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May 22, 2015

PROPs for Linear Systems

Posted by John Baez

PROPs were developed in topology, along with operads, to describe spaces with lots of operations on them. But now some of us are using them to think about ‘signal-flow diagrams’ in control theory—an important branch of engineering. I talked about that here on the n-Café a while ago, but it’s time for an update.

Eric Drexler likes to say: engineering is dual to science, because science tries to understand what the world does, while engineering is about getting the world to do what you want. I think we need a slightly less ‘coercive’, more ‘cooperative’ approach to the world in order to develop ‘ecotechnology’, but it’s still a useful distinction.

For example, classical mechanics is the study of what things do when they follow Newton’s laws. Control theory is the study of what you can get them to do.

Say you have an upside-down pendulum on a cart. Classical mechanics says what it will do. But control theory says: if you watch the pendulum and use what you see to move the cart back and forth correctly, you can make sure the pendulum doesn’t fall over!

Control theorists do their work with the help of ‘signal-flow diagrams’. For example, here is the signal-flow diagram for an inverted pendulum on a cart:

When I take a look at a diagram like this, I say to myself: that’s a string diagram for a morphism in a monoidal category! And it’s true. Jason Erbele wrote a paper explaining this. Independently, Bonchi, Sobociński and Zanasi did some closely related work:

• John Baez and Jason Erbele, Categories in control.

• Filippo Bonchi, Paweł Sobociński and Fabio Zanasi, Interacting Hopf algebras.

• Filippo Bonchi, Paweł Sobociński and Fabio Zanasi, A categorical semantics of signal flow graphs.

Next week I’ll explain some of the ideas at the Turin meeting on the categorical foundations of network theory. But I also want to talk about this new paper that Simon Wadsley of Cambridge University wrote with my student Nick Woods:

• Simon Wadsley and Nick Woods, PROPs for linear systems.

This makes the picture neater and more general!

You see, Jason and I used signal flow diagrams to give a new description of the category of finite-dimensional vector spaces and linear maps. This category plays a big role in the control theory of linear systems. Bonchi, Sobociński and Zanasi gave a closely related description of an equivalent category, Mat(k), \mathrm{Mat}(k), where:

• objects are natural numbers, and

• a morphism f:mn f : m \to n is an n×m n \times m matrix with entries in the field k, k,

and composition is given by matrix multiplication.

But Wadsley and Woods generalized all this work to cover Mat(R)\mathrm{Mat}(R) whenever R R is a commutative rig. A rig is a ‘ring without negatives’—like the natural numbers. We can multiply matrices valued in any rig, and this includes some very useful examples… as I’ll explain later.

Wadsley and Woods proved:

Theorem. Whenever R R is a commutative rig, Mat(R) \mathrm{Mat}(R) is the PROP for bicommutative bimonoids over R. R.

This result is quick to state, but it takes a bit of explaining! So, let me start by bringing in some definitions.

Bicommutative bimonoids

We will work in any symmetric monoidal category, and draw morphisms as string diagrams.

A commutative monoid is an object equipped with a multiplication:

and a unit:

obeying these laws:

For example, suppose FinVect k \mathrm{FinVect}_k is the symmetric monoidal category of finite-dimensional vector spaces over a field k k, with direct sum as its tensor product. Then any object VFinVect k V \in \mathrm{FinVect}_k is a commutative monoid where the multiplication is addition:

(x,y)x+y (x,y) \mapsto x + y

and the unit is zero: that is, the unique map from the zero-dimensional vector space to V. V.

Turning all this upside down, cocommutative comonoid has a comultiplication:

and a counit:

obeying these laws:

For example, consider our vector space VFinVect k V \in \mathrm{FinVect}_k again. It’s a commutative comonoid where the comultiplication is duplication:

x(x,x) x \mapsto (x,x)

and the counit is deletion: that is, the unique map from V V to the zero-dimensional vector space.

Given an object that’s both a commutative monoid and a cocommutative comonoid, we say it’s a bicommutative bimonoid if these extra axioms hold:

You can check that these are true for our running example of a finite-dimensional vector space V. V. The most exciting one is the top one, which says that adding two vectors and then duplicating the result is the same as duplicating each one, then adding them appropriately.

