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λ > 4: An Improved Lower Bound on the Growth Constant of Polyominoes

The universal constant λ, the growth constant of polyominoes (think Tetris pieces), is rigorously proved to be greater than 4.
  1. Introduction
  2. Key Insights
  3. Computer-Assisted Proofs
  4. Twisted Cylinders
  5. Method
  6. Sequential Runs
  7. Computing λ27
  8. Validity and Certification
  9. Acknowledgments
  10. References
  11. Authors
  12. Figures
  13. Sidebar: Certified Computations
  14. Sidebar: Representing Boundaries as Motzkin Paths
  15. Figure
? > 4: An Improved Lower Bound on the Growth Constant of Polyominoes, illustrative photo

What is λ? The universal constant λ arises in the study of three completely unrelated fields: combinatorics, percolation, and branched polymers. In combinatorics, analysis of self-avoiding walks (SAWs, or non-self-intersecting lattice paths starting at the origin, counted by lattice units), simple polygons or self-avoiding polygons (SAPs, or closed SAWs, counted by either perimeter or area), and “polyominoes” (SAPs possibly with holes, or edge-connected sets of lattice squares, counted by area) are all related. In statistical physics, SAWs and SAPs play a significant role in percolation processes and in the collapse transition that branched polymers undergo when being heated. A collection edited by Guttmann15 gives an excellent review of all these topics and the connections between them. In this article, we describe our effort to prove that the growth constant (or asymptotic growth rate, also called the “connective constant”) of polyominoes is strictly greater than 4. To this aim, we exploited to the maximum possible computer resources available to us. We eventually obtained a computer-generated proof that was verified by other programs implemented independently. We start with a brief introduction of the three research areas.

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Key Insights

  • Direct access to large shared RAM is useful for scientific computations, as well as for big-data applications.
  • The growth constant of polyominoes is provably strictly greater than 4.
  • The “proof” of this bound is an eigenvector of a giant matrix Q that can be verified as corresponding to the claimed bound—an eigenvalue of Q.

Enumerative combinatorics. Imagine a two-dimensional grid of square cells. A polyomino is a connected set of cells, where connectivity is along the sides of cells but not through corners; see Figure 1 for examples of small polyominoes. Polyominoes were popularized in the pioneering book by Golomb13 and by Martin Gardner’s columns in Scientific American; counting polyominoes by size became a popular and fascinating combinatorial problem. The size of a polyomino is the number of its cells.

Figure 2 shows a puzzle game with polyominoes of sizes 5-8 cells. In this article, we consider “fixed” polyominoes; two such polyominoes are considered identical if one can be obtained from the other by a translation, while rotations and flipping are not allowed. The number of polyominoes of size n is usually denoted as A(n). No formula is known for this number, but researchers have suggested efficient backtracking26,27 and transfer-matrix9,18 algorithms for computing A(n) for a given value of n. The latter algorithm was adapted in Barequet et al.5 and also in this work for polyominoes on so-called twisted cylinders.

To date, the sequence A(n) has been determined up to n = 56 by a parallel computation on a Hewlett Packard server cluster using 64 processors.18 The exact value of the growth constant of the sequence λ = limn→∞ A(n + 1)/A(n) continues to be elusive. Incidentally, in the square lattice, the growth constant is very close to the “coordination number,” or the number of neighbors of each cell in the lattice (4 in our case). In this work we reveal (rigorously) the leading decimal digit of λ. It is 4. Moreover, we establish that λ is strictly greater than the coordination number.

Percolation processes. In physics, chemistry, and materials science, percolation theory deals with the movement and filtering of fluids through porous materials. Giving it a mathematical model, the theory describes the behavior of connected clusters in random graphs. Suppose a unit of liquid L is poured on top of some porous material M. What is the chance L makes its way through M and reaches the bottom? An idealized mathematical model of this process is a two- or three-dimensional grid of vertices (“sites”) connected with edges (“bonds”), where each bond is independently open (or closed) for liquid flow with some probability μ. In 1957, Broadbent and Hammersley8 asked, for a fixed value of μ and for the size of the grid tending to infinity, what is the probability that a path consisting of open bonds exists from the top to the bottom. They essentially investigated solute diffusing through solvent, molecules penetrating a porous solid, and similar processes, representing space as a lattice with two distinct types of cells.

