to minimize this power. We can describe the potential at the terminals by

\psi \in \mathbb{R}^T

So, the power for a given potential at the terminals is

Q(\psi) = \displaystyle{ \frac{1}{2} \min_{\phi \in \mathbb{R}^N \; : \; \phi|_T = \psi} \sum_{e \in E} \frac{1}{r_e} \big(\phi(s(e)) - \phi(t(e))\big)^2 }

Actually this is half the power: I stuck in a factor of 1/2 for some reason we’ll soon see. This Q is a quadratic function

Q : \mathbb{R}^T \to \mathbb{R}

so its derivative is linear. And, our work last time showed something interesting: to compute the current J_x flowing into a terminal x \in T, we just differentiate Q with respect to the potential at that terminal:

\displaystyle{ J_x = \frac{\partial Q(\psi)}{\partial \psi_x} }

This is the reason for the 1/2: when we take the derivative of Q, we bring down a 2 from differentiating all those squares, and to make that go away we need a 1/2.

The space of allowed potential-current pairs at the terminals is thus the linear subspace

dQ = \{ (\psi, J) : \; \displaystyle{ J_x = \frac{\partial Q(\psi)}{\partial \psi_x} \}  \subseteq \mathbb{R}^T \times \mathbb{R}^T }

And this completes our precise description of the black box functor!

The hard part is this:

Theorem. \blacksquare : \mathrm{ResCirc} \to \mathrm{LinRel} is a functor.

In other words, we have to prove that it preserves composition:

\blacksquare(fg) = \blacksquare(f) \blacksquare(g)

For that, read our paper:

• John Baez and Brendan Fong, A compositional framework for passive linear networks.


Network Theory (Part 32)

20 October, 2014

Okay, today we will look at the ‘black box functor’ for circuits made of resistors. Very roughly, this takes a circuit made of resistors with some inputs and outputs:

and puts a ‘black box’ around it:

forgetting the internal details of the circuit and remembering only how the it behaves as viewed from outside. As viewed from outside, all the circuit does is define a relation between the potentials and currents at the inputs and outputs. We call this relation the circuit’s behavior. Lots of different choices of the resistances R_1, \dots, R_6 would give the same behavior. In fact, we could even replace the whole fancy circuit by a single edge with a single resistor on it, and get a circuit with the same behavior!

The idea is that when we use a circuit to do something, all we care about is its behavior: what it does as viewed from outside, not what it’s made of.

Furthermore, we’d like the behavior of a system made of parts to depend in a simple way on the external behaviors of its parts. We don’t want to have to ‘peek inside’ the parts to figure out what the whole will do! Of course, in some situations we do need to peek inside the parts to see what the whole will do. But in this particular case we don’t—at least in the idealization we are considering. And this fact is described mathematically by saying that black boxing is a functor.

So, how do circuits made of resistors behave? To answer this we first need to remember what they are!

Review

Remember that for us, a circuit made of resistors is a mathematical structure like this:

It’s a cospan where:

\Gamma is a graph labelled by resistances. So, it consists of a finite set N of nodes, a finite set E of edges, two functions

s, t : E \to N

sending each edge to its source and target nodes, and a function

r : E \to (0,\infty)

that labels each edge with its resistance.

i: I \to \Gamma is a map of graphs labelled by resistances, where I has no edges. A labelled graph with no edges has nothing but nodes! So, the map i is just a trick for specifying a finite set of nodes called inputs and mapping them to N. Thus i picks out some nodes of \Gamma and declares them to be inputs. (However, i may not be one-to-one! We’ll take advantage of that subtlety later.)

o: O \to \Gamma is another map of graphs labelled by resistances, where O again has no edges, and we call its nodes outputs.

The principle of minimum power

So what does a circuit made of resistors do? This is described by the principle of minimum power.