Our example has some other properties, too! Each element ck c \in k defines a morphism from V V to itself, namely scalar multiplication by c: c:

xcx x \mapsto c x

We draw this as follows:

These morphisms are compatible with the ones so far:

Moreover, all the ‘rig operations’ in k k—that is, addition, multiplication, 0 and 1, but not subtraction or division—can be recovered from what we have so far:

We summarize this by saying our vector space V V is a bicommutative bimonoid ‘over k k’.

More generally, suppose we have a bicommutative bimonoid A A in a symmetric monoidal category. Let End(A) \mathrm{End}(A) be the set of bicommutative bimonoid homomorphisms from A A to itself. This is actually a rig: there’s a way to add these homomorphisms, and also a way to ‘multiply’ them (namely, compose them).

Suppose R R is any commutative rig. Then we say A A is a bicommutative bimonoid over R R if it’s equipped with a rig homomorphism

Φ:REnd(A) \Phi : R \to \mathrm{End}(A)

This is a way of summarizing the diagrams I just showed you! You see, each cR c \in R gives a morphism from A A to itself, which we write as

The fact that this is a bicommutative bimonoid endomorphism says precisely this:

And the fact that Φ \Phi is a rig homomorphism says precisely this:

So sometimes the right word is worth a dozen pictures!

What Jason and I showed is that for any field k, k, the FinVect k \mathrm{FinVect}_k is the free symmetric monoidal category on a bicommutative bimonoid over k. k. This means that the above rules, which are rules for manipulating signal flow diagrams, completely characterize the world of linear algebra!

Bonchi, Sobociński and Zanasi used ‘PROPs’ to prove a similar result where the field is replaced by a sufficiently nice commutative ring. And Wadlsey and Woods used PROPS to generalize even further to the case of an arbitrary commutative rig!

But what are PROPs?

PROPs

A PROP is a particularly tractable sort of symmetric monoidal category: a strict symmetric monoidal category where the objects are natural numbers and the tensor product of objects is given by ordinary addition. The symmetric monoidal category FinVect k \mathrm{FinVect}_k is equivalent to the PROP Mat(k), \mathrm{Mat}(k), where a morphism f:mn f : m \to n is an n×m n \times m matrix with entries in k, k, composition of morphisms is given by matrix multiplication, and the tensor product of morphisms is the direct sum of matrices.

We can define a similar PROP Mat(R) \mathrm{Mat}(R) whenever R R is a commutative rig, and Wadsley and Woods gave an elegant description of the ‘algebras’ of Mat(R) \mathrm{Mat}(R). Suppose C C is a PROP and D D is a strict symmetric monoidal category. Then the category of algebras of C C in D D is the category of strict symmetric monoidal functors F:CD F : C \to D and natural transformations between these.

If for every choice of D D the category of algebras of C C in D D is equivalent to the category of algebraic structures of some kind in D, D, we say C C is the PROP for structures of that kind. This explains the theorem Wadsley and Woods proved:

Theorem. Whenever R R is a commutative rig, Mat(R) \mathrm{Mat}(R) is the PROP for bicommutative bimonoids over R. R.

The fact that an algebra of Mat(R) \mathrm{Mat}(R) is a bicommutative bimonoid is equivalent to all this stuff:

The fact that Φ(c) \Phi(c) is a bimonoid homomorphism for all cR c \in R is equivalent to this stuff:

And the fact that Φ \Phi is a rig homomorphism is equivalent to this stuff:

This is a great result because it includes some nice new examples.

