In grad school I ran across an cool problem and an interesting algorithm to solve it. Your input is a grid of elements, lets say rows 1, 2, 3, 4, 5 and columns A, B, C, D, E. In addition, you are given a set of "rectangles" which each consist of a subset of the grid's rows and a subset of the grid's columns. Is there a way to arrange the grid by permuting it's rows and columns such that all the rectangles are present contiguously?

Imagine a rectangle consisting of rows 2 and 5 and columns A, C, and D. The grid must be arranged such that row (2, 5) are contiguous and columns (A, C, D) are contiguous. One rectangle is trivial, multiple rectangles is a challenge. Take a look at the figure which visualizes one of these grids with embedded rectangles.

I realized the 2D problem decomposes into two separate 1D problems. The rows can be arranged without caring about the columns and vice versa. Once you reduce the problem to 1D, it turns out that it is a problem known as the Consecutive Ones Problem. If all you want to do is find an valid ordering, there is even an algorithm to solve the problem. In my case, there was rarely a perfect ordering, so I had to extend the problem to find the best ordering for some definition of

That would be the end of the boring story, except I mentioned that there was a algorithm that solved the perfect ordering problem and it was well-studied. It turned out that despite it being well studied, it is poorly known and virtually unreferenced on the internet.

The paper that explained it,

The algorithm is called the "PQ-Tree Algorithm". I picked up a copy of the paper in book-form from my local library. Surprisingly, the problem can be solved in linear time. It is quite an intricate algorithm. I think I recall spending 2 weeks doing nothing but implementing this algorithm.

Lets make sure that you know what the problem is first. I'll state it simpler than my above 2D problem:

Furthermore, there may be many of these types of constraints, so perhaps you have (Sue, Fred, Bob) as one constraint and another might be (Sue, Bob, Tom). One possible solution would be given as:

This is the crux of consecutive ones problem, although the consecutive ones problem is formulated a little differently: Given a binary matrix containing only ones and zeros, reorder the columns such that the ones in every row are consecutive. The image below illustrates the point:

The matrix (a) can be transformed to the matrix (b) be rearranging only the columns in (a). The matrix (b) has the consecutive ones property (COP) in that the 1 values in each row are consecutive when read in order. The consecutive ones problem is: Given a matrix (a), determine if it can be converted into a matrix (b) by rearranging columns such that (b) has the consecutive ones property, and if so represent all valid orderings of the columns.

To see that this is the same as the guest seating problem, assign each guest to a column in matrix A. Each row represents one of the constraints we want to apply to our seating - the columns in that row containing 1's are the guests that we want to seat together.

Not all matrices can be transformed into a COP matrix. Similarly, some matrices can have many possible orderings.

A PQ-tree has 3 types of nodes:

A PQ-Tree represents all valid solutions to a COP problem. An in-order traversal of the leaves of the tree gives you one valid solution, and by reordering the children of P and Q nodes in a simple way, one can produce all possible valid solutions:

Take, for example, the PQ-tree diagram to the left. One valid ordering from this PQ-tree is 1,2,3,4,5 given by an in-order traversal. If the children of the P-node were reordered as 4,2,3 we see another valid ordering is 1,4,2,3,5. Leaving the reordered children of the P-node, and reversing the order of the children of the Q-node, we construct another valid ordering of 5,3,2,4,1. The ordering of the leaves, read via an in-order traversal (aka: left to right), is called the PQ-tree's

###

###

The universal PQ-tree is incrementally modified by each constraint in the COP problem. A constraint is simply a set of leaves that must appear consecutively. The PQ Tree algorithm guarantees that any constraint can be applied in O(n) operations where n is the size of the constraint set. This ultimately means that the

When applying a constraint, we consider each leaf node in the constraint asnodes are displayed in diagrams below as black and grey shapes respectively. An internal node which has both

Applying a constraint is known as a

Children of Q-nodes which are interior will use their endmost-sibling's pointer and will remember this new pointer during the bubble phase. When an interior Q-node is encountered, we check to see if either of it's siblings have valid parent pointers. If not, that node becomes

P3 Pattern

P3's replacement is unique in that it's structure cannot ever be found within a PQ Tree between reductions. One of the rules describing a Q-node is that it must have >2 children, otherwise a P-node/Q-node distinction is ambiguous. Hence P3's replacement is considered a "pseudonode" as it will not survive in it's current form in later steps in the current reduction phase. When the replacement's parent is processed (and P3 only matches if it has a pertinent parent), the pseudonode will be transformed into a subtree containing only valid nodes.