Recall from Part 27 that when we put it to work, our circuit has a current I_e flowing along each edge e \in E. This is described by a function

I: E \to \mathbb{R}

It also has a voltage across each edge. The word ‘across’ is standard here, but don’t worry about it too much; what matters is that we have another function

V: E \to \mathbb{R}

describing the voltage V_e across each edge e.

Resistors heat up when current flows through them, so they eat up electrical power and turn this power into heat. How much? The power is given by

\displaystyle{ P = \sum_{e \in E} I_e V_e }

So far, so good. But what does it mean to minimize power?

To understand this, we need to manipulate the formula for power using the laws of electrical circuits described in Part 27. First, Ohm’s law says that for linear resistors, the current is proportional to the voltage. More precisely, for each edge e \in E,

\displaystyle{ I_e = \frac{V_e}{r_e} }

where r_e is the resistance of that edge. So, the bigger the resistance, the less current flows: that makes sense. Using Ohm’s law we get

\displaystyle{ P = \sum_{e \in E} \frac{V_e^2}{r_e} }

Now we see that power is always nonnegative! Now it makes more sense to minimize it. Of course we could minimize it simply by setting all the voltages equal to zero. That would work, but that would be boring: it gives a circuit with no current flowing through it. The fun starts when we minimize power subject to some constraints.

For this we need to remember another law of electrical circuits: a spinoff of Kirchhoff’s voltage law. This says that we can find a function called the potential

\phi: N \to \mathbb{R}

such that

V_e = \phi_{s(e)} - \phi_{t(e)}

for each e \in E. In other words, the voltage across each edge is the difference of potentials at the two ends of this edge.

Using this, we can rewrite the power as

\displaystyle{ P = \sum_{e \in E} \frac{1}{r_e} (\phi_{s(e)} - \phi_{t(e)})^2 }

Now we’re really ready to minimize power! Our circuit made of resistors has certain nodes called terminals:

T \subseteq N

These are the nodes that are either inputs or outputs. More precisely, they’re the nodes in the image of

i: I \to \Gamma

or

o: O \to \Gamma

The principle of minimum power says that:

If we fix the potential \phi on all terminals, the potential at other nodes will minimize the power

\displaystyle{ P(\phi) = \sum_{e \in E} \frac{1}{r_e} (\phi_{s(e)} - \phi_{t(e)})^2 }

subject to this constraint.

This should remind you of all the other minimum or maximum principles you know, like the principle of least action, or the way a system in thermodynamic equilibrium maximizes its entropy. All these principles—or at least, most of them—are connected. I could talk about this endlessly. But not now!

Now let’s just use the principle of minimum power. Let’s see what it tells us about the behavior of an electrical circuit.

Let’s imagine changing the potential \phi by adding some multiple of a function

\psi: N \to \mathbb{R}

If this other function vanishes at the terminals:

\forall n \in T \; \; \psi(n) = 0

then \phi + x \psi doesn’t change at the terminals as we change the number x.

Now suppose \phi obeys the principle of minimum power. In other words, supposes it minimizes power subject to the constraint of taking the values it does at the terminals. Then we must have

\displaystyle{ \frac{d}{d x} P(\phi + x \psi)\Big|_{x = 0} }

whenever

\forall n \in T \; \; \psi(n) = 0

This is just the first derivative test for a minimum. But the converse is true, too! The reason is that our power function is a sum of nonnegative quadratic terms. Its graph will look like a paraboloid. So, the power has no points where its derivative vanishes except minima, even when we constrain \phi by making it lie on a linear subspace.

