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.

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, where:

• objects are natural numbers, and

• a morphism is an matrix with entries in the field

and composition is given by matrix multiplication.

But Wadsley and Woods generalized all this work to cover whenever 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 is a commutative rig, is the PROP for bicommutative bimonoids over

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 is the symmetric monoidal category of finite-dimensional vector spaces over a field , with *direct sum* as its tensor product. Then any object is a commutative monoid where the multiplication is **addition**:

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

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

and a **counit**:

obeying these laws:

For example, consider our vector space again. It’s a commutative comonoid where the comultiplication is **duplication**:

and the counit is **deletion**: that is, the unique map from 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 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 defines a morphism from to itself, namely scalar multiplication by

We draw this as follows:

These morphisms are compatible with the ones so far:

Moreover, all the ‘rig operations’ in —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 is a bicommutative bimonoid ‘over ‘.

More generally, suppose we have a bicommutative bimonoid in a symmetric monoidal category. Let be the set of bicommutative bimonoid homomorphisms from 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 is any commutative rig. Then we say is a bicommutative bimonoid **over ** if it’s equipped with a rig homomorphism

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

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

And the fact that 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 the is the free symmetric monoidal category on a bicommutative bimonoid over 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 is equivalent to the PROP where a morphism is an matrix with entries in 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 whenever is a commutative rig, and Wadsley and Woods gave an elegant description of the ‘algebras’ of . Suppose is a PROP and is a strict symmetric monoidal category. Then the **category of algebras of in ** is the category of strict symmetric monoidal functors and natural transformations between these.

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

**Theorem.** Whenever is a commutative rig, is the PROP for bicommutative bimonoids over

The fact that an algebra of is a bicommutative bimonoid is equivalent to all this stuff:

The fact that is a bimonoid homomorphism for all is equivalent to this stuff:

And the fact that 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 This is equivalent to the symmetric monoidal category where morphisms are isomorphism classes of spans of finite sets, with disjoint union as the tensor product. Steve Lack had already shown that is the PROP for bicommutative bimonoids. But this also follows from the result of Wadsley and Woods, since every bicommutative bimonoid is automatically equipped with a unique rig homomorphism

Second, the commutative rig of booleans

with ‘or’ as addition and ‘and’ as multiplication gives a PROP This is equivalent to the symmetric monoidal category 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 Wadsley and Woods showed that is the PROP for bicommutative Hopf monoids. The key here is that scalar multiplication by obeys the axioms for an **antipode**—the extra morphism that makes a bimonoid into a **Hopf monoid**. Here are those axioms:

More generally, whenever is a commutative ring, the presence of guarantees that a bimonoid over is automatically a Hopf monoid over So, when is a commutative ring, Wadsley and Woods’ result implies that is the PROP for Hopf monoids over

Earlier, in their paper on ‘interacting Hopf algebras’, Bonchi, Sobociński and Zanasi had given an elegant and very different proof that is the PROP for Hopf monoids over whenever is a principal ideal domain. The advantage of their argument is that they build up the PROP for Hopf monoids over 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!

Reblogged this on Μαθεσις universalis οντοποσοφια.

Great summary, very useful!

If you don’t mind, I will use your comments to plug my blog:

http://graphicallinearalgebra.net/

where I plan to develop all this, but taking a much more leisurely pace. I have just written about bicommutative bimonoids over the weekend, and I plan to spend some time discussing the various approaches to proving the results that you mention here. It seems that the Wadsley Woods papers gives a really nice new perspective on things!

Thanks, Pawel! I’m glad you plan to blog about this! A more leisurely pace would make this material easier to understand.

At first I read this as saying that the tensor product of morphisms was given by ordinary element-by-element matrix addition, which seemed remarkably weird. Then I realized, “Oh, right,

directsum.”Yeah, it’s all about direct sums, nothing weird at all! This stuff is maximally simple and nice: just ordinary linear algebra. But it’s done in a somewhat new way, which I suppose could be completely disorienting. We’re doing it with signal flow diagrams—like control theorist do. And we’re explaining that setup using modern math, like PROPs.

If our field is the good old real numbers, we write as an abbreviation for The direct sum of objects is

which we abbreviate as addition of natural numbers: adding and gives We write

to mean a linear map

or more concretely an matrix. We compose these maps by matrix multiplication. And given linear maps

and

we get a linear map

corresponding to the block diagonal matrix with and as its blocks.

All this stuff is called the PROP Then you can replace by any other commutative rig.

By the way, if you are enjoying this blog, I think that you will also enjoy Dan Ghica’s wonderful series on inventing an algebraic knot theory for kids: Dan uses a similar diagrammatic language to talk about knots, but there’s one major difference between his diagrams and ours: his wires tangle! No wonder, since wires that do not tangle are not particularly useful if you want to construct knots. If you want the “real” reason, it is that his underlying mathematical structure is a braided monoidal category, whereas ours is symmetric monoidal category.

There is one more blog to mention: John Baez has recently been writing about PROPs and linear systems; check it out if you want a sneak preview of one of the star applications of graphical linear algebra! John and his student Jason Erbele developed the theory independently and more or less at the same time as Filippo, Fabio and I. But, apart from the fact that they have different conventions for drawing diagrams, the equational theory that they have developed can be shown to be equivalent to the one we will develop here. It’s super interesting that science is full of temporal coincidences like this; it’s not even the first time that this kind of thing happens to me!

Typo: “Say you an upside-down pendulum on a cart.”

Whoops! I’ll fix that.