P4 Pattern

###

###

###

I've created a project up at github which tracks the code: PQ Tree Algorithm Implementation.The code (just a library) should be decent, bug-free, and GPL. The first internet implementation out there.

If you stumble across this page while trying desperately to do some research for your graduate program, I hope this is useful to you. I ask one small thing of you though. I spent a good bit of time putting all of this together. Drop me a comment or email letting me know how you are using it. That way I'll know it was worth the effort.

Imagine a rectangle consisting of rows 2 and 5 and columns A, C, and D. The grid must be arranged such that row (2, 5) are contiguous and columns (A, C, D) are contiguous. One rectangle is trivial, multiple rectangles is a challenge. Take a look at the figure which visualizes one of these grids with embedded rectangles.

I realized the 2D problem decomposes into two separate 1D problems. The rows can be arranged without caring about the columns and vice versa. Once you reduce the problem to 1D, it turns out that it is a problem known as the Consecutive Ones Problem. If all you want to do is find an valid ordering, there is even an algorithm to solve the problem. In my case, there was rarely a perfect ordering, so I had to extend the problem to find the best ordering for some definition of

*best*(see this paper on the subject).### The PQ Tree Algorithm

That would be the end of the boring story, except I mentioned that there was a algorithm that solved the perfect ordering problem and it was well-studied. It turned out that despite it being well studied, it is poorly known and virtually unreferenced on the internet.

The paper that explained it,

*Testing for the consecutive ones property, interval graphs, and graph planarity using PQ-tree algorithms*, came out in 1976. The paper doesn't exist anywhere on the internet, not even for purchase. No full description or implementation of the algorithm exists on the internet either. None of this has changed in the 5 years since I looked into this algorithm - still barely any references on the web. I intend to change that today.The algorithm is called the "PQ-Tree Algorithm". I picked up a copy of the paper in book-form from my local library. Surprisingly, the problem can be solved in linear time. It is quite an intricate algorithm. I think I recall spending 2 weeks doing nothing but implementing this algorithm.

Lets make sure that you know what the problem is first. I'll state it simpler than my above 2D problem:

### Consecutive Ones Problem

Imagine that you are having a dinner party and are planning the seating arrangement. To simplify the problem, imagine your seating as a one-dimensional ordering of your guests: Sue > Fred > Tom > Rudy > Bob > ... You are trying to choose a seating arrangement that makes the most sense for your guests, and you have a number of constraints that apply to the problem. Each constraint is represented by a subset of your guests which you want to seat together (consecutively). So, if one constraint is that Sue, Fred, and Bob sit together, then there are certain permutations that are invalid and certain permutations that are valid:**Valid**: Rudy >**Sue > Fred > Bob**> Tom**Valid**: Tom > Rudy >**Bob > Sue > Fred****Invalid**: Tom >**Bob**> Rudy >**Fred > Sue**Furthermore, there may be many of these types of constraints, so perhaps you have (Sue, Fred, Bob) as one constraint and another might be (Sue, Bob, Tom). One possible solution would be given as:

__Tom >__**Sue > Bob****> Fred**> RudyThis is the crux of consecutive ones problem, although the consecutive ones problem is formulated a little differently: Given a binary matrix containing only ones and zeros, reorder the columns such that the ones in every row are consecutive. The image below illustrates the point:

The matrix (a) can be transformed to the matrix (b) be rearranging only the columns in (a). The matrix (b) has the consecutive ones property (COP) in that the 1 values in each row are consecutive when read in order. The consecutive ones problem is: Given a matrix (a), determine if it can be converted into a matrix (b) by rearranging columns such that (b) has the consecutive ones property, and if so represent all valid orderings of the columns.

To see that this is the same as the guest seating problem, assign each guest to a column in matrix A. Each row represents one of the constraints we want to apply to our seating - the columns in that row containing 1's are the guests that we want to seat together.

Not all matrices can be transformed into a COP matrix. Similarly, some matrices can have many possible orderings.

### PQ Tree

A PQ-tree is a data structure described in 1976 by Booth and Leuker[1] It represents all possible solutions to the consecutive ones problem. It allows one to incrementally apply constraints (or rows in the matrix representation) and have a representation of all possible solutions at any point given all constraings applied so far.A PQ-tree has 3 types of nodes:

**Leaf Nodes**represent each element in the permuted set, for example in the matrix each column would be represented by a single leaf node. As with regular trees, leaf nodes have no descendents.**P-nodes**have 2 or more children of any node type.**Q-nodes**have 3 or more children of any node type.

