Tilescope

Tilescope is an algorithm that builds a proof tree using several different proof strategies. The goal is to make the algorithm powerful enough to find proof trees that allow us to enumerate permutation classes avoiding patterns of length four. In the beginning we will start with a relatively simple algorithm that can handle bases with many length four patterns. As we consider smaller bases we will start seeing the algorithm fail and will then add new strategies to turn those failures into successes. We feel that it is natural to start with a known approach to connect with the current state of the literature. Currently we want to take the regular insertion encoding as the starting point. This is mainly because this is the only automatic method for which you can know a priori whether or not it will succeed. This depends on whether the basis of the permutation class intersects the permutation classes Av(123, 3142, 3412) and Av(132, 312), as well as the reversals of these classes. As the goal is to consider all bases of length four patterns, this condition allows us discard a large number of bases and focus on more complicated ones.

Quick note on the current versions: On the branch called v2 we have a version implementing strategies that do not achieve mimicking regular insertion encoding. That version is hard to add to so we are starting from scratch, on the master branch. The current version is however quite powerful. Jay wrote another implementation of the meta-tree that uses components to handle recursions. This is on the pantone_tree branch.

Roadmap

Step 0: Mimick the regular insertion encoding

Recall how the regular insertion encoding finds the structure of the class Av(123, 132):

The most basic implementation of Tilescope mimicks the regular insertion encoding. Notation for the next figure: X is a permutation class, epsilon (e here) is the empty permutation, X with a dot in the middle (X-e here) is a class with the empty permutation removed, and o is the point. At this stage we are leaning towards calling classes of the form X-e positive classes. We start with X at the root and use the following proof strategy to branch:

Cell insertion (ci): Given a cell marked with an X, create a left child with X replaced by e, and a right child with X replaced by X-e.

The left child is 'verified' meaning that it represents a subset of the class X. To progress from the right child we need a new proof strategy:

Place new maximum (nm): If there are no cells marked with 'X' in the top row of the tiling then branch (into as many branches as there are X-e's) depending on where the new maximum is. Note that illegal placements of the new maximum are not drawn. (Also note that when this is applied with a single X-e then we don't draw an edge pointing down, but rather an '=' since this is just another viewpoint on the same subset of X.)

To mimick the loops in the automaton created by regular insertion encoding we borrow reversibly-deletable points from enumeration schemes: A point o is reversibly-deletable if there is an isomorphism between the subset of the class X generated by a tiling T, and the subset of the class X generated by a tiling T-o. We call this strategy recursion (r). These are drawn with dashed arrows.

It should be easy to argue that this version of Tilescope is equivalent to regular insertion encoding. The proof trees outputted by it should also be easily turned into generating functions for the classes.

A final note on this version: Since we need to turn all X's in the top row into X-e's before we can apply (nm) this implies that the algorithm explores exactly one proof tree. This will change below when we have multiple choices for proceeding from a tiling.

Step 1: Generalizing (nm), adding (pp) and (rcs), and inferral of cells in a tiling

A natural generalization of (nm) is choosing a row or column and inserting a new bottom-most or top-most point in the row; or a left-most or right-most point in the column. We call this strategy row/column insertion (rci).

It is not settled what the first generalization of (r) will be, but probably at least allowing reversibly-deletable cells (not just points). Also keep in mind that non-ancestral recursions are easier to implement and understand. Also note that recursions that stay within a proof tree are easier to understand.

Instead of having to consider entire rows or columns when inserting new points we can take a cell marked with X-e and insert the top-most, bottom-most, left-most, or right-most point into it. This is point-placement (pp).

The proof strategy row-column separation (rcs) splits rows or columns depending on whether crossing 12's or 21's are allowed. Think of the structure of Av(231). This can be generalized to multiple cells in a row or column.

For two cells ci and cj in the same row say ci < cj if i < j and placing a 21 is not allowed or i > j and placing 12 is not allowed. If a row's cells forms a partial order with this relation (it is possible for ci < cj and cj < ci) and this forms a ranked poset (a poset that has the property that for every element x, all maximal chains among those with x as greatest element have the same finite length - this ensure minimal elements have the same rank) then the row splits into multiple rows. The ith row has the cells of rank i.

Finally, whenever we apply a proof strategy that adds a point or an X-e we should infer (i) what the rest of the cells need to avoid, instead of just marking them with an X.

Step 2: Generalizing (r)

First a preliminary definition: Let T be a tiling with a cell c, and let w be a permutation in the grid class of T. Then w-c is the permutation obtained by deleting the points in w that were contributed by c.