We can go ahead and start working out the derivative:

\displaystyle{ \frac{d}{d x} P(\phi + x \psi)! = ! \frac{d}{d x} \sum_{e \in E} \frac{1}{r_e} (\phi_{s(e)} - \phi_{t(e)} + x(\psi_{s(e)} -\psi_{t(e)}))^2  }

To work out the derivative of these quadratic terms at x = 0, we only need to keep the part that’s proportional to x. The rest gives zero. So:

\begin{array}{ccl} \displaystyle{ \frac{d}{d t} P(\phi + x \psi)\Big|_{x = 0} } &=& \displaystyle{ \frac{d}{d x} \sum_{e \in E} \frac{x}{r_e} (\phi_{s(e)} - \phi_{t(e)}) (\psi_{s(e)} - \psi_{t(e)}) \Big|_{x = 0} } \\ \\  &=&   \displaystyle{  \sum_{e \in E} \frac{1}{r_e} (\phi_{s(e)} - \phi_{t(e)}) (\psi_{s(e)} - \psi_{t(e)}) }  \end{array}

The principle of minimum power says this is zero whenever \psi : N \to \mathbb{R} is a function that vanishes at terminals. By linearity, it’s enough to consider functions \psi that are zero at every node except one node n that is not a terminal. By linearity we can also assume \psi(n) = 1.

Given this, the only nonzero terms in the sum

\displaystyle{ \sum_{e \in E} \frac{1}{r_e} (\phi_{s(e)} - \phi_{t(e)}) (\psi_{s(e)} - \psi_{t(e)}) }

will be those involving edges whose source or target is n. We get

\begin{array}{ccc} \displaystyle{ \frac{d}{d x} P(\phi + x \psi)\Big|_{x = 0} } &=& \displaystyle{ \sum_{e: \; s(e) = n}  \frac{1}{r_e} (\phi_{s(e)} - \phi_{t(e)})}  \\  \\        && -\displaystyle{ \sum_{e: \; t(e) = n}  \frac{1}{r_e} (\phi_{s(e)} - \phi_{t(e)}) }   \end{array}

So, the principle of minimum power says precisely

\displaystyle{ \sum_{e: \; s(e) = n}  \frac{1}{r_e} (\phi_{s(e)} - \phi_{t(e)}) = \sum_{e: \; t(e) = n}  \frac{1}{r_e} (\phi_{s(e)} - \phi_{t(e)}) }

for all nodes n that aren’t terminals.

What does this mean? You could just say it’s a set of linear equations that must be obeyed by the potential \phi. So, the principle of minimum power says that fixing the potential at terminals, the potential at other nodes must be chosen in a way that obeys a set of linear equations.

But what do these equations mean? They have a nice meaning. Remember, Kirchhoff’s voltage law says

V_e = \phi_{s(e)} - \phi_{t(e)}

and Ohm’s law says

\displaystyle{ I_e = \frac{V_e}{r_e} }

Putting these together,

\displaystyle{ I_e = \frac{1}{r_e} (\phi_{s(e)} - \phi_{t(e)}) }

so the principle of minimum power merely says that

\displaystyle{ \sum_{e: \; s(e) = n} I_e = \sum_{e: \; t(e) = n}  I_e }

for any node n that is not a terminal.

This is Kirchhoff’s current law: for any node except a terminal, the total current flowing into that node must equal the total current flowing out! That makes a lot of sense. We allow current to flow in or out of our circuit at terminals, but ‘inside’ the circuit charge is conserved, so if current flows into some other node, an equal amount has to flow out.

In short: the principle of minimum power implies Kirchoff’s current law! Conversely, we can run the whole argument backward and derive the principle of minimum power from Kirchhoff’s current law. (In both the forwards and backwards versions of this argument, we use Kirchhoff’s voltage law and Ohm’s law.)

When the node n is a terminal, the quantity

\displaystyle{  \sum_{e: \; s(e) = n} I_e \; - \; \sum_{e: \; t(e) = n}  I_e }

need not be zero. But it has an important meaning: it’s the amount of current flowing into that terminal!

We’ll call this I_n, the current at the terminal n \in T. This is something we can measure even when our circuit has a black box around it:

So is the potential \phi_n at the terminal n. It’s these currents and potentials at terminals that matter when we try to describe the behavior of a circuit while ignoring its inner workings.