In the literature of statistical physics, fixed polyominoes are usually called “strongly embedded lattice animals,” and in that context, the analogue of the growth rate of polyominoes is the growth constant of lattice animals. The terms high and low “temperature” mean high and low “density” of clusters, respectively, and the term “free energy” corresponds to the natural logarithm of the growth constant. Lattice animals were used for computing the mean cluster density in percolation processes, as in Gaunt et al.,12 particularly in processes involving fluid flow in random media. Sykes and Glen29 were the first to observe that A(n), the total number of connected clusters of size n, grows asymptotically like nnθ, where λ is Klarner’s constant and C, θ are two other fixed values.

Collapse of branched polymers. Another important topic in statistical physics is the existence of a collapse transition of branched polymers in dilute solution at a high temperature. In physics, a “field” is an entity each of whose points has a value that depends on location and time. Lubensky and Isaacson22 developed a field theory of branched polymers in the dilute limit, using statistics of (bond) lattice animals (important in the theory of percolation) to imply when a solvent is good or bad for a polymer. Derrida and Herrmann10 investigated two-dimensional branched polymers by looking at lattice animals on a square lattice and studying their free energy. Flesia et al.11 made the connection between collapse processes to percolation theory, relating the growth constant of strongly embedded site animals to the free energy in the processes. Several models of branched polymers in dilute solution were considered by Madras et al.,24 proving bounds on the growth constants for each such model.

Brief History. Determining the exact value of λ (or even setting good bounds on it) is a notably difficult problem in enumerative combinatorics. In 1967, Klarner19 showed that the limit λ = limn→∞ A(n)1/n exists. Since then, λ has been called “Klarner’s constant.” Only in 1999, Madras23 proved the stronger statement that the asymptotic growth rate in the sense of the limit λ = limn→∞ A(n + 1)/A(n) exists.

By using interpolation methods, Sykes and Glen29 estimated in 1976 that λ = 4.06 ± 0.02. This estimate was refined several times based on heuristic extrapolation from the increasing number of known values of A(n). The most accurate estimate—4.0625696 ± 0.0000005—was given by Jensen18 in 2003. Before carrying out this project, the best proven bounds on λ were roughly 3.9801 from below5 and 4.6496 from above.20 λ has thus always been an elusive constant, of which not even a single significant digit was known. Our goal was to raise the lower bound on λ over the barrier of 4, and thus reveal its first decimal digit and prove that λ ≠ 4. The current improvement of the lower bound on λ to 4.0025 also cuts the difference between the known lower bound and the estimated value of λ by approximately 25%—from 0.0825 to 0.0600.

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Computer-Assisted Proofs

Our proof relies heavily on computer calculations, thus raising questions about its credibility and reliability. There are two complementary approaches for addressing this issue: formally verified computations and certified computations.

Formally verified computing. In this paradigm, a program is accompanied by a correctness proof that is refined to such a degree of detail and formality it can be checked mechanically by a computer. This approach and, more generally, formally verified “mathematics,” has become feasible for industry-level software, as well as for mathematics far beyond toy problems due to big advances in recent years; see the review by Avigad and Harrison.2 One highlight is the formally verified proof by Gonthier14 of the Four-Color Theorem, whose original proof by Appel and Haken1 in 1977 already relied on computer assistance and was the prime instance of discussion about the validity and appropriateness of computer methods in mathematical proofs. (Incidentally, one step in Gonthier’s proof also involves polyominoes.) Another example is the verification of Hales’s proof21 of the Kepler conjecture, which states the face-centered cubic packing of spheres is the densest possible. This proof, in addition to involving extensive case enumerations at different levels, is also very complicated in the interaction between the various parts. In August 2014, a team headed by Hales announced the completion of the Flyspeck project, constructing a formal proof of Kepler’s conjecture.16 Yet another example is the proof by Tucker30 for Lorenz’s conjecture (number 14 in Smale’s list of challenging problems for the 21st century). The conjecture states that Lorenz’s system of three differential equations, providing a model for atmospheric convection, supports a strange attractor. Tucker (page 104)30 described the run of a parallel ODE solver several times on different computer setups, obtaining similar results.

Certified Computation. This technique is based on the idea it may be easier to check a given answer for correctness than come up with such an answer from scratch. The prototype example is the solution of an equation like 3x3 – 4x2 – 5x + 2 = 0. While it may be a challenge to find the solution x = 2, it is straightforward to substitute the solution into the equation and check whether it is fulfilled. The result is trustworthy not because it is accompanied by a formal proof but because it is so simple to check, much simpler than the algorithm (perhaps Newton iteration in this case) used to solve the problem in the first place. In addition to the solution itself, it may be required that a certifying algorithm provide a certificate in order to facilitate the checking procedure.25 Developing such certificates and computing them without excessive overhead may require algorithmic innovations (see the first sidebar “Certified Computations“).