START OF OLD DEFINITIONS OF RECURSION

There are four definitions that impact how we think about recursions:

Definition 1 Let T be a tiling before inferral, meaning that all the blocks are of the type Av(B) or Av(B)-e, where B is the input basis. A cell (containing a point or a class) is reversibly deletable if for any permutation w in the grid class of T satisfies: If w contains a pattern from B, then so does w-c. Equivalently we can say that if w contains a pattern from B then there is at least one occurrence of a pattern from B that does not have points in c.

Definition 2 We can make the same definition about a tiling after inferral and this leads to a slightly different behavior of the reversibly delatable cells.

We should probably prove a lemma that says that one type implies the other.

Definition 3 Let T be a tiling before inferral. Define graph structure on the on the cells of T as follows: A cell u has an (undirected) edge to a cell v if there exist a permutation in the grid class of T that contains an occurrence of a basis pattern, that has points from both u and v. A component of T is a connected component of this graph.

Definition 4 We can make the same definition about a tiling after inferral and this leads to a slightly different behavior of the reversibly delatable cells.

We should probably prove a lemma that says that one type implies the other.

In the v2 implementation Definition 2 is being used.

In Jay's implementation of the meta-tree Definition 4 is being used, and he looks for recursions to a tiling made up of any combination of components.

I think eventually we will consider all of these together: E.g., compute the components (before or after inferral) and try deleting reversibly deletable cells from these.

END OF OLD DEFINITIONS OF RECURSION

Note also that recursion to an ancester is good, while recursion to a non-ancestor does not directly lead to verification.

With these proof strategies (and some version of recursion) we should be able to find a proof tree for any Av(B) such that B contains at least one length 3 pattern and one length 4 pattern. (Note that out of about 14,000 such bases, only 4 do not have a regular insertion encoding.) Also a host of interesting examples, such as the separable permutations, and I would hope most of the 3x4 classes.

Note that we can get a proof tree for Av(123) but it does not easily imply that the class is counted by the Catalan numbers, see Step 3 below on isomorphic proof trees.

Step 3: Generalizing (ci), adding fission/fusion (ff)

To be able to mimick Zeilberger's original enumeration schemes we need to have fission and fusions (ff) of rows and columns.

Here is the enumeration scheme given by Zeilberger (he wrote it out in plain English.)

With these proof strategies we can find Zeilberger's original enumeration schemes. In particular we will be able to find a tree for Av(132) which is almost the same as the one for Av(123):

If we define isomorphisms of proof trees we can prove that Av(123) is Wilf-equivalent to Av(132). From Step 1 we will have established that Av(132) is enumerated by the Catalan numbers. This will finally give us a fully automatic Wilf-classification of all subsets of S3.

The strategy (ci) creates two branches depending on whether a cell avoids the pattern 1 (= is empty) or contains the pattern 1 (= is non-empty). This can be generalized by replacing 1 with an arbitrary pattern p. On the right branch where the pattern is contained (assuming this tiling is not verified) we can use a binary mesh pattern coincident with p (we say two patterns are coincident if Av(m) = Av(m'); a pattern is binary if it is contained in a permutation if and only if it is contained exactly once in the permutation) to place the points in the cell.

The 'binaryness' of the mesh pattern allows the placement of the points to be unique, preserving enumeration. We call this generalization of (ci) cell insertion with a pattern (cip). Inserting a binary mesh pattern into the cell we call binary mesh pattern placement (bmpp). In the above figure the basis of X implied all the shadings. In general we sometimes need to use a result like the shading lemma and the shading algorithm to make the pattern binary.

Having (cip) would imply we can do any Av(132, p) where p is any pattern (see paper by Mansour and Vainshtein). We would want to prove a stronger result: that we can do any subclass of Av(132).

A natural follow-up to automatically Wilf-classifying S3 is to try to do as much as possible of S4. A nice goal would be at least all bases with four or more patterns.

At this stage we should have a powerful enough algorithm to to some interesting classes, that have been enumerated by hand, as well as some unenumerated classes. Jay suggests that if we have interesting results we should aim for a general mathematics journal. I would agree.

Step 4: Gap matrices and more

Vatter defined gap vectors for his enumeration schemes. In some sense they are tools for early termination of the nodes in the scheme. In another sense they control how a point can mix into a block. In a third sense, they mess with enumerations. Our nodes are two-dimensional so we can define (completely analogously) gap matrices.

Christian thinks we might be able to mimick substition decomposition. He'll put more on that later.

There is a slight generalization of (rcs) which might be useful at some point: Branch into a left child where there is no crossing 12 between two cells, and a right child where there is a crossing 12. This only works if the crossing 12 can be made unique some how (similar to a binary mesh pattern).

This strategy can also be thought of as part of the following more general idea:

Can we define a space of proof strategies and search it for good ones? E.g., one can generalize (ci) and (rci) to a common strategy which puts a pattern into a group of cells.