Black boxing

Now let me quickly sketch how black boxing becomes a functor.

A circuit made of resistors gives a linear relation between the potentials and currents at terminals. A relation is something that can hold or fail to hold. A ‘linear’ relation is one defined using linear equations.

A bit more precisely, suppose we choose potentials and currents at the terminals:

\psi : T \to \mathbb{R}

J : T \to \mathbb{R}

Then we seek potentials and currents at all the nodes and edges of our circuit:

\phi: N \to \mathbb{R}

I : E \to \mathbb{R}

that are compatible with our choice of \psi and J. Here compatible means that

\psi_n = \phi_n

and

J_n = \displaystyle{  \sum_{e: \; s(e) = n} I_e \; - \; \sum_{e: \; t(e) = n}  I_e }

whenever n \in T, but also

\displaystyle{ I_e = \frac{1}{r_e} (\phi_{s(e)} - \phi_{t(e)}) }

for every e \in E, and

\displaystyle{  \sum_{e: \; s(e) = n} I_e \; = \; \sum_{e: \; t(e) = n}  I_e }

whenever n \in N - T. (The last two equations combine Kirchoff’s laws and Ohm’s law.)

There either exist I and \phi making all these equations true, in which case we say our potentials and currents at the terminals obey the relation… or they don’t exist, in which case we say the potentials and currents at the terminals don’t obey the relation.

The relation is clearly linear, since it’s defined by a bunch of linear equations. With a little work, we can make it into a linear relation between potentials and currents in

\mathbb{R}^I \oplus \mathbb{R}^I

and potentials and currents in

\mathbb{R}^O \oplus \mathbb{R}^O

Remember, I is our set of inputs and O is our set of outputs.

In fact, this process of getting a linear relation from a circuit made of resistors defines a functor:

\blacksquare : \mathrm{ResCirc} \to \mathrm{LinRel}

Here \mathrm{ResCirc} is the category where morphisms are circuits made of resistors, while \mathrm{LinRel} is the category where morphisms are linear relations.

More precisely, here is the category \mathrm{ResCirc}:

• an object of \mathrm{ResCirc} is a finite set;

• a morphism from I to O is an isomorphism class of circuits made of resistors:

having I as its set of inputs and O as its set of outputs;

• we compose morphisms in \mathrm{ResCirc} by composing isomorphism classes of cospans.

(Remember, circuits made of resistors are cospans. This lets us talk about isomorphisms between them. If you forget the how isomorphism between cospans work, you can review it in Part 31.)

And here is the category \mathrm{LinRel}:

• an object of \mathrm{LinRel} is a finite-dimensional real vector space;

• a morphism from U to V is a linear relation R \subseteq U \times V, meaning a linear subspace of the vector space U \times V;

• we compose a linear relation R \subseteq U \times V and a linear relation S \subseteq V \times W in the usual way we compose relations, getting:

SR = \{(u,w) \in U \times W : \; \exists v \in V \; (u,v) \in R \mathrm{\; and \;} (v,w) \in S \}

Next steps

So far I’ve set up most of the necessary background but not precisely defined the black boxing functor

\blacksquare : \mathrm{ResCirc} \to \mathrm{LinRel}

There are some nuances I’ve glossed over, like the difference between inputs and outputs as elements of I and O and their images in N. If you want to see the precise definition and the proof that it’s a functor, read our paper:

• John Baez and Brendan Fong, A compositional framework for passive linear networks.

The proof is fairly long: there may be a much quicker one, but at least this one has the virtue of introducing a lot of nice ideas that will be useful elsewhere.

Next time I’ll define the black box functor more carefully.


Network Theory Seminar (Part 2)

16 October, 2014

 

This time I explain more about how ‘cospans’ represent gadgets with two ends, an input end and an output end:

I describe how to glue such gadgets together by composing cospans. We compose cospans using a category-theoretic construction called a ‘pushout’, so I also explain pushouts. At the end, I explain how this gives us a category where the morphisms are electrical circuits made of resistors, and sketch what we’ll do next: study the behavior of these circuits.