In our case, the result computed by our program can be interpreted as the eigenvalue of a giant matrix Q, which is not explicitly stored but implicitly defined through an iteration procedure. The certificate is a vector v that is a good-enough approximation of the corresponding eigenvector. From this vector, one can compute certified bounds on the eigenvalue in a rather straightforward way by comparing the vector v to the product Qv.

These two approaches complement each other; a simpler checking procedure is more amenable to a formal proof and verification procedure. However, we did not go to such lengths; a formal verification of the program would have made sense only in the context of a full verification that includes also the human-readable parts of the proof. We instead used traditional methods of ensuring program correctness of the certificate-checking program.

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Twisted Cylinders

A “twisted cylinder” is a half-infinite wraparound spiral-like square lattice (see Figure 3). We denote the perimeter or “width” of the twisted cylinder by W. Like in the plane, one can count polyominoes on a twisted cylinder of width W and study their growth constant, λW. Barequet et al. proved the sequence (λW) is monotone increasing5 and converges to λ.3 The bigger W is thus the better (higher) the lower bound λW on λ gets.

Analyzing the growth constant of polyominoes is more convenient on a twisted cylinder than in the plane. The reason is we want to build up polyominoes incrementally by considering one square at a time. On a twisted cylinder, this can be done in a uniform way, without having to jump to a new row from time to time. Imagine we walk along the spiral order of squares and at each square decide whether or not to add it to the polyomino. The size of a polyomino is the number of positive decisions we make along the way.

The crucial observation is no matter how big the polyominoes get, they can be characterized in a finite number of ways that depends only on W. All one needs to remember is the structure of the last W squares of the twisted cylinder (the “boundary” of the polyomino) and how they are interconnected through cells that were considered before the boundary. This structure provides enough information for the continuation of the process; whenever a new square is considered and a decision is taken about whether or not to add it to the polyomino, the boundary is updated accordingly. This update is similar to the computation of connected components in a bi-level image by a row-wise scan. In this way, the growth of polyominoes on a twisted cylinder can be modeled by a finite-state automaton whose states are all possible boundaries. Every state of this automaton has two outgoing edges that correspond to whether or not the next square is added to the polyomino. A slight variation of this automaton can be seen in action in a video animation.7 See the online appendix for more on automata for modeling polyominoes on twisted cylinders. See also the second sidebar “Representing Boundaries as Motzkin Paths.”

The number of states of the automaton that models the growth of polyominoes on a twisted cylinder of perimeter W is large4,5—the (W + 1)st Motzkin number MW+1. The nth Motzkin number Mn counts Motzkin paths of length n; see Figure 4 for an illustration of a Motzkin path and the sidebar for the relation between states and Motzkin paths. Such a path connects the integer grid points (0,0) and (n,0) with n steps, consisting only of steps taken from {(1,1), (1,0), (1,-1)} and not going under the x axis. Asymptotically, Mn ~ 3nn–3/2, and MW thus increases roughly by a factor of 3 when W is incremented by 1.

The number of polyominoes with n cells that have state s as the boundary equals the number of paths the automaton can take from the starting state to s, involving n transitions in which a cell is added to the polyomino. We compute these numbers in a dynamic-programming recursion.

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In 2004, a sequential program that computes λW for any perimeter was developed by Ares Ribó as part of her Ph.D. thesis under the supervision of author Günter Rote. The program first computes the endpoints of the outgoing edges from all states of the automaton and saves them in two long arrays succ0 and succ1 that correspond to adding an empty or an occupied cell. Both arrays are of length M: = MW+1. Two successive iteration vectors, which contain the number of polyominoes corresponding to each boundary, are stored as two arrays yold and ynew of numbers, also of length M. The four arrays are indexed from 0 to M – 1. After initializing yold := (1,0,0, …), each iteration computes the new version of y through a very simple loop:


The pointer succ0[s] may be null, in which case the corresponding zero entry (ynew[0]) is used.

As explained earlier, each index s represents a state. The states are encoded by Motzkin paths, and these paths can be mapped to numbers s between 0 and M – 1 in a bijective manner. See the online appendix for more.