At this stage we will have a large collection of inputs (bases) and successful outputs (proof trees). Can we train an AI on this? Can we apply some big data or machine learning methods to this data set? There are some people at RU that know alot about this kind of stuff. We were also able to get people at ICERM (Brown University) excited about this, but are not ready with enough data.

The papers

I put authors down according to what I guessed would make sense. Nothing is set in stone. I would love for everybody to be everywhere if they want.

First paper on atrap (Albert, Ardal, Bean, Claesson, Magnusson, Pantone, Tannock, Ulfarsson)

• Initial proof strategies: (ci), (nm), basic (r) => regular insertion encoding
• Generalized, or new proof strategies: (rci), general (r), (pp), (rcs), (i)
• Say we can do all bases B with one S3 and one S4 pattern, point to PermPAL paper for enumerations
• Say we can do all bases B that struct succeeded, on point to PermPAL paper for enumerations
• New proof strategies: (ff) => Zeilberger's original enumeration schemes
• Isomorphisms of proof trees: Fully automatic Wilf-classification of S3
• Even more proof strategies: (cip), (bmpp), very general (r)
• A collection of nice S4 bases that we handle

PermPAL paper (Undergrads, Ardal, Claesson, Bean, Pantone, Ulfarsson)

• Turning struct covers into enumeration
• Turning atrap trees into enumeration
• Automatic Wilf-classification of bases B with one S3 and one S4 pattern
• Automatic Wilf-classification of bases B that struct succeeded on
• PermPAL.

Second paper on atrap - we need to see what overflows from the first (?)

• Gap matrices?
• Defining and searching a space of proof strategies
• AI, machine learning, big data method?

Thesis work of students

Ragnar Ardal

Ragnar is the main implementer of the meta-tree of atrap and a lot of the underpinnings of the algorithm. He wrote a very clever and fast algorithm for avoidance testing which warrants a section of his thesis. He will be an author on both atrap papers. He also wants to implement a Monte-Carlo version of atrap. That might also become part of one of the atrap papers, or a separate paper.

Papers from, or with a non-empty intersection with, thesis

• First atrap paper (see above)
• Second atrap paper (see above)
• Perhaps a Monte-Carlo paper, or that becomes part of one of the atrap papers
• The PermPAL paper (see above)

Christian Bean

Christian is an author on the paper about struct. He will also be an author on both atrap papers. His thesis can also include his work on vincular-covincular patterns and the independent subsets of graphs paper (both submitted)

Papers from, or with a non-empty intersection with, thesis

• Struct paper (with Gudmundsson and Ulfarsson), proof-reading
• First atrap paper (see above)
• Second atrap paper (see above)
• Maybe: vincular-covincular (submitted)
• Maybe: independent sets in graphs (submitted)

Bjarki Gudmundsson

Gudmundsson is also an author of the struct paper and implemented the algorithm. That should can be a part of his thesis. Also the work he did with Magnusson on the shading algorithm (the next student). He is currently working on something with Claesson.

Papers from, or with a non-empty intersection with, thesis

• Shading algorithm (with Magnusson and Ulfarsson), mostly ready
• Struct paper (with Bean and Ulfarsson), proof-reading
• A paper with Claesson I think

Tomas Magnusson

The work Magnusson did with Gudmundsson on the shading algorithm (sha) is necessary for finding (close to) all binary mesh patterns that are coincident with a classical pattern. He will also need to combine that work with what Tannock did in his MSc thesis about coincidences of patterns inside a permutation class. Finally building upon an example from Tannock's thesis, he will implement the inductive shading algorithm (isha) which is a generalization of (sha) and is hopefully strong enough to complete the coincidence classification of mesh patterns of length 3. He will implement the binary mesh pattern placement of atrap and therefore be an author on the atrap paper where we put that proof strategy.

Papers from, or with a non-empty intersection with, thesis

• Shading algorithm (with Gudmundsson and Ulfarsson), mostly ready
• First atrap paper (see above)
• Inductive shading algorithm paper (with Tannock and Ulfarsson)

Undergrad group (4 students)

These students have been parsing the logs from Struct (conjectured covers of permutation classes) and presenting them at PermPAL. They have also turned the structural descriptions into recurrence relations and are starting to turn them into generating functions as well. When Tilescope is able to find a proof tree for all bases B with at least one S3 patterna and at least on S4 pattern (almost possible now: 28 of them have external recursions - fixable), as well as all (most?) of the classes that Struct succeeded on I think we should write a paper on the Wilf-classification of that set and about the PermPAL. Here is a completely trivial class that demonstrates what I really like about PermPAL (at least when it is fully populated): If you keep clicking the classes that your starting class refers to you eventually reach a trivial class like Av(12,21) (Schroedinger's point!).

Papers from, or with a non-empty intersection with, thesis

• The PermPAL paper (see above)