These lecture notes provide extra details:

Network theory (part 31).


Network Theory (Part 31)

13 October, 2014

Last time we came up with a category of labelled graphs and described circuits as ‘cospans’ in this category.

Cospans may sound scary, but they’re not. A cospan is just a diagram consisting of an object with two morphisms going into it:

We can talk about cospans in any category. A cospan is an abstract way of thinking about a ‘chunk of stuff’ \Gamma with two ‘ends’ I and O. It could be any sort of stuff: a set, a graph, an electrical circuit, a network of any kind, or even a piece of matter (in some mathematical theory of matter).

We call the object \Gamma the apex of the cospan and call the morphisms i: I \to \Gamma, o : O \to \Gamma the legs of the cospan. We sometimes call the objects I and O the feet of the cospan. We call I the input and O the output. We say the cospan goes from I to O, though the direction is just a convention: we can flip a cospan and get a cospan going the other way!

If you’re wondering about the name ‘cospan’, it’s because a span is a diagram like this:

Since a ‘span’ is another name for a bridge, and this looks like a bridge from I to O, category theorists called it a span! And category theorists use the prefix ‘co-‘ when they turn all the arrows around. Spans came first historically, and we will use those too at times. But now let’s think about how to compose cospans.

Composing cospans is supposed to be like gluing together chunks of stuff by attaching the output of the first to the input of the second. So, we say two cospans are composable if the output of the first equals the input of the second, like this:

We then compose them by forming a new cospan going all the way from X to Z:

The new object \Gamma +_Y \Gamma' and the new morphisms i'', o'' are built using a process called a ‘pushout’ which I’ll explain in a minute. The result is cospan from X to Z, called the composite of the cospans we started with. Here it is:

So how does a pushout work? It’s a general construction that you can define in any category, though it only exists if the category is somewhat nice. (Ours always will be.) You start with a diagram like this:

and you want to get a commuting diamond like this:

which is in some sense ‘the best’ given the diagram we started with. For example, suppose we’re in the category of sets and Y is a set included in both \Gamma and \Gamma'. Then we’d like A to be the union of \Gamma and \Gamma. There are other choices of A that would give a commuting diamond, but the union is the best. Something similar is happening when we compose circuits, but instead of the category of sets we’re using the category of labelled graphs we discussed last time.

How do we make precise the idea that A is ‘the best’? We consider any other potential solution to this problem, that is, some other commuting diamond:

Then A is ‘the best’ if there exists a unique morphism q from A to the ‘competitor’ Q making the whole combined diagram commute:

This property is called a universal property: instead of saying that A is the ‘best’, grownups say it is universal.

When A has this universal property we call it the pushout of the original diagram, and we may write it as \Gamma +_Y \Gamma'. Actually we should call the whole diagram

the pushout, or a pushout square, because the morphisms i'', o'' matter too. The universal property is not really a property just of A, but of the whole pushout square. But often we’ll be sloppy and call just the object A the pushout.

Puzzle 1. Suppose we have a diagram in the category of sets

where Y = \Gamma \cap \Gamma' and the maps i, o' are the inclusions of this intersection in the sets \Gamma and \Gamma'. Prove that A = \Gamma \cup \Gamma' is the pushout, or more precisely the diagram

is a pushout square, where i'', o'' are the inclusions of \Gamma and \Gamma in the union A = \Gamma \cup \Gamma'.

More generally, a pushout in the category of sets is a way of gluing together sets \Gamma and \Gamma' with some ‘overlap’ given by the maps

And this works for labelled graphs, too!