In the iteration (*), the vector ynew depends on itself, but this does not cause any problem because succ0[s], if it is non-null, is always less than s. There are thus no circular references, and each entry is set before it is used. In fact, the states can be partitioned into groups G1, G2, …, GW; the group Gi contains the states corresponding to boundaries in which i is the smallest index of an occupied cell, or the boundaries that start with i – 1 empty cells. The dependence between the entries of the groups is shown schematically in Figure 5; succ0[s] of an entry s ∈ Gi (for 1 ≤ i ≤ W – 1), if it is non-null, belongs to Gi+1.

At the end, ynew is moved to yold to start the new iteration. In the iteration (*), the new vector ynew is a linear function of the old vector yold and, as already indicated, can be written as a linear transformation yold := Qyold. The nonnegative integer matrix Q is implicitly given through the iteration (*). We are interested in the growth rate of the sequence of vectors yold that is determined by the dominant eigenvalue λW of Q. It is not difficult to show5 that after every iteration, λW is contained in the interval


By the Perron-Frobenius Theorem about the eigenvalues of nonnegative matrices, the two bounds converge to λW, and yold converges to a corresponding eigenvector.

As written, the program calculates the exact number of polyominoes of each size and state, provided the computations are carried out with precise integer arithmetic. However, these numbers grow like (λw)n, and the program can thus not afford to store them exactly. Instead, we use single-precision floating-point numbers for yold and ynew. Even so, the program must rescale the vector yold from time to time in order to prevent floating-point overflow: Whenever the largest entry exceeds 280 at the end of an iteration, the program divides the whole vector by 2100. This rescaling does not affect the convergence of the process. The program terminates when the two bounds are close enough. The left-hand side of (1) is a lower bound on λW, which in turn is a lower bound on λ, and this is our real goal.

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Sequential Runs

In 2004, we obtained good approximations of yold up to W = 22. The program required quite a bit of main memory (RAM) by the standards of the time. The computation of λ22 ≈ 3.9801 took approximately six hours on a single-processor machine with 32GB of RAM. (Today, the same program runs in 20 minutes on a regular workstation.) By extrapolating the first 22 values of the sequence λW (see Figure 6), we estimated that only when we reach W = 27 would we break the mythical barrier of 4.0. However, as mentioned earlier, the storage requirement is proportional to MW, and MW increases roughly by a factor of 3 when W is incremented by 1. With this exponential growth of both memory consumption and running time, the goal of breaking the barrier was then out of reach.

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Computing λ27

Environment. In the spring of 2013, we were granted access to the Hewlett Packard ProLiant DL980 G7 server of the Hasso Plattner Institute Future SOC Lab in Potsdam, Germany (see Figure 7). This server consists of eight Intel Xeon X7560 nodes (Intel64 architecture), each with eight physical 2.26GHz processors (16 virtual cores), for a total of 64 processors (128 virtual cores). Each node was equipped with 256GiB of RAM (and 24MiB of cache memory) for a total of 2TiB of RAM. Simultaneous access by all processors to the shared main memory was crucial to project success. Distributed memory would incur a severe penalty in running time. The machine ran under the Ubuntu version of the Gnu/Linux operating system. We used the C-compiler gcc with OpenMP 2.0 directives for parallel processing.

Programming improvements. Since for W = 27 the finite automaton has M28 ≈ 2.1 · 1011 states, we initially estimated we would need memory for two 8-byte arrays (for storing succ0 and succ1) and two 4-byte arrays (for storing yold and ynew), all of length M28, for a total of 24 · 2.1 · 1011 ≈ 4.6 TiB of RAM, which exceeded the server’s available capacity. Only a combination of parallelization, storage-compression techniques, and a few other enhancements allowed us to compute λ27 and push the lower bound on λ above 4.0. Here are the main memory-reduction tricks we used (see the online appendix for more):

Elimination of unreachable states. Approximately 11% of all states of the automaton are unreachable and do not affect the result;

Bit-streaming of the succ0/1 arrays. Instead of storing each entry of these arrays in a full word, we allocated just the required number of bits and stored the entries consecutively in a packed manner; in addition, we eliminated all the null succ0-pointers (approximately 11% of all pointers);

Storing higher groups only once. Approximately 50% of the states (those not in G1) were not needed in the recursion (*); we thus did not keep in memory yold[·] of the respective entries;

Recomputing succ0. We computed the entries of the succ0 array on-the-fly instead of keeping them in memory; and

Streamlining the successor computation. Instead of representing a Motzkin path by a sequence of W + 1 integers, we kept a sequence of W + 1 two-bit items we could store in one 8-byte word; this representation allowed us to use word-level operations and look-up tables for a fast mapping from paths to numbers.