A PQ-Tree represents all valid solutions to a COP problem. An in-order traversal of the leaves of the tree gives you one valid solution, and by reordering the children of P and Q nodes in a simple way, one can produce all possible valid solutions:

- For each P-node, all of it's children can be reordered in any order.
- For each Q-Node, the order of the children can be reversed.

Take, for example, the PQ-tree diagram to the left. One valid ordering from this PQ-tree is 1,2,3,4,5 given by an in-order traversal. If the children of the P-node were reordered as 4,2,3 we see another valid ordering is 1,4,2,3,5. Leaving the reordered children of the P-node, and reversing the order of the children of the Q-node, we construct another valid ordering of 5,3,2,4,1. The ordering of the leaves, read via an in-order traversal (aka: left to right), is called the PQ-tree's

*frontier*.### PQ Tree Construction

In order to construct a PQ-tree for a given COP problem, a complex algorithm must be followed. First, a*universal*PQ-tree must be constructed consisting of a single P-node at it's root with one child leaf node for each element in the COP permuted set.The universal PQ-tree is incrementally modified by each constraint in the COP problem. A constraint is simply a set of leaves that must appear consecutively. The PQ Tree algorithm guarantees that any constraint can be applied in O(n) operations where n is the size of the constraint set. This ultimately means that the

**PQ Tree algorithm is O(n) in the number of elements in the permuted set plus the number of elements in all of the applied reductions**.When applying a constraint, we consider each leaf node in the constraint as

*full*and each leaf node not in the constraint as*empty*. We also consider any internal node as*full*whose children are also all*full*and we consider any internal node as*empty*whose children are also all*empty**.**Full*and*empty**full*and

*empty*children is considered

*partial. Partial*nodes appear in the diagrams below as both

*black*and

*grey*. The smallest subtree containing all of the

*full*nodes in the tree is considered the

*pertinent*

*sub*tree and it's root (note: may not be the root of the whole tree) is considered the

*pertinent root*.

Applying a constraint is known as a

*reduction*. A

*reduction*consists of two phases:

*bubble*and

*reduce*.

### Bubble Phase

The*bubble*phase simply marks all of the nodes in the pertinent subtree as*full*or*partial*and provides a count for each node of the number of pertinent children it has. In order to do this efficiently, it works bottom up, and never looks at any of the nodes outside of the pertinent subtree. This requires that children point to their parent, but the children frequently change parents during a reduction. In order to maintain the linear time bounds, only children of P-nodes and*endmost children*of Q-nodes will have accurate parent pointers at any given time. The*endmost children*of a Q-node are those children which only have one immediate sibling, they are at the ends of the ordering.Children of Q-nodes which are interior will use their endmost-sibling's pointer and will remember this new pointer during the bubble phase. When an interior Q-node is encountered, we check to see if either of it's siblings have valid parent pointers. If not, that node becomes

*blocked*. In a later step, when processing a sibling of this blocked node who has received a valid parent pointer, this blocked node is passed a parent pointer and becomes unblocked. If at the end of the bubble phase, there is still one consecutive set of blocked interior nodes, as would happen in the case of Pattern Q3 below, a "pseudo node" with no parent becomes the parent of that block which gets removed in the reduce phase.### Reduce Phase

The*reduce*phase, which runs after the*bubble*phase uses a queue to process nodes. Each leaf is first inserted into the queue. Then, while the queue is not empty, an element is dequeued and processed and it's parent gets added to the queue if the parent is part of the pertinent subtree. The processing for each node follows one of a set of 10 patterns. Each pattern has a*template*which determines whether or not the pattern matches, and a*replacement*, which describes the processing that must be done to exit the pattern. If none of the patterns match, the entire reduction is impossible for the given tree and fails. This means that there is no COP solution given the constraints applied so far. Each of the patterns is described in more detail below:### Reduction Patterns

#### Leaf Pattern

The leaf pattern is simple - it matches all leaves in the*reduction*and it's replacement simply marks the leaf as*full*.#### P1 Pattern

P1 Template: P-Node with full children |

P1 Replacement |

**Template**: The P1 Template matches if the current node is a P-node whose children are all full nodes.**Replacement**: Mark the current node as full#### P2 Pattern

P2 Template: P-Node with both empty and full children |

P2 Replacement |

**Template**: The P2 Template matches if the current node is a P-node with both full and empty children and if the current node is at the root of the pertinent subtree, that is all full leaves are descendants of the current node.**Replacement**: Move the full children out of the current node*C*and into a new full P-node*P*which becomes one of the current node's children.P3 Pattern