Puzzle 2. Suppose we have two circuits of resistors that are composable, like this:

and this:

These give cospans in the category L\mathrm{Graph} where

L = (0,\infty)

(Remember from last time that L\mathrm{Graph} is the category of graphs with edges labelled by elements of some set L.) Show that if we compose these cospans we get a cospan corresponding to this circuit:

If you’re a mathematician you might find it easier to solve this kind of problem in general, which requires pondering how pushouts work in L\mathrm{Graph}. Alternatively, you might find it easier to think about this particular example: then you can just check that the answer we want has the desired property of a pushout!

If this stuff seems complicated, well, just know that category theory is a very general, powerful tool and I’m teaching you just the microscopic fragment of it that we need right now. Category theory ultimately seems very simple: I can’t really think of any math that’s simpler! It only seem complicated when it’s unfamiliar and you have a fragmentary view of it.

So where are we? We know that circuits made of resistors are a special case of cospans. We know how to compose cospans. So, we know how to compose circuits… and in the last puzzle, we saw this does just what we want.

The advantage of this rather highbrow approach is that a huge amount is known about composing cospans! In particular, suppose we have any category C where pushouts exist: that is, where we can always complete any diagram like this:

to a pushout square. Then we can form a category \mathrm{Cospan}(C) where:

• an object is an object of C

• a morphism from an object I \in C to an object O \in C is an equivalence classes of cospans from I to O:

• we compose cospans in the manner just described.

Why did I say ‘equivalence class’? It’s because the pushout is not usually unique. It’s unique only up to isomorphism. So, composing cospans would be ill-defined unless we work with some kind of equivalence class of cospans.

To be precise, suppose we have two cospans from I to O:

Then a map of cospans from one to the other is a commuting diagram like this:

We say that this is an isomorphism of cospans if f is an isomorphism.

This gives our equivalence relation on cospans! It’s an old famous theorem in category theory—so famous that it’s hard to find a reference for the proof—that whenever C is a category with pushouts, there’s a category \mathrm{Cospan}(C) where:

• an object is an object of C

• a morphism from an object I \in C to an object O \in C is an isomorphism class of cospans from I to O.

• we compose isomorphism classes of cospans by picking representatives, composing them and then taking the isomorphism class.

This takes some work to prove, but it’s true, so this is how we get our category of circuits!

Next time we’ll do something with this category. Namely, we’ll cook up a category of ‘behaviors’. The behavior of a circuit made of resistors just says which currents and potentials its terminals can have. If we put a circuit in a metaphorical ‘black box’ and refuse to peek inside, all we can see is its behavior.

Then we’ll cook up a functor from the category of circuits to the category of behaviors. We’ll call this the ‘black box functor’. Saying that it’s a functor mainly means that

\blacksquare(f g) = \blacksquare(f) \blacksquare(g)

Here f and g are circuits that we can compose, and f g is their composite. The black square is the black box functor, so \blacksquare(fg) is the behavior of the circuit f g. There’s a way to compose behaviors, too, and the equation above says that the behavior of the composite circuit is the composite of their behaviors!

This is very important, because it says we can figure out what a big circuit does if we know what its pieces do. And this is one of the grand themes of network theory: understanding big complicated networks by understanding their pieces. We may not always be able to do this, in practice! But it’s something we’re always concerned with.


Network Theory Seminar (Part 1)

11 October, 2014

 

Check out this video! I start with a quick overview of network theory, and then begin building a category where the morphisms are electrical circuits. These lecture notes provide extra details:

Network theory (part 30).

With luck, this video will be the first of a series. I’m giving a seminar on network theory at U.C. Riverside this fall. I’ll start by sketching the results here:

• John Baez and Brendan Fong, A compositional framework for passive linear networks.

But this is a big paper, and I also want to talk about other papers, so I certainly won’t explain everything in here—just enough to help you get started! If you have questions, don’t be shy about asking them.

I thank Blake Pollard for filming this seminar, and Muhammad “Siddiq” Siddiqui-Ali for providing the videocamera and technical support.


Follow

Get every new post delivered to your Inbox.

Join 2,875 other followers