To make full use of the parallel capacities, we used the partition of the set of states into groups, as in Figure 5, such that ynew of all elements in a group could be computed independently. We also parallelized the computation of the succ arrays in the preprocessing phase and various housekeeping tasks.

Execution. After 120 iterations, the program announced the lower bound 4.00064 on λ27, thus breaking the 4 barrier. We continued to run the program for a few more days. On May 23, 2013, after 290 iterations, the program reached the stable situation (observed in a few successive tens of iterations) 4.002537727 ≤ λ27 ≤ 4.002542973, establishing the new record λ > 4.00253. The total running time for the computations leading to this result was approximately 36 hours using approximately 1.5TiB of RAM. We had exclusive use of the server for a few dozen hours in total, spread over several weeks.

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Validity and Certification

Our proof depends heavily on computer calculations, raising two issues about its validity:

Calculations. Reproducing elaborate calculations on a large computer is difficult; particularly when a complicated parallel computer program is involved, everybody should be skeptical about the reliability of the results; and

Computations. We performed the computations with 32-bit floating-point numbers.

We address these issues in turn.

What our program was trying to compute is an eigenvalue of a matrix. The amount and length of the computations are irrelevant to the fact that eventually we have stored on disk a witness array of floating-point numbers (the “proof”), approximately 450GB in size, which is a good approximation of the eigenvector corresponding to λ27. This array provides rigorous bounds on the true eigenvalue λ27, because the relation (1) holds for any vector yold and its successor vector ynew. To check the proof and evaluate the bounds (1), a program has to read only the approximate eigenvector yold and carry out one iteration (*). This approach of providing simple certificates for the result of complicated computations is the philosophy of “certifying algorithms.”25

To ensure the correctness of our checking program, we relied on traditional methods (such as testing and code inspection). Some parts of the program (such as reading the data from the files) are irrelevant for the correctness of the result. The main body of the program consists of a few simple loops (such as the iteration (*) and the evaluation of (1)). The only technically challenging part of the algorithm is the successor computation. For this task, we had two programs at our disposal that were written independently by two people who used different state representations—and lived in different countries and did their work several years apart. We ran two different checking programs based on these procedures, giving us additional confidence. We also tested explicitly that the two successor programs yielded the same results. Both checking programs ran in a purely sequential manner, and the running time was approximately 20 hours each.

Regarding the accuracy of the calculations, one can analyze how the recurrence (*) produces ynew from y old. One finds that each term in the lower bound (1) results from the input data (the approximate eigenvector yold) through at most 26 additions of positive numbers for computing ynew[s], plus one division, all in single-precision float. The final minimization was error-free. Since we took care that no denormalized floating-point numbers occurred, the magnitude of the numerical errors was comparable to the accuracy of floating-point numbers, and the accumulated error was thus much smaller than the gap we opened between our new bound and 4. By carefully bounding the floating-point error, we obtained 4.00253176 as a certified lower bound on λ. In particular, we thus now know that the leading digit of λ is 4.

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We acknowledge the support of the facilities and staff of the Hasso Plattner Institute Future SOC Lab in Potsdam, Germany, who let us use their Hewlett Packard ProLiant DL980 G7 server. A technical version of this article6 was presented at the 23rd Annual European Symposium on Algorithms in Patras, Greece, September 2015.

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F1 Figure 1. The single monomino (A(1) = 1), the two dominoes (A(2) = 2), the A(3) = 6 triominoes, and the A(4) = 19 tetrominoes (Tetris pieces).

F2 Figure 2. A solitaire game involving six polyominoes of size 5 (pentominoes), two of size 6 (hexominoes), two of size 7 (heptominoes), and one of size 8 (an octomino). The challenge is to tile the 8×8 square box with the polyominoes.

F3 Figure 3. A twisted cylinder of perimeter W = 5.

F4 Figure 4. A Motzkin path of length 7.

F5 Figure 5. The dependence between the different groups of ynew and yold.

F6 Figure 6. Extrapolating the sequence λW.

F7 Figure 7. Front view of the “supercomputer” we used, a box of approximately 45×35×70 cm.

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UF2-1 Figure. Polyomino boundaries and Motzkin paths.

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