P3 Template: P-node with both empty and full children. |

P3 Replacement |

**Template**: The P3 Template matches if the current node is a P-node with both full and empty children and if the current node is not at the root of the pertinent subtree (there exist full leaves which are not descendants of the current node).**Replacement**: Move the full children out of the current node*C*and into a new full P-node*F*. Move the empty children out of the current node and into a new empty P-node*E*. If either*E*or*F*would have had only one child, do not create a new P-node, simply assign that one child as the*E*or*F*node respectively. Replace*C*with a new Q-node*R*and set*R*'s children as*E*and*F*.P3's replacement is unique in that it's structure cannot ever be found within a PQ Tree between reductions. One of the rules describing a Q-node is that it must have >2 children, otherwise a P-node/Q-node distinction is ambiguous. Hence P3's replacement is considered a "pseudonode" as it will not survive in it's current form in later steps in the current reduction phase. When the replacement's parent is processed (and P3 only matches if it has a pertinent parent), the pseudonode will be transformed into a subtree containing only valid nodes.

P4 Pattern

P4 Template: P-node with a single partial child. |

Replacement P4 |

**Template**: The P4 Template matches if the current node is a P-node with 1 partial child and if the current node is at the root of the pertinent subtree, that is all full leaves are descendants of the current node. The current node may have any number of full and empty children.

**Replacement**: Move the full children out of the current node

*C*and into a new full P-Node

*F*.

*F*then becomes a child of

*C*'s only partial child

*P. F*is added to P's children as a new endmost child, whose sibling is the former endmost child of

*P*which is also full.

P5 Pattern

P5 Template: P-node with a single partial child. |

Replacement P5 |

**Template**: The P5 Template matches if the current node is a P-node with 1 partial child and if the current node is not at the root of the pertinent subtree (there exist full leaves which are not descendants of the current node). The current node may have any number of full and empty children.

**Replacement**: Move the full children out of the current node

*C*and into a new full P-node

*F*. Move the empty children out of the current node and into a new empty P-node

*E*. If either

*E*or

*F*would have had only one child, do not create a new P-node, simply assign that one child as the

*E*or

*F*node respectively. Let

*P*represent the only partial child of

*C*.

*E*becomes the new empty child of

*P*,

*E*is added to P's children as a new endmost child, whose sibling is the former empty endmost child of

*P*.

*F*becomes the new full child of

*P*,

*F*is added to

*P*'s children as a new endmost child, whose sibling is the former full endmost child of

*P*.

P6 Pattern

P6 Template: P-node with two partial children. |

Replacement P6 |

**Template**: The P6 Template matches if the current node is a P-node with exactly 2 partial children and any number of full or empty children.

**Replacement**: Move the full children out of the current node

*C*and into a new full P-node

*F*. If either

*F*would have had only one child, do not create a new P-node, instead assign that one child as the node denoted by

*F.*Let

*P*and

_{1}*P*represent partial children of

_{2}*C*. Add

*F*to

*P*as a sibling of

_{1}*P*'s full endmost child. Add as

_{1}*F*'s other sibling

*P*'s full endmost child. Consider

_{2}*P*'s endmost child pointer that previously pointed to it's full child, re-point that at

_{1}*P*'s empty endmost child. Discard

_{2}*P*as it is no longer needed since

_{2}*P*now consumes all of

_{1 }*P*'s children as well as any full children of

_{2}*C*.

Q1 Pattern

Q1 Template: Q-node with full children |

Q1 Replacement |

###
**Template**: The Q1 Template matches if the current node is a Q-node with exactly only full children.

**Replacement**: Like the P1 pattern, mark the current node as full.

### Q2 Pattern

Q2 Template: Q-node with a single partial child |

Q2 Replacement |

**Template**: The Q2 Template matches if the current node is a Q-node with exactly one partial child and any number of full or empty children. All of the full children must be consecutive and the partial child must be consecutive at the end of the list of consecutive full children.

**Replacement**: Identify the only partial partial child

*P*of the current node

*C.*Let

*P*be

_{F}*P*'s full endmost child and

*P*be

_{E}*P*'s empty endmost child. Furthermore let,

*F*be

*P*'s full sibling and

*E*be

*P*'s empty sibling. One of each of

*F*'s and

*E*'s siblings is P. Replace P with

*P*as

_{F}*F*'s sibling and replace

*P*with

*P*as

_{E}*E*'s sibling. If P did not have an empty or a full sibling,

*P*'s endmost child instead becomes

*C*'s endmost child. Now

*P*'s children are also

*C*'s children, Delete

*P*.