First, the commutative rig of natural numbers gives a PROP Mat. \mathrm{Mat}. This is equivalent to the symmetric monoidal category FinSpan, \mathrm{FinSpan}, where morphisms are isomorphism classes of spans of finite sets, with disjoint union as the tensor product. Steve Lack had already shown that FinSpan \mathrm{FinSpan} is the PROP for bicommutative bimonoids. But this also follows from the result of Wadsley and Woods, since every bicommutative bimonoid V V is automatically equipped with a unique rig homomorphism

Φ:End(V) \Phi : \mathbb{N} \to \mathrm{End}(V)

Second, the commutative rig of booleans

𝔹={F,T} \mathbb{B} = \{F,T\}

with ‘or’ as addition and ‘and’ as multiplication gives a PROP Mat(𝔹). \mathrm{Mat}(\mathbb{B}). This is equivalent to the symmetric monoidal category FinRel \mathrm{FinRel} where morphisms are relations between finite sets, with disjoint union as the tensor product. Samuel Mimram had already shown that this is the PROP for special bicommutative bimonoids, meaning those where comultiplication followed by multiplication is the identity:

But again, this follows from the general result of Wadsley and Woods!

Finally, taking the commutative ring of integers , \mathbb{Z}, Wadsley and Woods showed that Mat() \mathrm{Mat}(\mathbb{Z}) is the PROP for bicommutative Hopf monoids. The key here is that scalar multiplication by 1 -1 obeys the axioms for an antipode—the extra morphism that makes a bimonoid into a Hopf monoid. Here are those axioms:

More generally, whenever R R is a commutative ring, the presence of 1R -1 \in R guarantees that a bimonoid over R R is automatically a Hopf monoid over R. R. So, when R R is a commutative ring, Wadsley and Woods’ result implies that Mat(R) \mathrm{Mat}(R) is the PROP for Hopf monoids over R. R.

Earlier, in their paper on ‘interacting Hopf algebras’, Bonchi, Sobociński and Zanasi had given an elegant and very different proof that Mat(R) \mathrm{Mat}(R) is the PROP for Hopf monoids over R R whenever R R is a principal ideal domain. The advantage of their argument is that they build up the PROP for Hopf monoids over R R from smaller pieces, using some ideas developed by Steve Lack. But the new argument by Wadsley and Woods has its own charm.

In short, we’re getting the diagrammatics of linear algebra worked out very nicely, providing a solid mathematical foundation for signal flow diagrams in control theory!

Posted at May 22, 2015 8:43 PM UTC

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3 Comments & 0 Trackbacks

Re: PROPs for Linear Systems

This is really neat; thanks!

Matrices also come up naturally in a categorified setting: matrices with entries in a cocomplete monoidal category form a bicategory with some interesting properties, e.g. Mat(Set)Span(Set)Mat(Set) \cong Span(Set), and monads in Mat(V)Mat(V) are VV-enriched categories. I suppose this means Mat(V)Mat(V) (restricted to finite sets as objects) is likely to be something like the “2-PROP” for “bisymmetric pseudo-bimonoids over VV”? Does anyone study pseudo-bimonoids? (Unfortunately, “bimonoidal category” has been taken to mean “rig category”, rather than a categorification of bimonoids.)

Posted by: Mike Shulman on May 24, 2015 4:21 AM | Permalink | Reply to this

Re: PROPs for Linear Systems

I haven’t heard anyone study pseudo-bimonoids — they sound like the kind of thing I’d like to study.

Comonoids in SetSet are boring, in that every set has a unique comonoid structure and all maps preserve this structure. The same is true for comonoids in any category with finite products: the diagonal and the morphism to the terminal object make any object into a comonoid in the only way possible. We need to look at comonoids in another sort of symmetric monoidal category for the concept to become interesting.

So, bimonoids in a category with finite products are ‘half-boring’: the only interesting part is the monoid part. I expect the same will be true of pseudo-bimonoids in a 2-category with finite products, like CatCat. We should look at them in some noncartesian symmetric monoidal 2-category.

Posted by: John Baez on May 24, 2015 10:48 AM | Permalink | Reply to this

Re: PROPs for Linear Systems

Yes, makes sense. Like VCatV-Cat for a non-cartesian VV? Or is that still going to be “too cartesian” (since the tensor product of VV-categories acts as the cartesian product on sets of objects)? What about 2Vect2Vect?

Posted by: Mike Shulman on May 25, 2015 7:43 AM | Permalink | Reply to this

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