Q3 Pattern

Q3 Template: Q-node with two partial children |

Q3 Replacement |

**Template**: The Q3 Template matches if the current node is a Q-node with exactly two partial children and any number of full or empty children. All of the full children must be consecutive and immediately between the two partial children.

**Replacement**: For

*each of the two partial children*

*P*of the current node

*C*, process them as follows. Let

*P*be

_{F}*P*'s full endmost child and

*P*be

_{E}*P*'s empty endmost child. Furthermore let,

*F*be

*P*'s full or partial sibling and

*E*be

*P*'s empty sibling. One of each of

*F*'s and

*E*'s siblings is P. Replace P with

*P*as

_{F}*F*'s sibling and replace

*P*with

*P*as

_{E}*E*'s sibling. If P did not have an empty or a full sibling,

*P*'s endmost child instead becomes

*C*'s endmost child. Now

*P*'s children are also

*C*'s children, Delete

*P*. Repeat for both partial children

*P*.

## Applications

In addition to solving the consecutive ones problem described above, PQ-trees can be used to solve a class of related problems including interval graphs, or determining if a given graph is planar. They have practical applications in areas such as genetics, road planning, or compression.### References

### My Contributions

I implemented this algorithm in grad school. My implementation then was functional but not high quality, and it was embedded in a larger library. I've spent considerable spare time here and there over the last couple months rewriting a large part of that code.I've created a project up at github which tracks the code: PQ Tree Algorithm Implementation.The code (just a library) should be decent, bug-free, and GPL. The first internet implementation out there.

If you stumble across this page while trying desperately to do some research for your graduate program, I hope this is useful to you. I ask one small thing of you though. I spent a good bit of time putting all of this together. Drop me a comment or email letting me know how you are using it. That way I'll know it was worth the effort.

### Birds of a Feather

If you are one of the handful of people interested in PQ-Trees and advanced software algorithms like this, you are the type of person I'd like to see working with me at Google. If you send me your resume (ggrothau@gmail.com), I can make sure it gets in front of the right recruiters and watch to make sure that it doesn't get lost in the pile that we get every day.

**Update**: Despite the fact that this post was published in 2008, this offer still stands today.
## 7 comments:

Hi Greg,

very nice article. I'm actually studying a problem very close to this topic. I've downloaded the code of PQ-Tree implementation from your Gregable page on github.com

http://github.com/Gregable/pq-trees/tree/master

But there are relatively few examples. I didn't understand the way to insert a problem. For instance, how can I use the program on the matrix in the blog?

Thank you in advance for the answer!

The API needs a bit more documentation and in fact there is no good way to access more than one possible frontier within the current API. I intend to improve upon this in the future.

For now, take a look at the pqtest.cc file, which should give you a good idea of how to do reductions. The interface you want to interact with is in pqtree.h. Sorry that I don't have a better answer yet, but the code especially is still a little bit of a work in progress.

I added some API improvements so that you can recursively explore the tree if desired. Also the github wiki has a tiny bit of documentation in it. The API is very simple - you can create trees, apply reductions, and then explore or print the tree/frontier.

All of these methods are demonstrated in pqtest.cc and documented pretty well in pqtree.h.

Mate i donno who you are and what you do, But this piece of info you've blogged won here has made my day,

I have my project an 8-credit course on this topic. "COP & CROP" and this info youve written down here has helped me so so much,

Im just grateful to you. I would have never got this useful info without google. so thanks to google too...

And as i said i'll be working on it for the nex 2-3 months and i'll hopefully ask you more Q's and doubts.

Cheers, hope you'll be there to clear my doubts.

I gen'ly dont blog so mail me.

There is another data structure PC-tree for consecutive ones problem.

It is simpler than PQ-Tree.

You can google it.

http://www.google.com.tw/search?q=PC-Tree+consecutive+One+problem&sourceid=navclient-ff&ie=UTF-8&rlz=1B3GGGL_enES303ES303

FYI

There's another implementation in C++, together with a report describing it. The report is available here:

http://www.zaik.uni-koeln.de/~paper/preprints.html?show=zpr97-259

I don't know about the source code/license, but I guess it's free for "academic use".

Very interesting. The paper can be downloaded here

http://www.sciencedirect.com/science/article/pii/S0022000076800451

Thanks!

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